From 1eee77d3517c4b5c427075a0d84eb9b2a9a9a7f1 Mon Sep 17 00:00:00 2001 From: Roger Bivand Date: Thu, 23 Nov 2023 11:39:44 +0100 Subject: [PATCH] update docs --- docs/404.html | 4 +- docs/articles/SpatialFiltering.html | 100 +- docs/articles/index.html | 4 +- docs/articles/nb_igraph.html | 437 +++++--- .../figure-html/unnamed-chunk-13-1.png | Bin 31784 -> 32378 bytes docs/articles/sids_models.html | 120 ++- .../figure-html/unnamed-chunk-13-1.png | Bin 105507 -> 106410 bytes .../figure-html/unnamed-chunk-14-1.png | Bin 26596 -> 27043 bytes docs/authors.html | 58 +- docs/index.html | 14 +- docs/news/index.html | 26 +- docs/pkgdown.yml | 6 +- docs/reference/GMerrorsar-1.png | Bin 232875 -> 233356 bytes docs/reference/GMerrorsar.html | 15 +- docs/reference/MCMCsamp.html | 623 +----------- docs/reference/ME.html | 133 +-- docs/reference/ML_models.html | 941 ++---------------- docs/reference/Rplot001.png | Bin 9934 -> 10143 bytes docs/reference/SET_MCMC.html | 573 +---------- docs/reference/SLX.html | 81 +- docs/reference/SpatialFiltering.html | 17 +- docs/reference/aple.html | 20 +- docs/reference/aple.mc.html | 35 +- docs/reference/aple.plot.html | 62 +- docs/reference/do_ldet.html | 10 +- docs/reference/eigenw.html | 12 +- docs/reference/gstsls-1.png | Bin 190904 -> 190962 bytes docs/reference/gstsls.html | 6 +- docs/reference/impacts.html | 417 ++------ docs/reference/index.html | 4 +- docs/reference/invIrM-1.png | Bin 70679 -> 71917 bytes docs/reference/invIrM.html | 16 +- docs/reference/lagmess.html | 35 +- docs/reference/lextrB.html | 15 +- docs/reference/predict.sarlm.html | 210 ++-- docs/reference/sarlm_tests.html | 4 +- docs/reference/set.mcOption.html | 12 +- docs/reference/set.spChkOption.html | 4 +- docs/reference/similar.listw.html | 4 +- docs/reference/sparse_mat.html | 58 +- docs/reference/spautolm.html | 701 +------------ docs/reference/stsls.html | 48 +- docs/reference/trW-1.png | Bin 31014 -> 31408 bytes docs/reference/trW.html | 46 +- 44 files changed, 1036 insertions(+), 3835 deletions(-) diff --git a/docs/404.html b/docs/404.html index 366c8a6..7e41807 100644 --- a/docs/404.html +++ b/docs/404.html @@ -32,7 +32,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -106,7 +106,7 @@

Page not found (404)

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/docs/articles/SpatialFiltering.html b/docs/articles/SpatialFiltering.html index 2a5d869..d2f66f1 100644 --- a/docs/articles/SpatialFiltering.html +++ b/docs/articles/SpatialFiltering.html @@ -33,7 +33,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -85,7 +85,8 @@

Moran Eigenvectors

-

Roger Bivand

+

Roger +Bivand

Source: vignettes/SpatialFiltering.Rmd @@ -96,13 +97,36 @@

Roger Bivand

-

Introduction1 +

Introduction 1

-

The Moran eigenvector approach (Dray, Legendre, and Peres-Neto 2006; Griffith and Peres-Neto 2006) involved the spatial patterns represented by maps of eigenvectors; by choosing suitable orthogonal patterns and adding them to a linear or generalised linear model, the spatial dependence present in the residuals can be moved into the model.

-

It uses brute force to search the set of eigenvectors of the matrix \(\mathbf{M W M}\), where

-

\[\mathbf{M} = \mathbf{I} - \mathbf{X}(\mathbf{X}^{\rm T} -\mathbf{X})^{-1}\mathbf{X}^{\rm T}\] is a symmetric and idempotent projection matrix and \(\mathbf{W}\) are the spatial weights. In the spatial lag form of SpatialFiltering and in the GLM ME form below, \(\mathbf{X}\) is an \(n\)-vector of ones, that is the intercept only.

-

In its general form, SpatialFiltering chooses the subset of the \(n\) eigenvectors that reduce the residual spatial autocorrelation in the error of the model with covariates. The lag form adds the covariates in assessment of which eigenvectors to choose, but does not use them in constructing the eigenvectors. SpatialFiltering was implemented and contributed by Yongwan Chun and Michael Tiefelsdorf, and is presented in Tiefelsdorf and Griffith (2007); ME is based on Matlab code by Pedro Peres-Neto and is discussed in Dray, Legendre, and Peres-Neto (2006) and Griffith and Peres-Neto (2006).

+

The Moran eigenvector approach (Dray, Legendre, and Peres-Neto +2006; Griffith and Peres-Neto +2006) involved the spatial patterns represented by maps of +eigenvectors; by choosing suitable orthogonal patterns and adding them +to a linear or generalised linear model, the spatial dependence present +in the residuals can be moved into the model.

+

It uses brute force to search the set of eigenvectors of the matrix +\(\mathbf{M W M}\), where

+

\[\mathbf{M} = \mathbf{I} - +\mathbf{X}(\mathbf{X}^{\rm T} +\mathbf{X})^{-1}\mathbf{X}^{\rm T}\] is a symmetric and +idempotent projection matrix and \(\mathbf{W}\) are the spatial weights. In +the spatial lag form of SpatialFiltering and +in the GLM ME form below, \(\mathbf{X}\) is an \(n\)-vector of ones, that is the intercept +only.

+

In its general form, SpatialFiltering +chooses the subset of the \(n\) +eigenvectors that reduce the residual spatial autocorrelation in the +error of the model with covariates. The lag form adds the covariates in +assessment of which eigenvectors to choose, but does not use them in +constructing the eigenvectors. +SpatialFiltering was implemented and +contributed by Yongwan Chun and Michael Tiefelsdorf, and is presented in +Tiefelsdorf and Griffith (2007); +ME is based on Matlab code by Pedro Peres-Neto +and is discussed in Dray, Legendre, and +Peres-Neto (2006) +and Griffith and Peres-Neto (2006).

 library(spdep)
 require("sf", quietly=TRUE)
@@ -144,7 +168,7 @@ 
Introduction## fitted(nySFE)vec14 -1.05105    0.60534  -1.736 0.083662 .  
 ## fitted(nySFE)vec75  1.90600    0.60534   3.149 0.001826 ** 
 ## fitted(nySFE)vec21 -1.06331    0.60534  -1.757 0.080138 .  
-## fitted(nySFE)vec36 -1.17861    0.60534  -1.947 0.052578 .  
+## fitted(nySFE)vec36  1.17861    0.60534   1.947 0.052578 .  
 ## fitted(nySFE)vec61 -1.08582    0.60534  -1.794 0.073986 .  
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
@@ -164,8 +188,23 @@ 
Introduction## 2    267  97.837 10    21.782 5.9444 3.988e-08 ***
 ## ---
 ## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-

Since the SpatialFiltering approach does not allow weights to be used, we see that the residual autocorrelation of the original linear model is absorbed, or ‘whitened’ by the inclusion of selected eigenvectors in the model, but that the covariate coefficients change little. The addition of these eigenvectors – each representing an independent spatial pattern – relieves the residual autocorrelation, but otherwise makes few changes in the substantive coefficient values.

-

The ME function also searches for eigenvectors from the spatial lag variant of the underlying model, but in a GLM framework. The criterion is a permutation bootstrap test on Moran’s \(I\) for regression residuals, and in this case, because of the very limited remaining spatial autocorrelation, is set at \(\alpha = 0.5\). Even with this very generous stopping rule, only few eigenvectors are chosen; their combined contribution only just improves the fit of the GLM model.

+

Since the SpatialFiltering approach does +not allow weights to be used, we see that the residual autocorrelation +of the original linear model is absorbed, or ‘whitened’ by the inclusion +of selected eigenvectors in the model, but that the covariate +coefficients change little. The addition of these eigenvectors – each +representing an independent spatial pattern – relieves the residual +autocorrelation, but otherwise makes few changes in the substantive +coefficient values.

+

The ME function also searches for +eigenvectors from the spatial lag variant of the underlying model, but +in a GLM framework. The criterion is a permutation bootstrap test on +Moran’s \(I\) for regression residuals, +and in this case, because of the very limited remaining spatial +autocorrelation, is set at \(\alpha = +0.5\). Even with this very generous stopping rule, only few +eigenvectors are chosen; their combined contribution only just improves +the fit of the GLM model.

 NYlistwW <- nb2listw(NY_nb, style = "W")
 set.seed(111)
@@ -190,10 +229,6 @@

Introduction## glm(formula = Cases ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME + offset(log(POP8)) + ## fitted(nyME), family = "poisson", data = NY8) ## -## Deviance Residuals: -## Min 1Q Median 3Q Max -## -2.9695 -0.9875 -0.2365 0.6506 3.3866 -## ## Coefficients: ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) -8.13431 0.18388 -44.237 < 2e-16 *** @@ -227,27 +262,48 @@

Introduction## 2 273 340.08 4 13.269 0.01003 * ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 -

Figure \[fig:geigen2\] shows the spatial patterns chosen to match the very small amount of spatial autocorrelation remaining in the model. As with the other Poisson regressions, the closeness to TCE sites is highly significant. Since, however, many TCE sites are also in or close to more densely populated urban areas with the possible presence of both point-source and non-point-source pollution, it would be premature to take such results simply at their face value. There is, however, a potentially useful contrast between the cities of Binghamton in the south of the study area with several sites in its vicinity, and Syracuse in the north without TCE sites in this data set.

+

Figure \[fig:geigen2\] shows the +spatial patterns chosen to match the very small amount of spatial +autocorrelation remaining in the model. As with the other Poisson +regressions, the closeness to TCE sites is highly significant. Since, +however, many TCE sites are also in or close to more densely populated +urban areas with the possible presence of both point-source and +non-point-source pollution, it would be premature to take such results +simply at their face value. There is, however, a potentially useful +contrast between the cities of Binghamton in the south of the study area +with several sites in its vicinity, and Syracuse in the north without +TCE sites in this data set.

References

-Dray, S., P. Legendre, and P. R. Peres-Neto. 2006. “Spatial Modeling: A Comprehensive Framework for Principle Coordinate Analysis of Neighbor Matrices (PCNM).” Ecological Modelling 196: 483–93. +Dray, S., P. Legendre, and P. R. Peres-Neto. 2006. “Spatial +Modeling: A Comprehensive Framework for Principle Coordinate Analysis of +Neighbor Matrices (PCNM).” Ecological +Modelling 196: 483–93.
-Griffith, D. A., and P. R. Peres-Neto. 2006. “Spatial Modeling in Ecology: The Flexibility of Eigenfunction Spatial Analyses.” Ecology 87: 2603–13. +Griffith, D. A., and P. R. Peres-Neto. 2006. “Spatial Modeling in +Ecology: The Flexibility of Eigenfunction Spatial Analyses.” +Ecology 87: 2603–13.
-Tiefelsdorf, M., and D. A. Griffith. 2007. “Semiparametric Filtering of Spatial Autocorrelation: The Eigenvector Approach.” Environment and Planning A 39: 1193–1221. +Tiefelsdorf, M., and D. A. Griffith. 2007. “Semiparametric +Filtering of Spatial Autocorrelation: The Eigenvector Approach.” +Environment and Planning A 39: 1193–1221.
-
+
    -

  1. This vignette formed pp. 302–305 of the first edition of Bivand, R. S., Pebesma, E. and Gómez-Rubio V. (2008) Applied Spatial Data Analysis with R, Springer-Verlag, New York. It was retired from the second edition (2013) to accommodate material on other topics, and is made available in this form with the understanding of the publishers.↩︎

    2. +

  2. This vignette formed pp. 302–305 of the first edition of +Bivand, R. S., Pebesma, E. and Gómez-Rubio V. (2008) Applied Spatial +Data Analysis with R, Springer-Verlag, New York. It was retired from the +second edition (2013) to accommodate material on other topics, and is +made available in this form with the understanding of the publishers.↩︎

@@ -269,7 +325,7 @@

References

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/docs/articles/index.html b/docs/articles/index.html index dccea87..f5b6aed 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -17,7 +17,7 @@ spatialreg - 1.2-7 + 1.3-1
@@ -82,7 +82,7 @@

All vignettes

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/docs/articles/nb_igraph.html b/docs/articles/nb_igraph.html index 625b50e..867c600 100644 --- a/docs/articles/nb_igraph.html +++ b/docs/articles/nb_igraph.html @@ -33,7 +33,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -84,8 +84,10 @@
-

Spatial weights objects as sparse matrices and graphs

-

Roger Bivand

+

Spatial weights objects as sparse matrices and +graphs

+

Roger +Bivand

Source: vignettes/nb_igraph.Rmd @@ -98,27 +100,77 @@

Roger Bivand

Introduction

-

Since the spdep package was created, spatial weights objects have been constructed as lists with three components and a few attributes, in old-style class listw objects. The first component of a listw object is an nb object, a list of n integer vectors, with at least a character vector region.id attribute with n unique values (like the row.names of a data.frame object); n is the number of spatial entities. Component i of this list contains the integer identifiers of the neighbours of i as a sorted vector with no duplication and values in 1:n; if i has no neighbours, the component is a vector of length 1 with value 0L. The nb object may contain an attribute indicating whether it is symmetric or not, that is whether i is a neighbour of j implies that j is a neighbour of i. Some neighbour definitions are symmetric by construction, such as contiguities or distance thresholds, others are asymmetric, such as k-nearest neighbours. The nb object redundantly stores both i-j and j-i links.

-

The second component of a listw object is a list of n numeric vectors, each of the same length as the corresponding non-zero vectors in the nbobject. These give the values of the spatial weights for each i-j neighbour pair. It is often the case that while the neighbours are symmetric by construction, the weights are not, as for example when weights are row-standardised by dividing each row of input weights by the count of neighbours or cardinality of the neighbour set of i. In the nb2listwfunction, it is also possible to pass through general weights, such as inverse distances, shares of boundary lengths and so on.

-

The third component of a listw object records the style of the weights as a character code, with "B" for binary weights taking values zero or one (only one is recorded), "W" for row-standardised weights, and so on. In order to subset listw objects, knowledge of the style may be necessary

-

It is obvious that this is similar to the way in which sparse matrices are stored, either by row - like the listw object, or by column. The key insight is that storing zero values is unnecessary, as we only need to store the row and column locations of non-zero values. Early on, a Netlib library was used to provide limited support in spdep for sparse matrices, followed by functionality in SparseM, spam, and Matrix.

-

From spdep and spatialreg versions 1.2, this vignette and accompanying functionality has been moved to spatialreg.

+

Since the spdep package was created, spatial +weights objects have been constructed as lists with three +components and a few attributes, in old-style class listw +objects. The first component of a listw object is an +nb object, a list of n integer vectors, with +at least a character vector region.id attribute with +n unique values (like the row.names of a +data.frame object); n is the number of spatial +entities. Component i of this list contains the integer +identifiers of the neighbours of i as a sorted vector with +no duplication and values in 1:n; if i has no +neighbours, the component is a vector of length 1 with +value 0L. The nb object may contain an +attribute indicating whether it is symmetric or not, that is whether +i is a neighbour of j implies that +j is a neighbour of i. Some neighbour +definitions are symmetric by construction, such as contiguities or +distance thresholds, others are asymmetric, such as +k-nearest neighbours. The nb object +redundantly stores both i-j and +j-i links.

+

The second component of a listw object is a list of +n numeric vectors, each of the same length as the +corresponding non-zero vectors in the nbobject. These give +the values of the spatial weights for each i-j +neighbour pair. It is often the case that while the neighbours are +symmetric by construction, the weights are not, as for example when +weights are row-standardised by dividing each row of input +weights by the count of neighbours or cardinality of the neighbour set +of i. In the nb2listwfunction, it is also +possible to pass through general weights, such as inverse distances, +shares of boundary lengths and so on.

+

The third component of a listw object records the +style of the weights as a character code, with +"B" for binary weights taking values zero or one (only one +is recorded), "W" for row-standardised weights, and so on. +In order to subset listw objects, knowledge of the +style may be necessary

+

It is obvious that this is similar to the way in which sparse +matrices are stored, either by row - like the listw object, +or by column. The key insight is that storing zero values is +unnecessary, as we only need to store the row and column locations of +non-zero values. Early on, a Netlib library was used to provide limited +support in spdep for sparse matrices, followed by +functionality in SparseM, spam, and +Matrix.

+

From spdep and spatialreg versions +1.2, this vignette and accompanying functionality has been moved to +spatialreg.

spatialreg depends on Matrix

-

Since Matrix is a recommended package, its functionality has increasingly been used over time, and it has become one of two packages on which spatialreg depends. This is reported on loading:

+

Since Matrix is a recommended package, its +functionality has increasingly been used over time, and it has become +one of two packages on which spatialreg depends. This +is reported on loading:

## Loading required package: spData
## Loading required package: Matrix
## Loading required package: sf
-
## Linking to GEOS 3.11.0, GDAL 3.5.2, PROJ 9.0.1; sf_use_s2() is TRUE
+
## Linking to GEOS 3.12.1, GDAL 3.8.0, PROJ 9.3.0; sf_use_s2() is TRUE

Getting some data

-

The legacy Columbus OH data set has 49 spatial entities, polygons, defined as the boundaries of policing districts in the city. spatialreg imports from spdep which in turn depends on sf.

+

The legacy Columbus OH data set has 49 spatial entities, polygons, +defined as the boundaries of policing districts in the city. +spatialreg imports from spdep which in +turn depends on sf.

 dothis <- TRUE
 if (!suppressPackageStartupMessages(require(sf, quietly=TRUE))) {
@@ -129,9 +181,9 @@ 
Getting some datasf_extSoftVersion()
 }
##           GEOS           GDAL         proj.4 GDAL_with_GEOS     USE_PROJ_H 
-##       "3.11.0"        "3.5.2"        "9.0.1"         "true"         "true" 
+##       "3.12.1"        "3.8.0"        "9.3.0"         "true"         "true" 
 ##           PROJ 
-##        "9.0.1"
+## "9.3.0" 
 library(sf)
 columbus <- st_read(system.file("shapes/columbus.shp", package="spData")[1])
@@ -149,7 +201,11 @@

Getting some data -

Contiguous neighbours are often used for polygonal spatial entities, here with the poly2nb function defaulting to the queen criterion - entities are neighbours if they share a boundary point. We see that the entity IDs are copied across to the nb object:

+

Contiguous neighbours are often used for polygonal spatial entities, +here with the poly2nb function defaulting to the +queen criterion - entities are neighbours if they share a +boundary point. We see that the entity IDs are copied across to the +nb object:

 nb_q <- spdep::poly2nb(columbus)
 nb_q
@@ -164,7 +220,10 @@

@@ -385,6 +385,7 @@

Examples

#> rho: 0.4310232 function value: -182.3904 #> rho: 0.4310232 function value: -182.3904 #> rho: 0.4310232 function value: -182.3904 +#> rho: 0.4310232 function value: -182.3904 (x <- summary(COL.lag.eig, correlation=TRUE)) #> #> Call:lagsarlm(formula = CRIME ~ INC + HOVAL, data = COL.OLD, listw = listw, @@ -398,7 +399,7 @@

Examples

#> Type: lag #> Coefficients: (asymptotic standard errors) #> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 45.079251 7.177347 6.2808 3.369e-10 +#> (Intercept) 45.079249 7.177346 6.2808 3.369e-10 #> INC -1.031616 0.305143 -3.3808 0.0007229 #> HOVAL -0.265926 0.088499 -3.0049 0.0026570 #> @@ -411,9 +412,9 @@

Examples

#> ML residual variance (sigma squared): 95.494, (sigma: 9.7721) #> Number of observations: 49 #> Number of parameters estimated: 5 -#> AIC: 374.78, (AIC for lm: 382.75) +#> AIC: NA (not available for weighted model), (AIC for lm: 382.75) #> LM test for residual autocorrelation -#> test value: 0.31955, p-value: 0.57188 +#> test value: 0.31954, p-value: 0.57188 #> #> Correlation of coefficients #> sigma rho (Intercept) INC @@ -424,150 +425,42 @@

Examples

#> coef(x) #> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 45.0792505 7.17734654 6.280768 3.369041e-10 -#> INC -1.0316157 0.30514297 -3.380762 7.228517e-04 +#> (Intercept) 45.0792493 7.17734647 6.280768 3.369043e-10 +#> INC -1.0316157 0.30514297 -3.380762 7.228519e-04 #> HOVAL -0.2659263 0.08849862 -3.004863 2.657002e-03 -# \dontrun{ +if (FALSE) { COL.lag.eig$fdHess -#> [1] FALSE COL.lag.eig$resvar -#> sigma rho (Intercept) INC HOVAL -#> sigma 379.77510361 -0.3236306318 16.3015079 -0.29590801 -0.0202478459 -#> rho -0.32363063 0.0138487533 -0.6975717 0.01266245 0.0008664428 -#> (Intercept) 16.30150788 -0.6975716738 51.5143034 -1.32602704 -0.1616379856 -#> INC -0.29590801 0.0126624511 -1.3260270 0.09311223 -0.0117959715 -#> HOVAL -0.02024785 0.0008664428 -0.1616380 -0.01179597 0.0078320057 # using the apparent sign in Ord (1975, equation B.1) COL.lag.eigb <- lagsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw=listw, method="eigen", control=list(pre_eig=ev, OrdVsign=-1)) summary(COL.lag.eigb) -#> -#> Call:lagsarlm(formula = CRIME ~ INC + HOVAL, data = COL.OLD, listw = listw, -#> method = "eigen", control = list(pre_eig = ev, OrdVsign = -1)) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -37.68585 -5.35636 0.05421 6.02013 23.20555 -#> -#> Type: lag -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 45.079251 9.617835 4.6870 2.772e-06 -#> INC -1.031616 0.326524 -3.1594 0.001581 -#> HOVAL -0.265926 0.088855 -2.9928 0.002764 -#> -#> Rho: 0.43102, LR test value: 9.9736, p-value: 0.001588 -#> Asymptotic standard error: 0.17322 -#> z-value: 2.4884, p-value: 0.012833 -#> Wald statistic: 6.1919, p-value: 0.012833 -#> -#> Log likelihood: -182.3904 for lag model -#> ML residual variance (sigma squared): 95.494, (sigma: 9.7721) -#> Number of observations: 49 -#> Number of parameters estimated: 5 -#> AIC: 374.78, (AIC for lm: 382.75) -#> LM test for residual autocorrelation -#> test value: -0.93825, p-value: 1 -#> COL.lag.eigb$fdHess -#> [1] FALSE COL.lag.eigb$resvar -#> sigma rho (Intercept) INC HOVAL -#> sigma 388.59742931 -0.701154266 35.3176451 -0.64109248 -0.043867490 -#> rho -0.70115427 0.030003688 -1.5113074 0.02743353 0.001877171 -#> (Intercept) 35.31764510 -1.511307358 92.5027556 -2.07005707 -0.212549096 -#> INC -0.64109248 0.027433533 -2.0700571 0.10661800 -0.010871824 -#> HOVAL -0.04386749 0.001877171 -0.2125491 -0.01087182 0.007895242 # force numerical Hessian COL.lag.eig1 <- lagsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw=listw, method="Matrix", control=list(small=25)) summary(COL.lag.eig1) -#> -#> Call:lagsarlm(formula = CRIME ~ INC + HOVAL, data = COL.OLD, listw = listw, -#> method = "Matrix", control = list(small = 25)) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -37.68585 -5.35636 0.05421 6.02013 23.20555 -#> -#> Type: lag -#> Coefficients: (numerical Hessian approximate standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 45.07925 7.87089 5.7273 1.02e-08 -#> INC -1.03162 0.32842 -3.1411 0.001683 -#> HOVAL -0.26593 0.08823 -3.0140 0.002578 -#> -#> Rho: 0.43102, LR test value: 9.9736, p-value: 0.001588 -#> Approximate (numerical Hessian) standard error: 0.12362 -#> z-value: 3.4868, p-value: 0.00048892 -#> Wald statistic: 12.157, p-value: 0.00048892 -#> -#> Log likelihood: -182.3904 for lag model -#> ML residual variance (sigma squared): 95.494, (sigma: 9.7721) -#> Number of observations: 49 -#> Number of parameters estimated: 5 -#> AIC: 374.78, (AIC for lm: 382.75) -#> COL.lag.eig1$fdHess -#> rho (Intercept) INC HOVAL -#> rho 0.0152812237 -0.8345267 0.02005558 0.0002830225 -#> (Intercept) -0.8345266779 61.9509427 -1.78340176 -0.1334585288 -#> INC 0.0200555771 -1.7834018 0.10786140 -0.0122204417 -#> HOVAL 0.0002830225 -0.1334585 -0.01222044 0.0077846055 # force LeSage & Pace (2008, p. 57) approximation COL.lag.eig1a <- lagsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw=listw, method="Matrix", control=list(small=25), trs=trMatc) summary(COL.lag.eig1a) -#> -#> Call:lagsarlm(formula = CRIME ~ INC + HOVAL, data = COL.OLD, listw = listw, -#> method = "Matrix", trs = trMatc, control = list(small = 25)) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -37.68585 -5.35636 0.05421 6.02013 23.20555 -#> -#> Type: lag -#> Coefficients: (numerical Hessian approximate standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 45.079249 7.937572 5.6792 1.353e-08 -#> INC -1.031616 0.329337 -3.1324 0.001734 -#> HOVAL -0.265926 0.088222 -3.0143 0.002576 -#> -#> Rho: 0.43102, LR test value: 9.9736, p-value: 0.001588 -#> Approximate (numerical Hessian) standard error: 0.12503 -#> z-value: 3.4473, p-value: 0.00056624 -#> Wald statistic: 11.884, p-value: 0.00056624 -#> -#> Log likelihood: -182.3904 for lag model -#> ML residual variance (sigma squared): 95.494, (sigma: 9.7721) -#> Number of observations: 49 -#> Number of parameters estimated: 5 -#> AIC: 374.78, (AIC for lm: 382.75) -#> COL.lag.eig1a$fdHess -#> sigma2 rho (Intercept) INC HOVAL -#> sigma2 380.749548407 -0.3653290756 19.9519208 -0.47947507 -0.0067851124 -#> rho -0.365329076 0.0156331058 -0.8537795 0.02051762 0.0002903475 -#> (Intercept) 19.951920812 -0.8537795385 63.0050547 -1.80875071 -0.1338515483 -#> INC -0.479475072 0.0205176238 -1.8087507 0.10846276 -0.0122071279 -#> HOVAL -0.006785112 0.0002903475 -0.1338515 -0.01220713 0.0077831895 COL.lag.eig$resvar[2,2] -#> [1] 0.01384875 # using the apparent sign in Ord (1975, equation B.1) COL.lag.eigb$resvar[2,2] -#> [1] 0.03000369 # force numerical Hessian COL.lag.eig1$fdHess[1,1] -#> [1] 0.01528122 # force LeSage & Pace (2008, p. 57) approximation COL.lag.eig1a$fdHess[2,2] -#> [1] 0.01563311 -# } +} system.time(COL.lag.M <- lagsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, method="Matrix", quiet=FALSE)) #> #> Spatial lag model #> Jacobian calculated using sparse matrix Cholesky decomposition +#> Warning: the default value of argument 'sqrt' of method 'determinant(<CHMfactor>, <logical>)' may change from TRUE to FALSE as soon as the next release of Matrix; set 'sqrt' when programming #> rho: -0.2364499 function value: -192.9523 #> rho: 0.2354499 function value: -183.542 #> rho: 0.5271001 function value: -182.7039 @@ -583,7 +476,7 @@

Examples

#> Computing eigenvalues ... #> #> user system elapsed -#> 0.196 0.000 0.197 +#> 0.145 0.001 0.146 summary(COL.lag.M) #> #> Call:lagsarlm(formula = CRIME ~ INC + HOVAL, data = COL.OLD, listw = listw, @@ -609,7 +502,7 @@

Examples

#> ML residual variance (sigma squared): 95.494, (sigma: 9.7721) #> Number of observations: 49 #> Number of parameters estimated: 5 -#> AIC: 374.78, (AIC for lm: 382.75) +#> AIC: NA (not available for weighted model), (AIC for lm: 382.75) #> LM test for residual autocorrelation #> test value: 0.31954, p-value: 0.57188 #> @@ -618,455 +511,66 @@

Examples

#> Direct Indirect Total #> INC -1.0860220 -0.7270848 -1.8131068 #> HOVAL -0.2799509 -0.1874254 -0.4673763 -# \dontrun{ +if (FALSE) { system.time(COL.lag.sp <- lagsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw=listw, method="spam", quiet=FALSE)) -#> -#> Spatial lag model -#> Jacobian calculated using sparse matrix Cholesky decomposition -#> rho: -0.2364499 function value: -192.9523 -#> rho: 0.2354499 function value: -183.542 -#> rho: 0.5271001 function value: -182.7039 -#> rho: 0.4455543 function value: -182.3974 -#> rho: 0.4267907 function value: -182.391 -#> rho: 0.4311986 function value: -182.3904 -#> rho: 0.4310114 function value: -182.3904 -#> rho: 0.4310231 function value: -182.3904 -#> rho: 0.4310232 function value: -182.3904 -#> rho: 0.4310232 function value: -182.3904 -#> rho: 0.4310232 function value: -182.3904 -#> rho: 0.4310232 function value: -182.3904 -#> Computing eigenvalues ... -#> -#> user system elapsed -#> 0.518 0.001 0.522 summary(COL.lag.sp) -#> -#> Call:lagsarlm(formula = CRIME ~ INC + HOVAL, data = COL.OLD, listw = listw, -#> method = "spam", quiet = FALSE) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -37.68585 -5.35636 0.05421 6.02013 23.20555 -#> -#> Type: lag -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 45.079250 7.177347 6.2808 3.369e-10 -#> INC -1.031616 0.305143 -3.3808 0.0007229 -#> HOVAL -0.265926 0.088499 -3.0049 0.0026570 -#> -#> Rho: 0.43102, LR test value: 9.9736, p-value: 0.001588 -#> Asymptotic standard error: 0.11768 -#> z-value: 3.6626, p-value: 0.00024962 -#> Wald statistic: 13.415, p-value: 0.00024962 -#> -#> Log likelihood: -182.3904 for lag model -#> ML residual variance (sigma squared): 95.494, (sigma: 9.7721) -#> Number of observations: 49 -#> Number of parameters estimated: 5 -#> AIC: 374.78, (AIC for lm: 382.75) -#> LM test for residual autocorrelation -#> test value: 0.31954, p-value: 0.57188 -#> COL.lag.B <- lagsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, spdep::nb2listw(COL.nb, style="B"), control=list(pre_eig=ev)) summary(COL.lag.B) -#> -#> Call: -#> lagsarlm(formula = CRIME ~ INC + HOVAL, data = COL.OLD, listw = spdep::nb2listw(COL.nb, -#> style = "B"), control = list(pre_eig = ev)) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -33.6620 -4.8615 -1.3576 5.1567 25.7563 -#> -#> Type: lag -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 51.604815 6.075285 8.4942 < 2.2e-16 -#> INC -1.154463 0.301808 -3.8252 0.0001307 -#> HOVAL -0.251633 0.087612 -2.8721 0.0040773 -#> -#> Rho: 0.054543, LR test value: 13.453, p-value: 0.00024461 -#> Asymptotic standard error: 0.014836 -#> z-value: 3.6763, p-value: 0.00023662 -#> Wald statistic: 13.515, p-value: 0.00023662 -#> -#> Log likelihood: -180.6507 for lag model -#> ML residual variance (sigma squared): 93.22, (sigma: 9.655) -#> Number of observations: 49 -#> Number of parameters estimated: 5 -#> AIC: 371.3, (AIC for lm: 382.75) -#> LM test for residual autocorrelation -#> test value: 0.006827, p-value: 0.93415 -#> COL.mixed.B <- lagsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, spdep::nb2listw(COL.nb, style="B"), type="mixed", tol.solve=1e-9, control=list(pre_eig=ev)) summary(COL.mixed.B) -#> -#> Call: -#> lagsarlm(formula = CRIME ~ INC + HOVAL, data = COL.OLD, listw = spdep::nb2listw(COL.nb, -#> style = "B"), type = "mixed", tol.solve = 1e-09, control = list(pre_eig = ev)) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -34.8460 -4.2057 -0.1195 4.6525 21.6112 -#> -#> Type: mixed -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 5.4215e+01 5.6639e+00 9.5719 < 2.2e-16 -#> INC -8.2386e-01 3.1643e-01 -2.6036 0.0092262 -#> HOVAL -3.0085e-01 8.5629e-02 -3.5134 0.0004424 -#> lag.(Intercept) -7.5493e+00 1.7307e+00 -4.3620 1.289e-05 -#> lag.INC 2.1544e-05 1.2216e-01 0.0002 0.9998593 -#> lag.HOVAL 7.2458e-02 3.9007e-02 1.8576 0.0632281 -#> -#> Rho: 0.15212, LR test value: 7.435, p-value: 0.0063967 -#> Asymptotic standard error: 0.015565 -#> z-value: 9.7735, p-value: < 2.22e-16 -#> Wald statistic: 95.522, p-value: < 2.22e-16 -#> -#> Log likelihood: -177.7722 for mixed model -#> ML residual variance (sigma squared): 82.502, (sigma: 9.0831) -#> Number of observations: 49 -#> Number of parameters estimated: 8 -#> AIC: 371.54, (AIC for lm: 376.98) -#> LM test for residual autocorrelation -#> test value: -26.79, p-value: 1 -#> COL.mixed.W <- lagsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, type="mixed", control=list(pre_eig=ev)) summary(COL.mixed.W) -#> -#> Call:lagsarlm(formula = CRIME ~ INC + HOVAL, data = COL.OLD, listw = listw, -#> type = "mixed", control = list(pre_eig = ev)) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -37.47829 -6.46731 -0.33835 6.05200 22.62969 -#> -#> Type: mixed -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 42.822414 12.667204 3.3806 0.0007233 -#> INC -0.914223 0.331094 -2.7612 0.0057586 -#> HOVAL -0.293738 0.089212 -3.2926 0.0009927 -#> lag.INC -0.520284 0.565129 -0.9206 0.3572355 -#> lag.HOVAL 0.245640 0.178917 1.3729 0.1697756 -#> -#> Rho: 0.42634, LR test value: 5.3693, p-value: 0.020494 -#> Asymptotic standard error: 0.15623 -#> z-value: 2.7288, p-value: 0.0063561 -#> Wald statistic: 7.4465, p-value: 0.0063561 -#> -#> Log likelihood: -181.3935 for mixed model -#> ML residual variance (sigma squared): 91.791, (sigma: 9.5808) -#> Number of observations: 49 -#> Number of parameters estimated: 7 -#> AIC: 376.79, (AIC for lm: 380.16) -#> LM test for residual autocorrelation -#> test value: 0.28919, p-value: 0.59074 -#> COL.mixed.D00 <- lagsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, Durbin=TRUE, control=list(pre_eig=ev)) summary(COL.mixed.D00) -#> -#> Call:lagsarlm(formula = CRIME ~ INC + HOVAL, data = COL.OLD, listw = listw, -#> Durbin = TRUE, control = list(pre_eig = ev)) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -37.47829 -6.46731 -0.33835 6.05200 22.62969 -#> -#> Type: mixed -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 42.822414 12.667204 3.3806 0.0007233 -#> INC -0.914223 0.331094 -2.7612 0.0057586 -#> HOVAL -0.293738 0.089212 -3.2926 0.0009927 -#> lag.INC -0.520284 0.565129 -0.9206 0.3572355 -#> lag.HOVAL 0.245640 0.178917 1.3729 0.1697756 -#> -#> Rho: 0.42634, LR test value: 5.3693, p-value: 0.020494 -#> Asymptotic standard error: 0.15623 -#> z-value: 2.7288, p-value: 0.0063561 -#> Wald statistic: 7.4465, p-value: 0.0063561 -#> -#> Log likelihood: -181.3935 for mixed model -#> ML residual variance (sigma squared): 91.791, (sigma: 9.5808) -#> Number of observations: 49 -#> Number of parameters estimated: 7 -#> AIC: 376.79, (AIC for lm: 380.16) -#> LM test for residual autocorrelation -#> test value: 0.28919, p-value: 0.59074 -#> COL.mixed.D01 <- lagsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, Durbin=FALSE, control=list(pre_eig=ev)) summary(COL.mixed.D01) -#> -#> Call:lagsarlm(formula = CRIME ~ INC + HOVAL, data = COL.OLD, listw = listw, -#> Durbin = FALSE, control = list(pre_eig = ev)) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -37.68585 -5.35636 0.05421 6.02013 23.20555 -#> -#> Type: lag -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 45.079251 7.177347 6.2808 3.369e-10 -#> INC -1.031616 0.305143 -3.3808 0.0007229 -#> HOVAL -0.265926 0.088499 -3.0049 0.0026570 -#> -#> Rho: 0.43102, LR test value: 9.9736, p-value: 0.001588 -#> Asymptotic standard error: 0.11768 -#> z-value: 3.6626, p-value: 0.00024962 -#> Wald statistic: 13.415, p-value: 0.00024962 -#> -#> Log likelihood: -182.3904 for lag model -#> ML residual variance (sigma squared): 95.494, (sigma: 9.7721) -#> Number of observations: 49 -#> Number of parameters estimated: 5 -#> AIC: 374.78, (AIC for lm: 382.75) -#> LM test for residual autocorrelation -#> test value: 0.31955, p-value: 0.57188 -#> COL.mixed.D1 <- lagsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, Durbin= ~ INC + HOVAL, control=list(pre_eig=ev)) summary(COL.mixed.D1) -#> -#> Call:lagsarlm(formula = CRIME ~ INC + HOVAL, data = COL.OLD, listw = listw, -#> Durbin = ~INC + HOVAL, control = list(pre_eig = ev)) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -37.47829 -6.46731 -0.33835 6.05200 22.62969 -#> -#> Type: mixed -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 42.822414 12.667204 3.3806 0.0007233 -#> INC -0.914223 0.331094 -2.7612 0.0057586 -#> HOVAL -0.293738 0.089212 -3.2926 0.0009927 -#> lag.INC -0.520284 0.565129 -0.9206 0.3572355 -#> lag.HOVAL 0.245640 0.178917 1.3729 0.1697756 -#> -#> Rho: 0.42634, LR test value: 5.3693, p-value: 0.020494 -#> Asymptotic standard error: 0.15623 -#> z-value: 2.7288, p-value: 0.0063561 -#> Wald statistic: 7.4465, p-value: 0.0063561 -#> -#> Log likelihood: -181.3935 for mixed model -#> ML residual variance (sigma squared): 91.791, (sigma: 9.5808) -#> Number of observations: 49 -#> Number of parameters estimated: 7 -#> AIC: 376.79, (AIC for lm: 380.16) -#> LM test for residual autocorrelation -#> test value: 0.28919, p-value: 0.59074 -#> f <- CRIME ~ INC + HOVAL COL.mixed.D2 <- lagsarlm(f, data=COL.OLD, listw, Durbin=as.formula(delete.response(terms(f))), control=list(pre_eig=ev)) summary(COL.mixed.D2) -#> -#> Call: -#> lagsarlm(formula = f, data = COL.OLD, listw = listw, Durbin = as.formula(delete.response(terms(f))), -#> control = list(pre_eig = ev)) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -37.47829 -6.46731 -0.33835 6.05200 22.62969 -#> -#> Type: mixed -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 42.822414 12.667204 3.3806 0.0007233 -#> INC -0.914223 0.331094 -2.7612 0.0057586 -#> HOVAL -0.293738 0.089212 -3.2926 0.0009927 -#> lag.INC -0.520284 0.565129 -0.9206 0.3572355 -#> lag.HOVAL 0.245640 0.178917 1.3729 0.1697756 -#> -#> Rho: 0.42634, LR test value: 5.3693, p-value: 0.020494 -#> Asymptotic standard error: 0.15623 -#> z-value: 2.7288, p-value: 0.0063561 -#> Wald statistic: 7.4465, p-value: 0.0063561 -#> -#> Log likelihood: -181.3935 for mixed model -#> ML residual variance (sigma squared): 91.791, (sigma: 9.5808) -#> Number of observations: 49 -#> Number of parameters estimated: 7 -#> AIC: 376.79, (AIC for lm: 380.16) -#> LM test for residual autocorrelation -#> test value: 0.28919, p-value: 0.59074 -#> COL.mixed.D1a <- lagsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, Durbin= ~ INC, control=list(pre_eig=ev)) summary(COL.mixed.D1a) -#> -#> Call:lagsarlm(formula = CRIME ~ INC + HOVAL, data = COL.OLD, listw = listw, -#> Durbin = ~INC, control = list(pre_eig = ev)) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -37.82800 -5.85207 0.12047 6.00137 23.19963 -#> -#> Type: mixed -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 48.814687 12.198232 4.0018 6.287e-05 -#> INC -1.006620 0.330641 -3.0445 0.002331 -#> HOVAL -0.265514 0.088768 -2.9911 0.002780 -#> lag.INC -0.186684 0.530550 -0.3519 0.724936 -#> -#> Rho: 0.39229, LR test value: 4.5007, p-value: 0.033881 -#> Asymptotic standard error: 0.1561 -#> z-value: 2.513, p-value: 0.011971 -#> Wald statistic: 6.3151, p-value: 0.011971 -#> -#> Log likelihood: -182.3328 for mixed model -#> ML residual variance (sigma squared): 96.122, (sigma: 9.8042) -#> Number of observations: 49 -#> Number of parameters estimated: 6 -#> AIC: 376.67, (AIC for lm: 379.17) -#> LM test for residual autocorrelation -#> test value: 2.6134, p-value: 0.10596 -#> try(COL.mixed.D1 <- lagsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, Durbin= ~ inc + HOVAL, control=list(pre_eig=ev))) -#> Error in eval(predvars, data, env) : object 'inc' not found try(COL.mixed.D1 <- lagsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, Durbin= ~ DISCBD + HOVAL, control=list(pre_eig=ev))) -#> Error in lagsarlm(CRIME ~ INC + HOVAL, data = COL.OLD, listw, Durbin = ~DISCBD + : -#> WX variables not in X: DISCBD NA.COL.OLD <- COL.OLD NA.COL.OLD$CRIME[20:25] <- NA COL.lag.NA <- lagsarlm(CRIME ~ INC + HOVAL, data=NA.COL.OLD, listw, na.action=na.exclude) COL.lag.NA$na.action -#> 1020 1021 1022 1023 1024 1025 -#> 20 21 22 23 24 25 -#> attr(,"class") -#> [1] "exclude" COL.lag.NA -#> -#> Call: -#> lagsarlm(formula = CRIME ~ INC + HOVAL, data = NA.COL.OLD, listw = listw, -#> na.action = na.exclude) -#> Type: lag -#> -#> Coefficients: -#> rho (Intercept) INC HOVAL -#> 0.4537820 43.1054975 -0.9267352 -0.2715541 -#> -#> Log likelihood: -160.8867 resid(COL.lag.NA) -#> 1001 1002 1003 1004 1005 1006 -#> -4.4352945 -13.2750948 -2.9233555 -37.3067542 1.3568011 -3.8828027 -#> 1007 1008 1009 1010 1011 1012 -#> 5.9980436 -12.8113318 -4.0482558 18.1813323 5.9714203 1.0035599 -#> 1013 1014 1015 1016 1017 1018 -#> -1.7490666 -1.7490651 5.8456328 10.1074158 -4.3706895 3.3713771 -#> 1019 1020 1021 1022 1023 1024 -#> -4.0823022 NA NA NA NA NA -#> 1025 1026 1027 1028 1029 1030 -#> NA -6.0553296 -11.5813311 -8.0011389 -1.9047478 3.9744243 -#> 1031 1032 1033 1034 1035 1036 -#> 4.8148614 6.0673483 10.2059847 22.7419913 -2.0830998 0.1422777 -#> 1037 1038 1039 1040 1041 1042 -#> 8.6436388 8.8150864 6.4974685 15.4121789 9.4182148 5.6427603 -#> 1043 1044 1045 1046 1047 1048 -#> -7.5727123 -7.5983733 -9.7792620 -10.0870764 -3.1242393 3.4921796 -#> 1049 -#> 0.7173251 COL.lag.NA1 <- lagsarlm(CRIME ~ INC + HOVAL, data=NA.COL.OLD, listw, Durbin=~INC) # https://github.com/r-spatial/spatialreg/issues/10 COL.lag.NA1$na.action -#> 1020 1021 1022 1023 1024 1025 -#> 20 21 22 23 24 25 -#> attr(,"class") -#> [1] "omit" COL.lag.NA2 <- lagsarlm(CRIME ~ INC + HOVAL, data=NA.COL.OLD, listw, Durbin=~INC, na.action=na.exclude) COL.lag.NA2$na.action -#> 1020 1021 1022 1023 1024 1025 -#> 20 21 22 23 24 25 -#> attr(,"class") -#> [1] "exclude" # https://github.com/r-spatial/spatialreg/issues/11 COL.lag.NA3 <- lagsarlm(CRIME ~ INC + HOVAL, data=NA.COL.OLD, listw, control=list(pre_eig=ev)) -#> Warning: NAs found, precomputed eigenvalues ignored COL.lag.NA3$na.action -#> 1020 1021 1022 1023 1024 1025 -#> 20 21 22 23 24 25 -#> attr(,"class") -#> [1] "omit" -# } +} -# \dontrun{ +if (FALSE) { data(boston, package="spData") gp2mM <- lagsarlm(log(CMEDV) ~ CRIM + ZN + INDUS + CHAS + I(NOX^2) + I(RM^2) + AGE + log(DIS) + log(RAD) + TAX + PTRATIO + B + log(LSTAT), data=boston.c, spdep::nb2listw(boston.soi), type="mixed", method="Matrix") summary(gp2mM) -#> -#> Call:lagsarlm(formula = log(CMEDV) ~ CRIM + ZN + INDUS + CHAS + I(NOX^2) + -#> I(RM^2) + AGE + log(DIS) + log(RAD) + TAX + PTRATIO + B + -#> log(LSTAT), data = boston.c, listw = spdep::nb2listw(boston.soi), -#> type = "mixed", method = "Matrix") -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -0.6316833 -0.0629790 -0.0090776 0.0682421 0.6991072 -#> -#> Type: mixed -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 1.89816218 0.22759605 8.3400 < 2.2e-16 -#> CRIM -0.00571021 0.00093504 -6.1069 1.016e-09 -#> ZN 0.00069091 0.00051869 1.3320 0.182851 -#> INDUS -0.00111343 0.00307354 -0.3623 0.717155 -#> CHAS1 -0.04163225 0.02739364 -1.5198 0.128567 -#> I(NOX^2) -0.01034950 0.19360214 -0.0535 0.957367 -#> I(RM^2) 0.00794979 0.00102063 7.7891 6.661e-15 -#> AGE -0.00128789 0.00048920 -2.6326 0.008473 -#> log(DIS) -0.12404108 0.09510940 -1.3042 0.192168 -#> log(RAD) 0.05863502 0.02257078 2.5978 0.009382 -#> TAX -0.00049084 0.00012145 -4.0416 5.308e-05 -#> PTRATIO -0.01319853 0.00595352 -2.2169 0.026628 -#> B 0.00056383 0.00011089 5.0847 3.682e-07 -#> log(LSTAT) -0.24724454 0.02262033 -10.9302 < 2.2e-16 -#> lag.CRIM -0.00464215 0.00172935 -2.6843 0.007267 -#> lag.ZN -0.00037937 0.00070584 -0.5375 0.590940 -#> lag.INDUS 0.00025064 0.00385901 0.0649 0.948215 -#> lag.CHAS1 0.12518252 0.04071559 3.0746 0.002108 -#> lag.I(NOX^2) -0.38640401 0.22157523 -1.7439 0.081177 -#> lag.I(RM^2) -0.00451252 0.00153180 -2.9459 0.003220 -#> lag.AGE 0.00149678 0.00068418 2.1877 0.028693 -#> lag.log(DIS) -0.00453785 0.10046478 -0.0452 0.963973 -#> lag.log(RAD) -0.00940702 0.03104930 -0.3030 0.761912 -#> lag.TAX 0.00041083 0.00017867 2.2994 0.021481 -#> lag.PTRATIO 0.00060355 0.00788837 0.0765 0.939012 -#> lag.B -0.00050781 0.00014155 -3.5874 0.000334 -#> lag.log(LSTAT) 0.09846781 0.03399423 2.8966 0.003772 -#> -#> Rho: 0.59578, LR test value: 181.68, p-value: < 2.22e-16 -#> Asymptotic standard error: 0.038445 -#> z-value: 15.497, p-value: < 2.22e-16 -#> Wald statistic: 240.16, p-value: < 2.22e-16 -#> -#> Log likelihood: 300.6131 for mixed model -#> ML residual variance (sigma squared): 0.016011, (sigma: 0.12654) -#> Number of observations: 506 -#> Number of parameters estimated: 29 -#> AIC: -543.23, (AIC for lm: -363.55) -#> LM test for residual autocorrelation -#> test value: 29.772, p-value: 4.8604e-08 -#> W <- as(spdep::nb2listw(boston.soi), "CsparseMatrix") trMatb <- trW(W, type="mult") gp2mMi <- lagsarlm(log(CMEDV) ~ CRIM + ZN + INDUS + CHAS + I(NOX^2) + @@ -1074,61 +578,7 @@

Examples

data=boston.c, spdep::nb2listw(boston.soi), type="mixed", method="Matrix", trs=trMatb) summary(gp2mMi) -#> -#> Call:lagsarlm(formula = log(CMEDV) ~ CRIM + ZN + INDUS + CHAS + I(NOX^2) + -#> I(RM^2) + AGE + log(DIS) + log(RAD) + TAX + PTRATIO + B + -#> log(LSTAT), data = boston.c, listw = spdep::nb2listw(boston.soi), -#> type = "mixed", method = "Matrix", trs = trMatb) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -0.6316833 -0.0629790 -0.0090776 0.0682421 0.6991072 -#> -#> Type: mixed -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 1.89816218 0.22759605 8.3400 < 2.2e-16 -#> CRIM -0.00571021 0.00093504 -6.1069 1.016e-09 -#> ZN 0.00069091 0.00051869 1.3320 0.182851 -#> INDUS -0.00111343 0.00307354 -0.3623 0.717155 -#> CHAS1 -0.04163225 0.02739364 -1.5198 0.128567 -#> I(NOX^2) -0.01034950 0.19360214 -0.0535 0.957367 -#> I(RM^2) 0.00794979 0.00102063 7.7891 6.661e-15 -#> AGE -0.00128789 0.00048920 -2.6326 0.008473 -#> log(DIS) -0.12404108 0.09510940 -1.3042 0.192168 -#> log(RAD) 0.05863502 0.02257078 2.5978 0.009382 -#> TAX -0.00049084 0.00012145 -4.0416 5.308e-05 -#> PTRATIO -0.01319853 0.00595352 -2.2169 0.026628 -#> B 0.00056383 0.00011089 5.0847 3.682e-07 -#> log(LSTAT) -0.24724454 0.02262033 -10.9302 < 2.2e-16 -#> lag.CRIM -0.00464215 0.00172935 -2.6843 0.007267 -#> lag.ZN -0.00037937 0.00070584 -0.5375 0.590940 -#> lag.INDUS 0.00025064 0.00385901 0.0649 0.948215 -#> lag.CHAS1 0.12518252 0.04071559 3.0746 0.002108 -#> lag.I(NOX^2) -0.38640401 0.22157523 -1.7439 0.081177 -#> lag.I(RM^2) -0.00451252 0.00153180 -2.9459 0.003220 -#> lag.AGE 0.00149678 0.00068418 2.1877 0.028693 -#> lag.log(DIS) -0.00453785 0.10046478 -0.0452 0.963973 -#> lag.log(RAD) -0.00940702 0.03104930 -0.3030 0.761912 -#> lag.TAX 0.00041083 0.00017867 2.2994 0.021481 -#> lag.PTRATIO 0.00060355 0.00788837 0.0765 0.939012 -#> lag.B -0.00050781 0.00014155 -3.5874 0.000334 -#> lag.log(LSTAT) 0.09846781 0.03399423 2.8966 0.003772 -#> -#> Rho: 0.59578, LR test value: 181.68, p-value: < 2.22e-16 -#> Asymptotic standard error: 0.038445 -#> z-value: 15.497, p-value: < 2.22e-16 -#> Wald statistic: 240.16, p-value: < 2.22e-16 -#> -#> Log likelihood: 300.6131 for mixed model -#> ML residual variance (sigma squared): 0.016011, (sigma: 0.12654) -#> Number of observations: 506 -#> Number of parameters estimated: 29 -#> AIC: -543.23, (AIC for lm: -363.55) -#> LM test for residual autocorrelation -#> test value: 29.772, p-value: 4.8604e-08 -#> -# } +} COL.errW.eig <- errorsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, quiet=FALSE, control=list(pre_eig=ev)) #> @@ -1147,8 +597,8 @@

Examples

#> lambda: 0.5617888 function: -183.3805 Jacobian: -2.134769 SSE: 4683.153 #> lambda: 0.5617902 function: -183.3805 Jacobian: -2.134782 SSE: 4683.151 #> lambda: 0.5617903 function: -183.3805 Jacobian: -2.134782 SSE: 4683.151 -#> lambda: 0.5617903 function: -183.3805 Jacobian: -2.134783 SSE: 4683.151 -#> lambda: 0.5617903 function: -183.3805 Jacobian: -2.134782 SSE: 4683.151 +#> lambda: 0.5617902 function: -183.3805 Jacobian: -2.134782 SSE: 4683.151 +#> lambda: 0.5617902 function: -183.3805 Jacobian: -2.134782 SSE: 4683.151 summary(COL.errW.eig) #> #> Call:errorsarlm(formula = CRIME ~ INC + HOVAL, data = COL.OLD, listw = listw, @@ -1161,7 +611,7 @@

Examples

#> Type: error #> Coefficients: (asymptotic standard errors) #> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 59.893219 5.366163 11.1613 < 2.2e-16 +#> (Intercept) 59.893220 5.366162 11.1613 < 2.2e-16 #> INC -0.941312 0.330569 -2.8476 0.0044057 #> HOVAL -0.302250 0.090476 -3.3407 0.0008358 #> @@ -1228,10 +678,10 @@

Examples

#> lambda: 0.5617866 function: -183.3805 Jacobian: -2.134749 SSE: 4683.157 #> lambda: 0.5617903 function: -183.3805 Jacobian: -2.134783 SSE: 4683.15 #> lambda: 0.5617903 function: -183.3805 Jacobian: -2.134782 SSE: 4683.151 -#> lambda: 0.5617902 function: -183.3805 Jacobian: -2.134782 SSE: 4683.151 #> lambda: 0.5617903 function: -183.3805 Jacobian: -2.134782 SSE: 4683.151 #> lambda: 0.5617903 function: -183.3805 Jacobian: -2.134783 SSE: 4683.151 -#> lambda: 0.5617903 function: -183.3805 Jacobian: -2.134782 SSE: 4683.151 +#> lambda: 0.5617903 function: -183.3805 Jacobian: -2.134783 SSE: 4683.151 +#> lambda: 0.5617903 function: -183.3805 Jacobian: -2.134783 SSE: 4683.151 summary(COL.errW.M) #> #> Call:errorsarlm(formula = CRIME ~ INC + HOVAL, data = COL.OLD, listw = listw, @@ -1290,215 +740,62 @@

Examples

#> Number of parameters estimated: 7 #> AIC: 377.17, (AIC for lm: 380.16) #> -# \dontrun{ +if (FALSE) { COL.SDEM.eig <- errorsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, Durbin=TRUE, control=list(pre_eig=ev)) summary(COL.SDEM.eig) -#> -#> Call:errorsarlm(formula = CRIME ~ INC + HOVAL, data = COL.OLD, listw = listw, -#> Durbin = TRUE, control = list(pre_eig = ev)) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -37.31635 -6.54376 -0.22212 6.44591 23.15801 -#> -#> Type: error -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 73.545133 8.783543 8.3731 < 2.2e-16 -#> INC -1.051673 0.319514 -3.2915 0.0009966 -#> HOVAL -0.275608 0.091151 -3.0236 0.0024976 -#> lag.INC -1.156711 0.578629 -1.9991 0.0456024 -#> lag.HOVAL 0.111691 0.198993 0.5613 0.5746048 -#> -#> Lambda: 0.4254, LR test value: 4.9871, p-value: 0.025537 -#> Asymptotic standard error: 0.15842 -#> z-value: 2.6852, p-value: 0.0072485 -#> Wald statistic: 7.2103, p-value: 0.0072485 -#> -#> Log likelihood: -181.5846 for error model -#> ML residual variance (sigma squared): 92.531, (sigma: 9.6193) -#> Number of observations: 49 -#> Number of parameters estimated: 7 -#> AIC: 377.17, (AIC for lm: 380.16) -#> COL.SDEM.eig <- errorsarlm(CRIME ~ DISCBD + INC + HOVAL, data=COL.OLD, listw, Durbin=~INC, control=list(pre_eig=ev)) summary(COL.SDEM.eig) -#> -#> Call:errorsarlm(formula = CRIME ~ DISCBD + INC + HOVAL, data = COL.OLD, -#> listw = listw, Durbin = ~INC, control = list(pre_eig = ev)) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -34.61867 -7.31993 0.82879 5.92877 17.82211 -#> -#> Type: error -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 68.961912 6.985785 9.8717 < 2.2e-16 -#> DISCBD -5.412936 2.009281 -2.6940 0.007061 -#> INC -0.899425 0.315174 -2.8537 0.004321 -#> HOVAL -0.202846 0.090125 -2.2507 0.024403 -#> lag.INC 0.161858 0.603859 0.2680 0.788669 -#> -#> Lambda: 0.24524, LR test value: 1.2719, p-value: 0.25942 -#> Asymptotic standard error: 0.18393 -#> z-value: 1.3334, p-value: 0.18241 -#> Wald statistic: 1.7778, p-value: 0.18241 -#> -#> Log likelihood: -178.9895 for error model -#> ML residual variance (sigma squared): 85.935, (sigma: 9.2701) -#> Number of observations: 49 -#> Number of parameters estimated: 7 -#> AIC: 371.98, (AIC for lm: 371.25) -#> summary(impacts(COL.SDEM.eig)) -#> Impact measures (SDEM, estimable, n): -#> Direct Indirect Total -#> DISCBD -5.4129364 NA -5.4129364 -#> INC -0.8994250 0.161858 -0.7375670 -#> HOVAL -0.2028457 NA -0.2028457 -#> ======================================================== -#> Standard errors: -#> Direct Indirect Total -#> DISCBD 2.00928149 NA 2.00928149 -#> INC 0.31517363 0.6038588 0.67559404 -#> HOVAL 0.09012493 NA 0.09012493 -#> ======================================================== -#> Z-values: -#> Direct Indirect Total -#> DISCBD -2.693966 NA -2.693966 -#> INC -2.853745 0.2680395 -1.091731 -#> HOVAL -2.250717 NA -2.250717 -#> -#> p-values: -#> Direct Indirect Total -#> DISCBD 0.0070607 NA 0.0070607 -#> INC 0.0043207 0.78867 0.2749513 -#> HOVAL 0.0244035 NA 0.0244035 -#> NA.COL.OLD <- COL.OLD NA.COL.OLD$CRIME[20:25] <- NA COL.err.NA <- errorsarlm(CRIME ~ INC + HOVAL, data=NA.COL.OLD, listw, na.action=na.exclude) COL.err.NA$na.action -#> 1020 1021 1022 1023 1024 1025 -#> 20 21 22 23 24 25 -#> attr(,"class") -#> [1] "exclude" COL.err.NA -#> -#> Call: -#> errorsarlm(formula = CRIME ~ INC + HOVAL, data = NA.COL.OLD, -#> listw = listw, na.action = na.exclude) -#> Type: error -#> -#> Coefficients: -#> lambda (Intercept) INC HOVAL -#> 0.5748430 58.2460528 -0.8473028 -0.3024909 -#> -#> Log likelihood: -161.8763 resid(COL.err.NA) -#> 1001 1002 1003 1004 1005 1006 -#> -4.18270830 -11.44133842 0.31874928 -34.47163074 2.42244758 -4.32095072 -#> 1007 1008 1009 1010 1011 1012 -#> 8.66744165 -13.38669935 -1.92276585 17.85753951 -1.11484596 -2.30434792 -#> 1013 1014 1015 1016 1017 1018 -#> -8.16935116 -5.80500231 0.14973721 5.93191444 -7.03028271 2.39112829 -#> 1019 1020 1021 1022 1023 1024 -#> -8.95099917 NA NA NA NA NA -#> 1025 1026 1027 1028 1029 1030 -#> NA -2.52940711 -9.60025384 -6.95635586 -0.43630579 5.98493664 -#> 1031 1032 1033 1034 1035 1036 -#> 6.25882669 7.75527031 10.83413235 23.23927250 -0.05594958 1.43808573 -#> 1037 1038 1039 1040 1041 1042 -#> 9.51995265 12.18295373 8.31031724 17.06834503 7.04418392 7.50088924 -#> 1043 1044 1045 1046 1047 1048 -#> -7.78485976 -6.79207446 -7.94977534 -11.25362117 -5.68994630 5.04837399 -#> 1049 -#> 2.22497381 print(system.time(ev <- eigenw(similar.listw(listw)))) -#> user system elapsed -#> 0.002 0.000 0.002 print(system.time(COL.errW.eig <- errorsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, method="eigen", control=list(pre_eig=ev)))) -#> user system elapsed -#> 0.198 0.000 0.199 ocoef <- coefficients(COL.errW.eig) print(system.time(COL.errW.eig <- errorsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, method="eigen", control=list(pre_eig=ev, LAPACK=FALSE)))) -#> user system elapsed -#> 0.195 0.000 0.196 print(all.equal(ocoef, coefficients(COL.errW.eig))) -#> [1] TRUE print(system.time(COL.errW.eig <- errorsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, method="eigen", control=list(pre_eig=ev, compiled_sse=TRUE)))) -#> user system elapsed -#> 0.192 0.000 0.193 print(all.equal(ocoef, coefficients(COL.errW.eig))) -#> [1] TRUE print(system.time(COL.errW.eig <- errorsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, method="Matrix_J", control=list(super=TRUE)))) -#> user system elapsed -#> 0.243 0.000 0.245 print(all.equal(ocoef, coefficients(COL.errW.eig))) -#> [1] TRUE print(system.time(COL.errW.eig <- errorsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, method="Matrix_J", control=list(super=FALSE)))) -#> user system elapsed -#> 0.238 0.000 0.239 print(all.equal(ocoef, coefficients(COL.errW.eig))) -#> [1] TRUE print(system.time(COL.errW.eig <- errorsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, method="Matrix_J", control=list(super=as.logical(NA))))) -#> user system elapsed -#> 0.238 0.000 0.239 print(all.equal(ocoef, coefficients(COL.errW.eig))) -#> [1] TRUE print(system.time(COL.errW.eig <- errorsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, method="Matrix", control=list(super=TRUE)))) -#> user system elapsed -#> 0.203 0.000 0.204 print(all.equal(ocoef, coefficients(COL.errW.eig))) -#> [1] TRUE print(system.time(COL.errW.eig <- errorsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, method="Matrix", control=list(super=FALSE)))) -#> user system elapsed -#> 0.202 0.000 0.203 print(all.equal(ocoef, coefficients(COL.errW.eig))) -#> [1] TRUE print(system.time(COL.errW.eig <- errorsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, method="Matrix", control=list(super=as.logical(NA))))) -#> user system elapsed -#> 0.200 0.000 0.202 print(all.equal(ocoef, coefficients(COL.errW.eig))) -#> [1] TRUE print(system.time(COL.errW.eig <- errorsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, method="spam", control=list(spamPivot="MMD")))) -#> user system elapsed -#> 0.201 0.000 0.201 print(all.equal(ocoef, coefficients(COL.errW.eig))) -#> [1] TRUE print(system.time(COL.errW.eig <- errorsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, method="spam", control=list(spamPivot="RCM")))) -#> user system elapsed -#> 0.202 0.000 0.204 print(all.equal(ocoef, coefficients(COL.errW.eig))) -#> [1] TRUE print(system.time(COL.errW.eig <- errorsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, method="spam_update", control=list(spamPivot="MMD")))) -#> user system elapsed -#> 0.201 0.000 0.201 print(all.equal(ocoef, coefficients(COL.errW.eig))) -#> [1] TRUE print(system.time(COL.errW.eig <- errorsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, method="spam_update", control=list(spamPivot="RCM")))) -#> user system elapsed -#> 0.202 0.000 0.202 print(all.equal(ocoef, coefficients(COL.errW.eig))) -#> [1] TRUE -# } +} COL.sacW.eig <- sacsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, control=list(pre_eig1=ev, pre_eig2=ev)) summary(COL.sacW.eig) @@ -1513,7 +810,7 @@

Examples

#> Type: sac #> Coefficients: (asymptotic standard errors) #> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 47.783766 9.902659 4.8253 1.398e-06 +#> (Intercept) 47.783764 9.902659 4.8253 1.398e-06 #> INC -1.025894 0.326326 -3.1438 0.001668 #> HOVAL -0.281651 0.090033 -3.1283 0.001758 #> @@ -1530,14 +827,14 @@

Examples

#> ML residual variance (sigma squared): 95.604, (sigma: 9.7777) #> Number of observations: 49 #> Number of parameters estimated: 6 -#> AIC: 376.47, (AIC for lm: 382.75) +#> AIC: NA (not available for weighted model), (AIC for lm: 382.75) #> set.seed(1) summary(impacts(COL.sacW.eig, tr=trMatc, R=2000), zstats=TRUE, short=TRUE) #> Impact measures (sac, trace): #> Direct Indirect Total -#> INC -1.0632723 -0.5601501 -1.6234223 -#> HOVAL -0.2919129 -0.1537847 -0.4456977 +#> INC -1.0632723 -0.5601502 -1.6234225 +#> HOVAL -0.2919129 -0.1537848 -0.4456977 #> ======================================================== #> Simulation results ( variance matrix): #> ======================================================== @@ -1548,13 +845,13 @@

Examples

#> #> Simulated z-values: #> Direct Indirect Total -#> INC -3.376570 -0.8371024 -1.861677 -#> HOVAL -3.159909 -0.7431009 -1.553156 +#> INC -3.376570 -0.8371025 -1.861677 +#> HOVAL -3.159909 -0.7431010 -1.553156 #> #> Simulated p-values: #> Direct Indirect Total -#> INC 0.00073396 0.40254 0.062649 -#> HOVAL 0.00157818 0.45742 0.120386 +#> INC 0.00073396 0.40253 0.062649 +#> HOVAL 0.00157819 0.45742 0.120386 COL.msacW.eig <- sacsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, type="sacmixed", control=list(pre_eig1=ev, pre_eig2=ev)) summary(COL.msacW.eig) @@ -1569,7 +866,7 @@

Examples

#> Type: sacmixed #> Coefficients: (asymptotic standard errors) #> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 50.92025 68.25721 0.7460 0.455664 +#> (Intercept) 50.92026 68.25721 0.7460 0.455664 #> INC -0.95072 0.44033 -2.1591 0.030841 #> HOVAL -0.28650 0.09994 -2.8667 0.004148 #> lag.INC -0.69261 1.69113 -0.4096 0.682132 @@ -1588,7 +885,7 @@

Examples

#> ML residual variance (sigma squared): 93.149, (sigma: 9.6514) #> Number of observations: 49 #> Number of parameters estimated: 8 -#> AIC: 378.68, (AIC for lm: 382.75) +#> AIC: NA (not available for weighted model), (AIC for lm: 382.75) #> set.seed(1) summary(impacts(COL.msacW.eig, tr=trMatc, R=2000), zstats=TRUE, short=TRUE) @@ -1601,13 +898,13 @@

Examples

#> ======================================================== #> Simulated standard errors #> Direct Indirect Total -#> INC 0.3778064 2.1709292 2.3099451 -#> HOVAL 0.1144714 0.7736217 0.8389227 +#> INC 0.3778064 2.1709289 2.3099448 +#> HOVAL 0.1144714 0.7736216 0.8389226 #> #> Simulated z-values: #> Direct Indirect Total -#> INC -2.737182 -0.7297008 -1.13347001 -#> HOVAL -2.364128 0.2526688 -0.08958504 +#> INC -2.737182 -0.7297008 -1.13347010 +#> HOVAL -2.364129 0.2526689 -0.08958505 #> #> Simulated p-values: #> Direct Indirect Total @@ -1627,7 +924,7 @@

Examples

#> Type: sacmixed #> Coefficients: (asymptotic standard errors) #> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 50.92025 68.25721 0.7460 0.455664 +#> (Intercept) 50.92026 68.25721 0.7460 0.455664 #> INC -0.95072 0.44033 -2.1591 0.030841 #> HOVAL -0.28650 0.09994 -2.8667 0.004148 #> lag.INC -0.69261 1.69113 -0.4096 0.682132 @@ -1646,7 +943,7 @@

Examples

#> ML residual variance (sigma squared): 93.149, (sigma: 9.6514) #> Number of observations: 49 #> Number of parameters estimated: 8 -#> AIC: 378.68, (AIC for lm: 382.75) +#> AIC: NA (not available for weighted model), (AIC for lm: 382.75) #> set.seed(1) summary(impacts(COL.msacW1.eig, tr=trMatc, R=2000), zstats=TRUE, short=TRUE) @@ -1659,13 +956,13 @@

Examples

#> ======================================================== #> Simulated standard errors #> Direct Indirect Total -#> INC 0.3778064 2.1709292 2.3099451 -#> HOVAL 0.1144714 0.7736217 0.8389227 +#> INC 0.3778064 2.1709289 2.3099448 +#> HOVAL 0.1144714 0.7736216 0.8389226 #> #> Simulated z-values: #> Direct Indirect Total -#> INC -2.737182 -0.7297008 -1.13347001 -#> HOVAL -2.364128 0.2526688 -0.08958504 +#> INC -2.737182 -0.7297008 -1.13347010 +#> HOVAL -2.364129 0.2526689 -0.08958505 #> #> Simulated p-values: #> Direct Indirect Total @@ -1686,7 +983,7 @@

Examples

#> Type: sacmixed #> Coefficients: (asymptotic standard errors) #> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 74.064506 37.738937 1.9625 0.049699 +#> (Intercept) 74.064502 37.738940 1.9625 0.049699 #> DISCBD -5.707678 3.248629 -1.7569 0.078926 #> INC -0.900975 0.314996 -2.8603 0.004233 #> HOVAL -0.203399 0.092982 -2.1875 0.028705 @@ -1705,161 +1002,45 @@

Examples

#> ML residual variance (sigma squared): 85.135, (sigma: 9.2269) #> Number of observations: 49 #> Number of parameters estimated: 8 -#> AIC: 373.93, (AIC for lm: 369.42) +#> AIC: NA (not available for weighted model), (AIC for lm: 369.42) #> summary(impacts(COL.msacW2.eig, tr=trMatc, R=2000), zstats=TRUE, short=TRUE) #> Impact measures (sacmixed, trace): #> Direct Indirect Total -#> DISCBD -5.7150801 0.41841708 -5.2966631 -#> INC -0.9031729 0.12421194 -0.7789609 -#> HOVAL -0.2036631 0.01491075 -0.1887524 +#> DISCBD -5.7150799 0.41841671 -5.2966632 +#> INC -0.9031729 0.12421198 -0.7789609 +#> HOVAL -0.2036631 0.01491074 -0.1887524 #> ======================================================== #> Simulation results ( variance matrix): #> ======================================================== #> Simulated standard errors -#> Direct Indirect Total -#> DISCBD 3.1420102 5.8423996 5.8501653 -#> INC 0.3651443 1.8447846 2.0077003 -#> HOVAL 0.1088636 0.6010839 0.6599894 +#> Direct Indirect Total +#> DISCBD 3.1446963 5.7201656 5.757459 +#> INC 0.3621137 1.7150155 1.876353 +#> HOVAL 0.1090172 0.6360488 0.698836 #> #> Simulated z-values: #> Direct Indirect Total -#> DISCBD -1.854977 0.0193511 -0.9769469 -#> INC -2.433297 0.1815217 -0.2757565 -#> HOVAL -2.034012 -0.1886907 -0.5073549 +#> DISCBD -1.852002 0.0280018 -0.9837341 +#> INC -2.461864 0.1861184 -0.3049951 +#> HOVAL -2.021743 -0.1841543 -0.4829973 #> #> Simulated p-values: #> Direct Indirect Total -#> DISCBD 0.063599 0.98456 0.32860 -#> INC 0.014962 0.85596 0.78274 -#> HOVAL 0.041950 0.85034 0.61191 -# \dontrun{ +#> DISCBD 0.064026 0.97766 0.32525 +#> INC 0.013822 0.85235 0.76037 +#> HOVAL 0.043203 0.85389 0.62910 +if (FALSE) { COL.mix.eig <- lagsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, type="mixed", method="eigen") summary(COL.mix.eig, correlation=TRUE, Nagelkerke=TRUE) -#> -#> Call:lagsarlm(formula = CRIME ~ INC + HOVAL, data = COL.OLD, listw = listw, -#> type = "mixed", method = "eigen") -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -37.47829 -6.46731 -0.33835 6.05200 22.62969 -#> -#> Type: mixed -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 42.822415 12.667205 3.3806 0.0007233 -#> INC -0.914223 0.331094 -2.7612 0.0057586 -#> HOVAL -0.293738 0.089212 -3.2926 0.0009927 -#> lag.INC -0.520284 0.565129 -0.9206 0.3572355 -#> lag.HOVAL 0.245640 0.178917 1.3729 0.1697756 -#> -#> Rho: 0.42634, LR test value: 5.3693, p-value: 0.020494 -#> Asymptotic standard error: 0.15623 -#> z-value: 2.7288, p-value: 0.0063561 -#> Wald statistic: 7.4465, p-value: 0.0063561 -#> -#> Log likelihood: -181.3935 for mixed model -#> ML residual variance (sigma squared): 91.791, (sigma: 9.5808) -#> Nagelkerke pseudo-R-squared: 0.6494 -#> Number of observations: 49 -#> Number of parameters estimated: 7 -#> AIC: 376.79, (AIC for lm: 380.16) -#> LM test for residual autocorrelation -#> test value: 0.28919, p-value: 0.59074 -#> -#> Correlation of coefficients -#> sigma rho (Intercept) INC HOVAL lag.INC -#> rho -0.18 -#> (Intercept) 0.16 -0.89 -#> INC -0.03 0.14 -0.19 -#> HOVAL 0.02 -0.09 0.03 -0.45 -#> lag.INC -0.09 0.49 -0.53 -0.36 0.05 -#> lag.HOVAL -0.04 0.19 -0.36 0.19 -0.24 -0.41 -#> COL.mix.M <- lagsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, listw, type="mixed", method="Matrix") summary(COL.mix.M, correlation=TRUE, Nagelkerke=TRUE) -#> -#> Call:lagsarlm(formula = CRIME ~ INC + HOVAL, data = COL.OLD, listw = listw, -#> type = "mixed", method = "Matrix") -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -37.47829 -6.46731 -0.33835 6.05200 22.62969 -#> -#> Type: mixed -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 42.822413 12.667204 3.3806 0.0007233 -#> INC -0.914223 0.331094 -2.7612 0.0057586 -#> HOVAL -0.293738 0.089212 -3.2926 0.0009927 -#> lag.INC -0.520283 0.565129 -0.9206 0.3572355 -#> lag.HOVAL 0.245640 0.178917 1.3729 0.1697756 -#> -#> Rho: 0.42634, LR test value: 5.3693, p-value: 0.020494 -#> Asymptotic standard error: 0.15623 -#> z-value: 2.7288, p-value: 0.0063561 -#> Wald statistic: 7.4465, p-value: 0.0063561 -#> -#> Log likelihood: -181.3935 for mixed model -#> ML residual variance (sigma squared): 91.791, (sigma: 9.5808) -#> Nagelkerke pseudo-R-squared: 0.6494 -#> Number of observations: 49 -#> Number of parameters estimated: 7 -#> AIC: 376.79, (AIC for lm: 380.16) -#> LM test for residual autocorrelation -#> test value: 0.28919, p-value: 0.59074 -#> -#> Correlation of coefficients -#> sigma rho (Intercept) INC HOVAL lag.INC -#> rho -0.18 -#> (Intercept) 0.16 -0.89 -#> INC -0.03 0.14 -0.19 -#> HOVAL 0.02 -0.09 0.03 -0.45 -#> lag.INC -0.09 0.49 -0.53 -0.36 0.05 -#> lag.HOVAL -0.04 0.19 -0.36 0.19 -0.24 -0.41 -#> COL.errW.eig <- errorsarlm(CRIME ~ INC + HOVAL, data=COL.OLD, spdep::nb2listw(COL.nb, style="W"), method="eigen") summary(COL.errW.eig, correlation=TRUE, Nagelkerke=TRUE, Hausman=TRUE) -#> -#> Call: -#> errorsarlm(formula = CRIME ~ INC + HOVAL, data = COL.OLD, listw = spdep::nb2listw(COL.nb, -#> style = "W"), method = "eigen") -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -34.81174 -6.44031 -0.72142 7.61476 23.33626 -#> -#> Type: error -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 59.893219 5.366163 11.1613 < 2.2e-16 -#> INC -0.941312 0.330569 -2.8476 0.0044057 -#> HOVAL -0.302250 0.090476 -3.3407 0.0008358 -#> -#> Lambda: 0.56179, LR test value: 7.9935, p-value: 0.0046945 -#> Asymptotic standard error: 0.13387 -#> z-value: 4.1966, p-value: 2.7098e-05 -#> Wald statistic: 17.611, p-value: 2.7098e-05 -#> -#> Log likelihood: -183.3805 for error model -#> ML residual variance (sigma squared): 95.575, (sigma: 9.7762) -#> Nagelkerke pseudo-R-squared: 0.61978 -#> Number of observations: 49 -#> Number of parameters estimated: 5 -#> AIC: 376.76, (AIC for lm: 382.75) -#> Hausman test: 4.902, df: 3, p-value: 0.17911 -#> -#> Correlation of coefficients -#> sigma lambda (Intercept) INC -#> lambda -0.24 -#> (Intercept) 0.00 0.00 -#> INC 0.00 0.00 -0.56 -#> HOVAL 0.00 0.00 -0.26 -0.45 -#> -# } +}
@@ -1874,7 +1055,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

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z7GtaJSq8#G0b|Ri^g|M!eX-fJNs)LW!jRodTf`f|K9hs5V&;s(ixWEgS8AF^N;$d9tzkhf|D#i8di+um==0!J&oiR8 QUP~h$bhoeBPrmqn0HA3ICIA2c diff --git a/docs/reference/SET_MCMC.html b/docs/reference/SET_MCMC.html index c6e8af8..c63be90 100644 --- a/docs/reference/SET_MCMC.html +++ b/docs/reference/SET_MCMC.html @@ -17,7 +17,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -290,9 +290,6 @@

Examples

#require("spdep", quietly=TRUE)
 data(oldcol, package="spdep")
 lw <- spdep::nb2listw(COL.nb, style="W")
-ev <- eigenw(lw)
-W <- as(lw, "CsparseMatrix")
-trMatc <- trW(W, type="mult")
 require("coda", quietly=TRUE)
 set.seed(1)
 COL.err.Bayes <- spBreg_err(CRIME ~ INC + HOVAL, data=COL.OLD, listw=lw)
@@ -336,621 +333,65 @@ 
Examples

 #>  lambda      3        1010  937          1.080     
 #>  sige        2        930   937          0.993     
 #> 
-# \dontrun{
+if (FALSE) {
+ev <- eigenw(lw)
+W <- as(lw, "CsparseMatrix")
+trMatc <- trW(W, type="mult")
 set.seed(1)
 COL.err.Bayes <- spBreg_err(CRIME ~ INC + HOVAL, data=COL.OLD, listw=lw,
  control=list(prior=list(lambdaMH=TRUE)))
 print(summary(COL.err.Bayes))
-#> 
-#> Iterations = 501:2500
-#> Thinning interval = 1 
-#> Number of chains = 1 
-#> Sample size per chain = 2000 
-#> 
-#> 1. Empirical mean and standard deviation for each variable,
-#>    plus standard error of the mean:
-#> 
-#>                 Mean      SD Naive SE Time-series SE
-#> (Intercept)  59.5304  8.9916 0.201059       0.275885
-#> INC          -0.9142  0.3969 0.008874       0.011266
-#> HOVAL        -0.3027  0.1021 0.002282       0.002282
-#> lambda        0.6011  0.1502 0.003358       0.007723
-#> sige        116.6773 27.9707 0.625443       0.660902
-#> 
-#> 2. Quantiles for each variable:
-#> 
-#>                2.5%    25%      50%      75%    97.5%
-#> (Intercept) 44.0607 54.802  59.6796  64.2473  72.9189
-#> INC         -1.6866 -1.184  -0.9177  -0.6422  -0.1429
-#> HOVAL       -0.5102 -0.370  -0.3005  -0.2359  -0.1019
-#> lambda       0.2826  0.511   0.6135   0.7023   0.8676
-#> sige        74.1788 97.167 113.2955 130.2498 182.6024
-#> 
 print(raftery.diag(COL.err.Bayes, r=0.01))
-#> 
-#> Quantile (q) = 0.025
-#> Accuracy (r) = +/- 0.01
-#> Probability (s) = 0.95 
-#>                                                    
-#>              Burn-in  Total Lower bound  Dependence
-#>              (M)      (N)   (Nmin)       factor (I)
-#>  (Intercept) 3        1143  937          1.220     
-#>  INC         2        969   937          1.030     
-#>  HOVAL       2        892   937          0.952     
-#>  lambda      17       4454  937          4.750     
-#>  sige        2        930   937          0.993     
-#> 
 set.seed(1)
 COL.err.Bayes <- spBreg_err(CRIME ~ INC + HOVAL, data=COL.OLD, listw=lw,
  Durbin=TRUE)
 print(summary(COL.err.Bayes))
-#> 
-#> Iterations = 501:2500
-#> Thinning interval = 1 
-#> Number of chains = 1 
-#> Sample size per chain = 2000 
-#> 
-#> 1. Empirical mean and standard deviation for each variable,
-#>    plus standard error of the mean:
-#> 
-#>                  Mean      SD Naive SE Time-series SE
-#> (Intercept)  72.66455 11.7701 0.263187       0.263187
-#> INC          -1.01543  0.3763 0.008414       0.008414
-#> HOVAL        -0.28060  0.1050 0.002347       0.002347
-#> lag.INC      -1.06754  0.7388 0.016519       0.016519
-#> lag.HOVAL     0.08961  0.2383 0.005328       0.005328
-#> lambda        0.48878  0.1732 0.003873       0.003873
-#> sige        114.66234 26.8527 0.600444       0.659903
-#> 
-#> 2. Quantiles for each variable:
-#> 
-#>                2.5%      25%       50%      75%     97.5%
-#> (Intercept) 48.2182 65.57218  73.00283  80.0588  95.67986
-#> INC         -1.7264 -1.27249  -1.01848  -0.7658  -0.26325
-#> HOVAL       -0.4835 -0.35298  -0.27998  -0.2105  -0.07685
-#> lag.INC     -2.4553 -1.54934  -1.08873  -0.6092   0.39927
-#> lag.HOVAL   -0.3926 -0.06243   0.09177   0.2443   0.55929
-#> lambda       0.1165  0.37669   0.49775   0.6118   0.79690
-#> sige        73.4827 95.70364 110.68368 129.1459 177.97468
-#> 
 print(summary(impacts(COL.err.Bayes)))
-#> Impact measures (SDEM, MCMC, n):
-#>           Direct    Indirect      Total
-#> INC   -1.0154294 -1.06753527 -2.0829647
-#> HOVAL -0.2806034  0.08960515 -0.1909982
-#> ========================================================
-#> Standard errors:
-#>           Direct  Indirect     Total
-#> INC   0.35656492 0.7000652 0.8257127
-#> HOVAL 0.09946104 0.2258118 0.2716818
-#> ========================================================
-#> Z-values:
-#>          Direct   Indirect      Total
-#> INC   -2.847811 -1.5249083 -2.5226265
-#> HOVAL -2.821239  0.3968134 -0.7030219
-#> 
-#> p-values:
-#>       Direct    Indirect Total   
-#> INC   0.0044021 0.12728  0.011648
-#> HOVAL 0.0047839 0.69151  0.482042
-#> 
 print(raftery.diag(COL.err.Bayes, r=0.01))
-#> 
-#> Quantile (q) = 0.025
-#> Accuracy (r) = +/- 0.01
-#> Probability (s) = 0.95 
-#>                                                    
-#>              Burn-in  Total Lower bound  Dependence
-#>              (M)      (N)   (Nmin)       factor (I)
-#>  (Intercept) 2        930   937          0.993     
-#>  INC         3        1010  937          1.080     
-#>  HOVAL       2        930   937          0.993     
-#>  lag.INC     2        892   937          0.952     
-#>  lag.HOVAL   2        892   937          0.952     
-#>  lambda      2        930   937          0.993     
-#>  sige        2        930   937          0.993     
-#> 
 set.seed(1)
 COL.err.Bayes <- spBreg_err(CRIME ~ INC + HOVAL, data=COL.OLD, listw=lw,
  Durbin=TRUE, control=list(prior=list(lambdaMH=TRUE)))
 print(summary(COL.err.Bayes))
-#> 
-#> Iterations = 501:2500
-#> Thinning interval = 1 
-#> Number of chains = 1 
-#> Sample size per chain = 2000 
-#> 
-#> 1. Empirical mean and standard deviation for each variable,
-#>    plus standard error of the mean:
-#> 
-#>                  Mean      SD Naive SE Time-series SE
-#> (Intercept)  70.40019 70.4345 1.574962       1.385430
-#> INC          -0.97037  0.4110 0.009190       0.009823
-#> HOVAL        -0.28455  0.1145 0.002561       0.002561
-#> lag.INC      -0.95215  0.8426 0.018842       0.027001
-#> lag.HOVAL     0.06555  0.2679 0.005990       0.007555
-#> lambda        0.60702  0.1781 0.003983       0.014276
-#> sige        120.45188 29.1908 0.652727       1.279486
-#> 
-#> 2. Quantiles for each variable:
-#> 
-#>                2.5%     25%       50%      75%     97.5%
-#> (Intercept) 37.9440 62.5478  71.80756  80.9018 104.95272
-#> INC         -1.7687 -1.2346  -0.98524  -0.7063  -0.14242
-#> HOVAL       -0.5141 -0.3592  -0.28460  -0.2088  -0.06554
-#> lag.INC     -2.6086 -1.5191  -0.95619  -0.4294   0.72268
-#> lag.HOVAL   -0.4869 -0.1027   0.06799   0.2443   0.56913
-#> lambda       0.2366  0.5043   0.62080   0.7333   0.92570
-#> sige        76.7686 99.6498 116.80421 135.9061 189.12857
-#> 
 print(summary(impacts(COL.err.Bayes)))
-#> Impact measures (SDEM, MCMC, n):
-#>           Direct    Indirect      Total
-#> INC   -0.9703690 -0.95214894 -1.9225179
-#> HOVAL -0.2845537  0.06555386 -0.2189999
-#> ========================================================
-#> Standard errors:
-#>          Direct  Indirect     Total
-#> INC   0.3894378 0.7984944 0.9933065
-#> HOVAL 0.1085247 0.2538476 0.3175629
-#> ========================================================
-#> Z-values:
-#>          Direct  Indirect      Total
-#> INC   -2.491717 -1.192430 -1.9354730
-#> HOVAL -2.622018  0.258241 -0.6896269
-#> 
-#> p-values:
-#>       Direct    Indirect Total   
-#> INC   0.0127127 0.23309  0.052932
-#> HOVAL 0.0087411 0.79622  0.490429
-#> 
 print(raftery.diag(COL.err.Bayes, r=0.01))
-#> 
-#> Quantile (q) = 0.025
-#> Accuracy (r) = +/- 0.01
-#> Probability (s) = 0.95 
-#>                                                    
-#>              Burn-in  Total Lower bound  Dependence
-#>              (M)      (N)   (Nmin)       factor (I)
-#>  (Intercept) 5        1472  937          1.570     
-#>  INC         2        892   937          0.952     
-#>  HOVAL       3        1010  937          1.080     
-#>  lag.INC     3        1010  937          1.080     
-#>  lag.HOVAL   2        930   937          0.993     
-#>  lambda      14       3645  937          3.890     
-#>  sige        2        930   937          0.993     
-#> 
 set.seed(1)
 COL.err.Bayes <- spBreg_err(CRIME ~ INC + HOVAL, data=COL.OLD, listw=lw,
  Durbin=~INC)
 print(summary(COL.err.Bayes))
-#> 
-#> Iterations = 501:2500
-#> Thinning interval = 1 
-#> Number of chains = 1 
-#> Sample size per chain = 2000 
-#> 
-#> 1. Empirical mean and standard deviation for each variable,
-#>    plus standard error of the mean:
-#> 
-#>                 Mean       SD Naive SE Time-series SE
-#> (Intercept)  74.5423 10.98931 0.245728       0.245728
-#> INC          -1.0339  0.38293 0.008563       0.008987
-#> HOVAL        -0.2860  0.09857 0.002204       0.002204
-#> lag.INC      -0.9190  0.59170 0.013231       0.012507
-#> lambda        0.4879  0.17196 0.003845       0.003672
-#> sige        112.0658 25.47022 0.569531       0.567228
-#> 
-#> 2. Quantiles for each variable:
-#> 
-#>                2.5%     25%      50%      75%     97.5%
-#> (Intercept) 52.0905 67.8363  74.9216  81.7016  94.97558
-#> INC         -1.7796 -1.2965  -1.0425  -0.7816  -0.23925
-#> HOVAL       -0.4762 -0.3494  -0.2861  -0.2205  -0.08885
-#> lag.INC     -2.0736 -1.3191  -0.9217  -0.5457   0.27871
-#> lambda       0.1355  0.3737   0.4987   0.6088   0.80100
-#> sige        73.0813 94.1656 108.2980 125.8857 173.13082
-#> 
 print(summary(impacts(COL.err.Bayes)))
-#> Impact measures (SDEM, MCMC, n):
-#>           Direct   Indirect      Total
-#> INC   -1.0339443 -0.9190497 -1.9529940
-#> HOVAL -0.2860029         NA -0.2860029
-#> ========================================================
-#> Standard errors:
-#>           Direct  Indirect      Total
-#> INC   0.36696535 0.5670367 0.69945670
-#> HOVAL 0.09446444        NA 0.09446444
-#> ========================================================
-#> Z-values:
-#>          Direct  Indirect     Total
-#> INC   -2.817553 -1.620794 -2.792159
-#> HOVAL -3.027625        NA -3.027625
-#> 
-#> p-values:
-#>       Direct    Indirect Total    
-#> INC   0.0048391 0.10506  0.0052358
-#> HOVAL 0.0024648 NA       0.0024648
-#> 
 print(raftery.diag(COL.err.Bayes, r=0.01))
-#> 
-#> Quantile (q) = 0.025
-#> Accuracy (r) = +/- 0.01
-#> Probability (s) = 0.95 
-#>                                                    
-#>              Burn-in  Total Lower bound  Dependence
-#>              (M)      (N)   (Nmin)       factor (I)
-#>  (Intercept) 3        1010  937          1.080     
-#>  INC         2        892   937          0.952     
-#>  HOVAL       2        930   937          0.993     
-#>  lag.INC     2        892   937          0.952     
-#>  lambda      2        892   937          0.952     
-#>  sige        3        1010  937          1.080     
-#> 
 set.seed(1)
 COL.err.Bayes <- spBreg_err(CRIME ~ INC + HOVAL, data=COL.OLD, listw=lw,
  Durbin=~INC, control=list(prior=list(lambdaMH=TRUE)))
 print(summary(COL.err.Bayes))
-#> 
-#> Iterations = 501:2500
-#> Thinning interval = 1 
-#> Number of chains = 1 
-#> Sample size per chain = 2000 
-#> 
-#> 1. Empirical mean and standard deviation for each variable,
-#>    plus standard error of the mean:
-#> 
-#>                 Mean      SD Naive SE Time-series SE
-#> (Intercept)  72.8864 13.2070 0.295318       0.332323
-#> INC          -0.9843  0.3831 0.008566       0.009768
-#> HOVAL        -0.2889  0.1008 0.002253       0.002253
-#> lag.INC      -0.8606  0.6640 0.014847       0.014847
-#> lambda        0.5779  0.1662 0.003715       0.010526
-#> sige        114.9801 26.2240 0.586387       0.665850
-#> 
-#> 2. Quantiles for each variable:
-#> 
-#>                2.5%     25%      50%      75%    97.5%
-#> (Intercept) 45.5280 65.3717  73.3929  81.2895  97.2255
-#> INC         -1.7170 -1.2373  -0.9898  -0.7261  -0.2197
-#> HOVAL       -0.4870 -0.3565  -0.2850  -0.2177  -0.1032
-#> lag.INC     -2.1579 -1.2914  -0.8847  -0.4386   0.4898
-#> lambda       0.2436  0.4725   0.5835   0.6981   0.8672
-#> sige        73.5737 96.1297 111.5039 130.5606 176.0332
-#> 
 print(summary(impacts(COL.err.Bayes)))
-#> Impact measures (SDEM, MCMC, n):
-#>           Direct   Indirect      Total
-#> INC   -0.9843411 -0.8605551 -1.8448962
-#> HOVAL -0.2889068         NA -0.2889068
-#> ========================================================
-#> Standard errors:
-#>           Direct  Indirect      Total
-#> INC   0.36712699 0.6362974 0.80457985
-#> HOVAL 0.09657666        NA 0.09657666
-#> ========================================================
-#> Z-values:
-#>          Direct  Indirect     Total
-#> INC   -2.681201 -1.352442 -2.292993
-#> HOVAL -2.991476        NA -2.991476
-#> 
-#> p-values:
-#>       Direct    Indirect Total    
-#> INC   0.0073359 0.17623  0.0218484
-#> HOVAL 0.0027763 NA       0.0027763
-#> 
 print(raftery.diag(COL.err.Bayes, r=0.01))
-#> 
-#> Quantile (q) = 0.025
-#> Accuracy (r) = +/- 0.01
-#> Probability (s) = 0.95 
-#>                                                    
-#>              Burn-in  Total Lower bound  Dependence
-#>              (M)      (N)   (Nmin)       factor (I)
-#>  (Intercept) 4        1192  937          1.270     
-#>  INC         2        892   937          0.952     
-#>  HOVAL       2        930   937          0.993     
-#>  lag.INC     2        892   937          0.952     
-#>  lambda      10       2853  937          3.040     
-#>  sige        2        930   937          0.993     
-#> 
 set.seed(1)
 COL.sacW.B0 <- spBreg_sac(CRIME ~ INC + HOVAL, data=COL.OLD, listw=lw,
  Durbin=FALSE, control=list(ndraw=1500L, nomit=500L))
 print(summary(COL.sacW.B0))
-#> 
-#> Iterations = 501:1500
-#> Thinning interval = 1 
-#> Number of chains = 1 
-#> Sample size per chain = 1000 
-#> 
-#> 1. Empirical mean and standard deviation for each variable,
-#>    plus standard error of the mean:
-#> 
-#>                 Mean       SD Naive SE Time-series SE
-#> (Intercept)  49.3672  9.23725 0.292108       0.847100
-#> INC          -0.9925  0.35777 0.011314       0.012624
-#> HOVAL        -0.2847  0.09798 0.003099       0.003307
-#> rho           0.3117  0.19633 0.006208       0.020392
-#> lambda        0.1927  0.27130 0.008579       0.027162
-#> sige        105.0462 23.96805 0.757936       0.757936
-#> 
-#> 2. Quantiles for each variable:
-#> 
-#>                2.5%      25%      50%      75%    97.5%
-#> (Intercept) 31.8783 43.12583  48.9398  55.3573  68.1858
-#> INC         -1.6817 -1.24459  -0.9872  -0.7516  -0.3168
-#> HOVAL       -0.4952 -0.34433  -0.2827  -0.2201  -0.1028
-#> rho         -0.1548  0.21285   0.3406   0.4438   0.6176
-#> lambda      -0.3788  0.01455   0.2171   0.3794   0.6988
-#> sige        68.5025 88.13909 101.6888 118.2545 160.2792
-#> 
 print(summary(impacts(COL.sacW.B0, tr=trMatc), zstats=TRUE, short=TRUE))
-#> Impact measures (sac, trace):
-#>          Direct   Indirect      Total
-#> INC   -1.017325 -0.4246675 -1.4419930
-#> HOVAL -0.291816 -0.1218143 -0.4136303
-#> ========================================================
-#> Simulation results ( variance matrix):
-#> ========================================================
-#> Simulated standard errors
-#>          Direct  Indirect     Total
-#> INC   0.3668822 0.4180525 0.6533395
-#> HOVAL 0.1016518 0.1341689 0.1983041
-#> 
-#> Simulated z-values:
-#>          Direct  Indirect     Total
-#> INC   -2.808257 -1.219897 -2.357550
-#> HOVAL -2.910868 -1.106171 -2.240542
-#> 
-#> Simulated p-values:
-#>       Direct    Indirect Total   
-#> INC   0.0049810 0.22250  0.018396
-#> HOVAL 0.0036043 0.26865  0.025056
 set.seed(1)
 COL.sacW.B1 <- spBreg_sac(CRIME ~ INC + HOVAL, data=COL.OLD, listw=lw,
  Durbin=TRUE, control=list(ndraw=1500L, nomit=500L))
 print(summary(COL.sacW.B1))
-#> 
-#> Iterations = 501:1500
-#> Thinning interval = 1 
-#> Number of chains = 1 
-#> Sample size per chain = 1000 
-#> 
-#> 1. Empirical mean and standard deviation for each variable,
-#>    plus standard error of the mean:
-#> 
-#>                 Mean       SD Naive SE Time-series SE
-#> (Intercept)  60.1833 24.14827 0.763635       3.169760
-#> INC          -0.9880  0.36251 0.011464       0.015819
-#> HOVAL        -0.2839  0.09749 0.003083       0.003433
-#> lag.INC      -0.8740  0.77272 0.024435       0.061565
-#> lag.HOVAL     0.1704  0.22184 0.007015       0.013821
-#> rho           0.1808  0.32007 0.010121       0.044327
-#> lambda        0.2106  0.32071 0.010142       0.041657
-#> sige        100.9463 24.32264 0.769149       0.946662
-#> 
-#> 2. Quantiles for each variable:
-#> 
-#>                2.5%       25%     50%      75%    97.5%
-#> (Intercept) 22.2042 42.277313 56.1007  76.4887 113.5316
-#> INC         -1.7045 -1.223372 -0.9842  -0.7329  -0.3299
-#> HOVAL       -0.4803 -0.349479 -0.2841  -0.2152  -0.1085
-#> lag.INC     -2.5306 -1.336778 -0.8386  -0.3451   0.5156
-#> lag.HOVAL   -0.3031  0.026900  0.1851   0.3249   0.5752
-#> rho         -0.5076 -0.021227  0.2386   0.4320   0.6655
-#> lambda      -0.5106 -0.007594  0.2129   0.4482   0.7389
-#> sige        64.6867 84.395156 96.9599 114.4101 159.7252
-#> 
 print(summary(impacts(COL.sacW.B1, tr=trMatc), zstats=TRUE, short=TRUE))
-#> Impact measures (sacmixed, trace):
-#>           Direct  Indirect      Total
-#> INC   -1.0332779 -1.239705 -2.2729830
-#> HOVAL -0.2787547  0.140259 -0.1384957
-#> ========================================================
-#> Simulation results ( variance matrix):
-#> ========================================================
-#> Simulated standard errors
-#>          Direct  Indirect     Total
-#> INC   0.3510687 0.9383466 1.0337787
-#> HOVAL 0.1002385 0.2776460 0.3184971
-#> 
-#> Simulated z-values:
-#>          Direct   Indirect      Total
-#> INC   -2.962199 -1.4351185 -2.3085930
-#> HOVAL -2.775887  0.5415921 -0.4015108
-#> 
-#> Simulated p-values:
-#>       Direct    Indirect Total   
-#> INC   0.0030545 0.15125  0.020966
-#> HOVAL 0.0055051 0.58810  0.688044
 set.seed(1)
 COL.lag.Bayes <- spBreg_lag(CRIME ~ INC + HOVAL, data=COL.OLD,
  listw=lw)
 print(summary(COL.lag.Bayes))
-#> 
-#> Iterations = 501:2500
-#> Thinning interval = 1 
-#> Number of chains = 1 
-#> Sample size per chain = 2000 
-#> 
-#> 1. Empirical mean and standard deviation for each variable,
-#>    plus standard error of the mean:
-#> 
-#>                 Mean       SD Naive SE Time-series SE
-#> (Intercept)  45.8256  8.04911 0.179984       0.179984
-#> INC          -1.0459  0.34746 0.007770       0.007770
-#> HOVAL        -0.2662  0.09366 0.002094       0.002094
-#> rho           0.4146  0.12434 0.002780       0.002780
-#> sige        107.8827 23.75095 0.531087       0.561152
-#> 
-#> 2. Quantiles for each variable:
-#> 
-#>                2.5%     25%      50%      75%     97.5%
-#> (Intercept) 30.3334 40.3140  45.9376  51.0903  61.51008
-#> INC         -1.7252 -1.2774  -1.0477  -0.8158  -0.35423
-#> HOVAL       -0.4533 -0.3312  -0.2686  -0.2019  -0.08107
-#> rho          0.1645  0.3327   0.4157   0.5008   0.64982
-#> sige        70.5778 90.6316 104.8556 120.4706 164.75421
-#> 
 print(summary(impacts(COL.lag.Bayes, tr=trMatc), short=TRUE, zstats=TRUE))
-#> Impact measures (lag, trace):
-#>           Direct   Indirect      Total
-#> INC   -1.0962113 -0.6903627 -1.7865741
-#> HOVAL -0.2790451 -0.1757347 -0.4547797
-#> ========================================================
-#> Simulation results ( variance matrix):
-#> ========================================================
-#> Simulated standard errors
-#>          Direct  Indirect     Total
-#> INC   0.3680189 0.4927553 0.7674941
-#> HOVAL 0.1002735 0.1393959 0.2171077
-#> 
-#> Simulated z-values:
-#>          Direct  Indirect     Total
-#> INC   -3.003178 -1.554252 -2.437925
-#> HOVAL -2.809370 -1.425055 -2.212507
-#> 
-#> Simulated p-values:
-#>       Direct    Indirect Total   
-#> INC   0.0026718 0.12012  0.014772
-#> HOVAL 0.0049639 0.15414  0.026932
 print(summary(impacts(COL.lag.Bayes, evalues=ev), short=TRUE, zstats=TRUE))
-#> Impact measures (lag, evalues):
-#>           Direct   Indirect      Total
-#> INC   -1.0962113 -0.6903627 -1.7865741
-#> HOVAL -0.2790451 -0.1757347 -0.4547797
-#> ========================================================
-#> Simulation results ( variance matrix):
-#> ========================================================
-#> Simulated standard errors
-#>          Direct  Indirect     Total
-#> INC   0.3680193 0.4929524 0.7676307
-#> HOVAL 0.1002746 0.1395496 0.2172252
-#> 
-#> Simulated z-values:
-#>          Direct  Indirect     Total
-#> INC   -3.003178 -1.553686 -2.437527
-#> HOVAL -2.809345 -1.423571 -2.211367
-#> 
-#> Simulated p-values:
-#>       Direct    Indirect Total   
-#> INC   0.0026718 0.12026  0.014788
-#> HOVAL 0.0049642 0.15457  0.027010
 set.seed(1)
 COL.D0.Bayes <- spBreg_lag(CRIME ~ INC + HOVAL, data=COL.OLD,
  listw=lw, Durbin=TRUE)
 print(summary(COL.D0.Bayes))
-#> 
-#> Iterations = 501:2500
-#> Thinning interval = 1 
-#> Number of chains = 1 
-#> Sample size per chain = 2000 
-#> 
-#> 1. Empirical mean and standard deviation for each variable,
-#>    plus standard error of the mean:
-#> 
-#>                 Mean       SD Naive SE Time-series SE
-#> (Intercept)  45.3106 13.74660 0.307383       0.307383
-#> INC          -0.9255  0.36779 0.008224       0.008224
-#> HOVAL        -0.2941  0.09628 0.002153       0.002153
-#> lag.INC      -0.5834  0.63860 0.014279       0.014279
-#> lag.HOVAL     0.2396  0.19206 0.004295       0.004295
-#> rho           0.3932  0.16296 0.003644       0.003644
-#> sige        109.7428 24.97328 0.558419       0.607251
-#> 
-#> 2. Quantiles for each variable:
-#> 
-#>                 2.5%     25%      50%      75%    97.5%
-#> (Intercept) 19.18523 35.9952  45.0347  54.5253  72.8545
-#> INC         -1.63995 -1.1761  -0.9206  -0.6651  -0.2148
-#> HOVAL       -0.48230 -0.3566  -0.2931  -0.2308  -0.1021
-#> lag.INC     -1.82283 -1.0066  -0.5712  -0.1763   0.6692
-#> lag.HOVAL   -0.14391  0.1068   0.2393   0.3670   0.6168
-#> rho          0.06053  0.2826   0.3977   0.5078   0.6949
-#> sige        71.19421 91.4872 106.1690 123.7826 170.9503
-#> 
 print(summary(impacts(COL.D0.Bayes, tr=trMatc), short=TRUE, zstats=TRUE))
-#> Impact measures (mixed, trace):
-#>          Direct   Indirect       Total
-#> INC   -1.027773 -1.4589793 -2.48675192
-#> HOVAL -0.280732  0.1908138 -0.08991828
-#> ========================================================
-#> Simulation results ( variance matrix):
-#> ========================================================
-#> Simulated standard errors
-#>           Direct  Indirect     Total
-#> INC   0.38264314 1.3114491 1.4534522
-#> HOVAL 0.09937135 0.3347428 0.3698437
-#> 
-#> Simulated z-values:
-#>          Direct   Indirect      Total
-#> INC   -2.739321 -1.2582210 -1.8564594
-#> HOVAL -2.838369  0.5610536 -0.2548209
-#> 
-#> Simulated p-values:
-#>       Direct    Indirect Total   
-#> INC   0.0061566 0.20831  0.063388
-#> HOVAL 0.0045345 0.57476  0.798861
 set.seed(1)
 COL.D1.Bayes <- spBreg_lag(CRIME ~ DISCBD + INC + HOVAL, data=COL.OLD,
  listw=lw, Durbin= ~ INC)
 print(summary(COL.D1.Bayes))
-#> 
-#> Iterations = 501:2500
-#> Thinning interval = 1 
-#> Number of chains = 1 
-#> Sample size per chain = 2000 
-#> 
-#> 1. Empirical mean and standard deviation for each variable,
-#>    plus standard error of the mean:
-#> 
-#>                 Mean       SD Naive SE Time-series SE
-#> (Intercept)  56.4447 13.43635 0.300446       0.300446
-#> DISCBD       -4.7748  2.14395 0.047940       0.047940
-#> INC          -0.9493  0.34681 0.007755       0.007755
-#> HOVAL        -0.1798  0.09983 0.002232       0.002232
-#> lag.INC       0.4262  0.63352 0.014166       0.014166
-#> rho           0.1873  0.18174 0.004064       0.004064
-#> sige        103.8701 23.29384 0.520866       0.566802
-#> 
-#> 2. Quantiles for each variable:
-#> 
-#>                2.5%       25%      50%      75%     97.5%
-#> (Intercept) 30.5093 47.769573  56.4789  65.0877  84.86254
-#> DISCBD      -9.2416 -6.153288  -4.7453  -3.3591  -0.67219
-#> INC         -1.6655 -1.174948  -0.9451  -0.7102  -0.28365
-#> HOVAL       -0.3744 -0.247270  -0.1775  -0.1128   0.01524
-#> lag.INC     -0.8028  0.004752   0.4224   0.8433   1.66618
-#> rho         -0.2116  0.072536   0.1836   0.3067   0.53579
-#> sige        67.8946 87.054945 100.9815 116.4528 161.25826
-#> 
 print(summary(impacts(COL.D1.Bayes, tr=trMatc), short=TRUE, zstats=TRUE))
-#> Impact measures (mixed, trace):
-#>            Direct    Indirect      Total
-#> DISCBD -4.8148501 -1.06067837 -5.8755285
-#> INC    -0.9381442  0.29454370 -0.6436005
-#> HOVAL  -0.1813164 -0.03994275 -0.2212591
-#> ========================================================
-#> Simulation results ( variance matrix):
-#> ========================================================
-#> Simulated standard errors
-#>           Direct   Indirect     Total
-#> DISCBD 2.1926383 1.82145831 3.3798560
-#> INC    0.3502903 0.81693522 0.8950370
-#> HOVAL  0.1015612 0.06692703 0.1429623
-#> 
-#> Simulated z-values:
-#>           Direct   Indirect      Total
-#> DISCBD -2.219068 -0.7449248 -1.8410439
-#> INC    -2.696796  0.3333552 -0.7511779
-#> HOVAL  -1.802805 -0.7412143 -1.6277185
-#> 
-#> Simulated p-values:
-#>        Direct   Indirect Total   
-#> DISCBD 0.026482 0.45632  0.065615
-#> INC    0.007001 0.73887  0.452546
-#> HOVAL  0.071419 0.45856  0.103585
 #data(elect80, package="spData")
 #lw <- spdep::nb2listw(e80_queen, zero.policy=TRUE)
 #el_ml <- lagsarlm(log(pc_turnout) ~ log(pc_college) + log(pc_homeownership)
@@ -962,7 +403,7 @@ 
Examples

 #print(summary(el_B))
 #print(el_ml$timings)
 #print(attr(el_B, "timings"))
-# }
+}
@@ -977,7 +418,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/docs/reference/SLX.html b/docs/reference/SLX.html index bdadaf2..698dc47 100644 --- a/docs/reference/SLX.html +++ b/docs/reference/SLX.html @@ -17,7 +17,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -69,8 +69,8 @@

Spatial Durbin linear (SLX, spatially lagged X) model

- 
lmSLX(formula, data = list(), listw, na.action, weights=NULL,
- Durbin=TRUE, zero.policy=NULL)
+    
lmSLX(formula, data = list(), listw, na.action, weights=NULL, Durbin=TRUE,
+ zero.policy=NULL)
 create_WX(x, listw, zero.policy=NULL, prefix="")
 # S3 method for SlX
 impacts(obj, ...)
@@ -106,8 +106,7 @@ 
Arguments

 
default TRUE for lmSLX (Durbin model including WX); if TRUE, full spatial Durbin model; if a formula object, the subset of explanatory variables to lag

 
   
zero.policy

-
default NULL, use global option value; if TRUE assign zero to the lagged value of zones without 
-neighbours, if FALSE assign NA

+
default NULL, use global option value; if TRUE assign zero to the lagged value of zones without neighbours, if FALSE assign NA

 
   
obj

 
A spatial regression object created by lmSLX

@@ -174,7 +173,7 @@ 
Examples

 #> F-statistic: 17.12 on 4 and 44 DF,  p-value: 1.553e-08
 #> 
 summary(impacts(COL.SLX))
-#> Impact measures (SlX, estimable, n-k):
+#> Impact measures (SlX, glht, n-k):
 #>           Direct   Indirect       Total
 #> INC   -1.1089293 -1.3709725 -2.47990173
 #> HOVAL -0.2897283  0.1917608 -0.09796753
@@ -196,7 +195,7 @@ 
Examples

 #> 
 COL.SLX <- lmSLX(CRIME ~ INC + HOVAL + I(HOVAL^2), data=COL.OLD, listw=lw, Durbin=TRUE)
 summary(impacts(COL.SLX))
-#> Impact measures (SlX, estimable, n-k):
+#> Impact measures (SlX, glht, n-k):
 #>                  Direct     Indirect       Total
 #> INC        -0.947594274 -1.275338647 -2.22293292
 #> HOVAL      -0.777427839 -0.355048446 -1.13247628
@@ -248,7 +247,7 @@ 
Examples

 #> 
 COL.SLX <- lmSLX(CRIME ~ INC + HOVAL + I(HOVAL^2), data=COL.OLD, listw=lw, Durbin=~INC)
 summary(impacts(COL.SLX))
-#> Impact measures (SlX, estimable, n-k):
+#> Impact measures (SlX, glht, n-k):
 #>                  Direct  Indirect        Total
 #> INC        -1.079064628 -1.010896 -2.089960575
 #> HOVAL      -0.634518755        NA -0.634518755
@@ -320,7 +319,7 @@ 
Examples

 #> F-statistic: 26.49 on 2 and 46 DF,  p-value: 2.214e-08
 #> 
 summary(impacts(COL.SLX))
-#> Impact measures (SlX, estimable, n-k):
+#> Impact measures (SlX, glht, n-k):
 #>        Direct  Indirect     Total
 #> INC -1.588901 -1.085867 -2.674768
 #> ========================================================
@@ -336,7 +335,7 @@ 
Examples

 #>     Direct     Indirect Total     
 #> INC 8.2671e-06 0.024029 5.1387e-11
 #> 
-# \dontrun{
+if (FALSE) {
 crds <- cbind(COL.OLD$X, COL.OLD$Y)
 mdist <- sqrt(sum(diff(apply(crds, 2, range))^2))
 dnb <- spdep::dnearneigh(crds, 0, mdist)
@@ -350,66 +349,12 @@ 
Examples

 }
 opt <- optimize(f, interval=c(0.1, 4), form=CRIME ~ INC + HOVAL,
  data=COL.OLD, dnb=dnb, dists=dists, verbose=TRUE, maximum=TRUE)
-#> power: 1.589667 logLik: -172.6864 
-#> power: 2.510333 logLik: -177.741 
-#> power: 1.020665 logLik: -171.5379 
-#> power: 0.8721475 logLik: -171.7979 
-#> power: 1.11302 logLik: -171.4973 
-#> power: 1.107329 logLik: -171.497 
-#> power: 1.105705 logLik: -171.497 
-#> power: 1.105746 logLik: -171.497 
-#> power: 1.105664 logLik: -171.497 
-#> power: 1.105705 logLik: -171.497 
 glst <- lapply(dists, function(d) 1/(d^opt$maximum))
 lw <- spdep::nb2listw(dnb, glist=glst, style="B")
 SLX <- lmSLX(CRIME ~ INC + HOVAL, data=COL.OLD, listw=lw)
 summary(SLX)
-#> 
-#> Call:
-#> lm(formula = formula(paste("y ~ ", paste(colnames(x)[-1], collapse = "+"))), 
-#>     data = as.data.frame(x), weights = weights)
-#> 
-#> Residuals:
-#>      Min       1Q   Median       3Q      Max 
-#> -25.1535  -4.7529  -0.9819   4.8053  22.2274 
-#> 
-#> Coefficients:
-#>                 Estimate Std. Error t value Pr(>|t|)  
-#> (Intercept)     18.78498   11.23795   1.672   0.1019  
-#> INC             -0.65428    0.29463  -2.221   0.0317 *
-#> HOVAL           -0.18244    0.08138  -2.242   0.0302 *
-#> lag..Intercept.  6.27790    4.44284   1.413   0.1648  
-#> lag.INC         -0.12958    0.28794  -0.450   0.6550  
-#> lag.HOVAL        0.02668    0.11273   0.237   0.8141  
-#> ---
-#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-#> 
-#> Residual standard error: 8.553 on 43 degrees of freedom
-#> Multiple R-squared:  0.7659,	Adjusted R-squared:  0.7387 
-#> F-statistic: 28.14 on 5 and 43 DF,  p-value: 1.544e-12
-#> 
 summary(impacts(SLX))
-#> Impact measures (SlX, estimable, n-k):
-#>           Direct    Indirect      Total
-#> INC   -0.6542760 -0.12957739 -0.7838534
-#> HOVAL -0.1824383  0.02667561 -0.1557627
-#> ========================================================
-#> Standard errors:
-#>           Direct  Indirect     Total
-#> INC   0.29463342 0.2879383 0.3742251
-#> HOVAL 0.08138257 0.1127279 0.1404591
-#> ========================================================
-#> Z-values:
-#>          Direct   Indirect     Total
-#> INC   -2.220644 -0.4500179 -2.094604
-#> HOVAL -2.241736  0.2366371 -1.108954
-#> 
-#> p-values:
-#>       Direct   Indirect Total   
-#> INC   0.026375 0.65270  0.036206
-#> HOVAL 0.024978 0.81294  0.267450
-#> 
-# }
+}
 COL.SLX <- lmSLX(CRIME ~ INC + HOVAL, data=COL.OLD, listw=lw)
 pslx0 <- predict(COL.SLX)
 pslx1 <- predict(COL.SLX, newdata=COL.OLD, listw=lw)
@@ -419,9 +364,9 @@ 
Examples

 COL.OLD1$INC <- COL.OLD1$INC + 1
 pslx2 <- predict(COL.SLX, newdata=COL.OLD1, listw=lw)
 sum(coef(COL.SLX)[c(2,4)])
-#> [1] 5.623621
+#> [1] -2.479902
 mean(pslx2-pslx1)
-#> [1] -1.425045
+#> [1] -2.479902
@@ -436,7 +381,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/docs/reference/SpatialFiltering.html b/docs/reference/SpatialFiltering.html index 304f392..0438ab7 100644 --- a/docs/reference/SpatialFiltering.html +++ b/docs/reference/SpatialFiltering.html @@ -17,7 +17,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -198,8 +198,8 @@

Examples

#> 1 30.34786 #> 2 19.13010 #> 3 -18.12234 -#> 4 19.19379 -#> 5 22.99586 +#> 4 -19.19379 +#> 5 -22.99586 #> 6 17.12127 #> 7 11.66487 lmsar <- lm(CRIME ~ INC + HOVAL + fitted(sarcol), data=columbus) @@ -220,8 +220,8 @@

Examples

#> fitted(sarcol)vec5 30.34786 8.87679 3.419 0.00149 ** #> fitted(sarcol)vec3 19.13010 8.87679 2.155 0.03739 * #> fitted(sarcol)vec1 -18.12234 8.87679 -2.042 0.04800 * -#> fitted(sarcol)vec10 19.19379 8.87679 2.162 0.03679 * -#> fitted(sarcol)vec14 22.99586 8.87679 2.591 0.01341 * +#> fitted(sarcol)vec10 -19.19379 8.87679 -2.162 0.03679 * +#> fitted(sarcol)vec14 -22.99586 8.87679 -2.591 0.01341 * #> fitted(sarcol)vec11 17.12127 8.87679 1.929 0.06106 . #> fitted(sarcol)vec2 11.66487 8.87679 1.314 0.19649 #> --- @@ -239,8 +239,8 @@

Examples

#> fitted(sarcol)vec5 30.3478552 8.87679493 3.418785 1.486164e-03 #> fitted(sarcol)vec3 19.1300996 8.87679493 2.155068 3.738943e-02 #> fitted(sarcol)vec1 -18.1223409 8.87679493 -2.041541 4.800339e-02 -#> fitted(sarcol)vec10 19.1937947 8.87679493 2.162244 3.679422e-02 -#> fitted(sarcol)vec14 22.9958588 8.87679493 2.590559 1.340783e-02 +#> fitted(sarcol)vec10 -19.1937947 8.87679493 -2.162244 3.679422e-02 +#> fitted(sarcol)vec14 -22.9958588 8.87679493 -2.590559 1.340783e-02 #> fitted(sarcol)vec11 17.1212741 8.87679493 1.928768 6.106079e-02 #> fitted(sarcol)vec2 11.6648669 8.87679493 1.314085 1.964945e-01 anova(lmbase, lmsar) @@ -342,6 +342,7 @@

Examples

NA.columbus$CRIME[20:25] <- NA COL.SF.NA <- SpatialFiltering(CRIME ~ INC + HOVAL, data=NA.columbus, nb=col.gal.nb, style="W", na.action=na.exclude) +#> Warning: subsetting caused increase in subgraph count COL.SF.NA$na.action #> 20 21 22 23 24 25 #> 20 21 22 23 24 25 @@ -388,7 +389,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/docs/reference/aple.html b/docs/reference/aple.html index d1d1b56..398ac03 100644 --- a/docs/reference/aple.html +++ b/docs/reference/aple.html @@ -17,7 +17,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -114,7 +114,6 @@

See also

Examples

wheat <- st_read(system.file("shapes/wheat.shp", package="spData")[1], quiet=TRUE)
 library(spdep)
-#> Loading required package: sp
 #> 
 #> Attaching package: ‘spdep’
 #> The following objects are masked from ‘package:spatialreg’:
@@ -148,20 +147,9 @@ 
Examples

 #> 
 aple(as.vector(scale(wheat$yield_detrend, scale=FALSE)), spdep::nb2listw(nbr12, style="W"))
 #> [1] 0.6601805
-# \dontrun{
+if (FALSE) {
 errorsarlm(yield_detrend ~ 1, wheat, spdep::nb2listw(nbr12, style="W"))
-#> 
-#> Call:
-#> errorsarlm(formula = yield_detrend ~ 1, data = wheat, listw = spdep::nb2listw(nbr12, 
-#>     style = "W"))
-#> Type: error 
-#> 
-#> Coefficients:
-#>      lambda (Intercept) 
-#>  0.60189686 -0.00251772 
-#> 
-#> Log likelihood: -192.9519 
-# }
+}
@@ -176,7 +164,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/docs/reference/aple.mc.html b/docs/reference/aple.mc.html index 3461338..ad8e8c8 100644 --- a/docs/reference/aple.mc.html +++ b/docs/reference/aple.mc.html @@ -17,7 +17,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -111,7 +111,7 @@

See also

Examples

- 
# \dontrun{
+    
if (FALSE) {
 wheat <- st_read(system.file("shapes/wheat.shp", package="spData")[1], quiet=TRUE)
 nbr1 <- spdep::poly2nb(wheat, queen=FALSE)
 nbrl <- spdep::nblag(nbr1, 2)
@@ -127,23 +127,10 @@ 
Examples

 boot_out_ser <- aple.mc(as.vector(scale(wheat$yield_detrend, scale=FALSE)),
  spdep::nb2listw(nbr12, style="W"), nsim=500)
 plot(boot_out_ser)
-
 boot_out_ser
-#> 
-#> DATA PERMUTATION
-#> 
-#> 
-#> Call:
-#> boot(data = x, statistic = aple.boot, R = nsim, sim = "permutation", 
-#>     pre = pre, parallel = parallel, ncpus = ncpus, cl = cl)
-#> 
-#> 
-#> Bootstrap Statistics :
-#>      original     bias    std. error
-#> t1* 0.6601805 -0.6708415   0.1092922
 library(parallel)
 oldCores <- set.coresOption(NULL)
-nc <- detectCores(logical=FALSE)
+nc <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L
 # set nc to 1L here
 if (nc > 1L) nc <- 1L
 invisible(set.coresOption(nc))
@@ -161,21 +148,9 @@ 
Examples

   stopCluster(cl)
 }
 boot_out_par
-#> 
-#> DATA PERMUTATION
-#> 
-#> 
-#> Call:
-#> boot(data = x, statistic = aple.boot, R = nsim, sim = "permutation", 
-#>     pre = pre, parallel = parallel, ncpus = ncpus, cl = cl)
-#> 
-#> 
-#> Bootstrap Statistics :
-#>      original     bias    std. error
-#> t1* 0.6601805 -0.6708415   0.1092922
 invisible(set.coresOption(oldCores))
 RNGkind(oldRNG[1], oldRNG[2])
-# }
+}
@@ -190,7 +165,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/docs/reference/aple.plot.html b/docs/reference/aple.plot.html index 804a4c1..f792cb2 100644 --- a/docs/reference/aple.plot.html +++ b/docs/reference/aple.plot.html @@ -17,7 +17,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -127,7 +127,7 @@

See also

Examples

- 
# \dontrun{
+    
if (FALSE) {
 wheat <- st_read(system.file("shapes/wheat.shp", package="spData")[1], quiet=TRUE)
 nbr1 <- spdep::poly2nb(wheat, queen=FALSE)
 nbrl <- spdep::nblag(nbr1, 2)
@@ -141,72 +141,18 @@ 
Examples

 abline(lm_obj)
 abline(v=0, h=0, lty=2)
 zz <- summary(influence.measures(lm_obj))
-#> Potentially influential observations of
-#> 	 lm(formula = Y ~ X, data = plt_out) :
-#> 
-#>     dfb.1_ dfb.X dffit   cov.r   cook.d hat    
-#> 34  -0.12   0.01 -0.12    0.98_*  0.01   0.00  
-#> 50   0.10   0.01  0.11    0.98_*  0.01   0.00  
-#> 60   0.00   0.00  0.00    1.01_*  0.00   0.01  
-#> 118  0.01   0.01  0.02    1.01_*  0.00   0.01  
-#> 137 -0.10  -0.01 -0.10    0.98_*  0.01   0.00  
-#> 143 -0.02  -0.04 -0.05    1.01_*  0.00   0.01  
-#> 157 -0.10  -0.05 -0.11    0.99_*  0.01   0.00  
-#> 166  0.00   0.00  0.00    1.02_*  0.00   0.02_*
-#> 168  0.01   0.02  0.03    1.01_*  0.00   0.01  
-#> 176 -0.10   0.17 -0.20_*  0.99    0.02   0.01  
-#> 177  0.03  -0.07  0.08    1.01_*  0.00   0.01  
-#> 191  0.01   0.04  0.04    1.02_*  0.00   0.02_*
-#> 192  0.10   0.18  0.20_*  0.99    0.02   0.01  
-#> 201 -0.10   0.07 -0.12    0.99_*  0.01   0.00  
-#> 216  0.02   0.05  0.05    1.02_*  0.00   0.01_*
-#> 217  0.03   0.08  0.09    1.02_*  0.00   0.01_*
-#> 225 -0.11  -0.07 -0.13    0.98_*  0.01   0.00  
-#> 237 -0.10   0.02 -0.10    0.99_*  0.01   0.00  
-#> 242 -0.02  -0.04 -0.04    1.01_*  0.00   0.01  
-#> 287  0.14  -0.23  0.27_*  0.97_*  0.04   0.01  
-#> 290 -0.18  -0.35 -0.40_*  0.95_*  0.08   0.01  
-#> 295 -0.01  -0.01 -0.01    1.01_*  0.00   0.01  
-#> 322  0.00   0.00  0.00    1.01_*  0.00   0.01  
-#> 325 -0.10  -0.07 -0.12    0.99_*  0.01   0.00  
-#> 351  0.19  -0.04  0.20_*  0.94_*  0.02   0.00  
-#> 369  0.01  -0.03  0.03    1.01_*  0.00   0.01  
-#> 376 -0.05  -0.13 -0.14    1.02_*  0.01   0.02_*
-#> 392 -0.04   0.08 -0.09    1.01_*  0.00   0.01  
-#> 393 -0.03   0.06 -0.07    1.01_*  0.00   0.01  
-#> 394 -0.01   0.03 -0.03    1.01_*  0.00   0.01  
-#> 402  0.10   0.02  0.10    0.99_*  0.01   0.00  
-#> 429  0.13  -0.10  0.16    0.98_*  0.01   0.00  
-#> 430 -0.11  -0.23 -0.25_*  0.99    0.03   0.01  
-#> 438  0.00  -0.01 -0.01    1.01_*  0.00   0.01  
-#> 442 -0.01   0.04 -0.04    1.02_*  0.00   0.02_*
-#> 443 -0.01   0.02 -0.02    1.02_*  0.00   0.01_*
-#> 461  0.02   0.04  0.04    1.01_*  0.00   0.01  
-#> 462  0.01   0.03  0.03    1.03_*  0.00   0.02_*
-#> 466  0.01  -0.02  0.02    1.02_*  0.00   0.01_*
-#> 467 -0.03   0.08 -0.08    1.02_*  0.00   0.01_*
-#> 468  0.02  -0.04  0.04    1.01_*  0.00   0.01  
-#> 480  0.13   0.05  0.14    0.97_*  0.01   0.00  
-#> 488  0.16   0.09  0.18    0.96_*  0.02   0.00  
-#> 492 -0.13   0.12 -0.17    0.98_*  0.01   0.00  
 infl <- as.integer(rownames(zz))
 points(plt_out$X[infl], plt_out$Y[infl], pch=3, cex=0.6, col="red")
-
 crossprod(plt_out$Y, plt_out$X)/crossprod(plt_out$X)
-#>           [,1]
-#> [1,] 0.6601805
 wheat$localAple <- localAple(as.vector(scale(wheat$yield_detrend, scale=FALSE)),
  spdep::nb2listw(nbr12, style="W"))
 mean(wheat$localAple)
-#> [1] 0.6601805
 hist(wheat$localAple)
-
 opar <- par(no.readonly=TRUE)
 plot(wheat[,"localAple"], reset=FALSE)
 text(st_coordinates(st_centroid(st_geometry(wheat)))[infl,], labels=rep("*", length(infl)))
-
 par(opar)
-# }
+}
@@ -221,7 +167,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/docs/reference/do_ldet.html b/docs/reference/do_ldet.html index 105ff01..7d93dd2 100644 --- a/docs/reference/do_ldet.html +++ b/docs/reference/do_ldet.html @@ -17,7 +17,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -148,9 +148,9 @@

Arguments

default TRUE: truncate Smirnov correction term, see trW

eq7
-

default TRUE

-

use equation 7 in Smirnov and Anselin (2009), if FALSE no unit root correction

-
SE_method
+

default TRUE; use equation 7 in Smirnov and Anselin (2009), if FALSE no unit root correction

+ +
SE_method

default “LU”, alternatively “MC”; underlying lndet method to use for generating SE toolbox emulation grid

nrho
@@ -609,7 +609,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/docs/reference/eigenw.html b/docs/reference/eigenw.html index 9a5aac9..b29e22d 100644 --- a/docs/reference/eigenw.html +++ b/docs/reference/eigenw.html @@ -23,7 +23,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -221,7 +221,7 @@

Examples

rg_GS <- griffith_sone(P=7, Q=7, type="rook") all.equal(rg_eig, rg_GS) #> [1] TRUE -# \dontrun{ +if (FALSE) { run <- FALSE if (require("RSpectra", quietly=TRUE)) run <- TRUE if (run) { @@ -230,22 +230,18 @@

Examples

resn <- eigs(B, k=1, which="SR")$values print(Re(c(resn, res1))) } -#> [1] -3.695518 3.695518 if (run) { print(all.equal(range(Re(rg_eig)), c(resn, res1))) } -#> [1] TRUE if (run) { lw <- spdep::nb2listw(rg, style="W") rg_eig <- eigenw(similar.listw(lw)) print(range(Re(rg_eig))) } -#> [1] -1 1 if (run) { W <- as(lw, "CsparseMatrix") print(Re(c(eigs(W, k=1, which="SR")$values, eigs(W, k=1, which="LR")$values))) -}# } -#> [1] -1 1 +}} @@ -260,7 +256,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

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z`~yAB>l1W@O;G$6uyYSIVd;rNx>j&Em-@Z0+_0DA*xS^??sv9HD*dcar&p$Z)^6TH zyQDKA(4@583_f7#-_br^QGYElbRce@!$Z~SEO*@+#Jh)^AGy_5<;o?YhUD7R=s<2~ zObMLmUdcD2Z!58hZlmsAXcv>-G@X3o?E-{(xaqnWCr0(kfzT%tgi7yYmEryD_;b%} zC#}XHuAQUg4~Wo{`=@FF{=K;!wW7o0JeQois-k=q9k=M?rbFTsHXC21G6`XPqP4ml zAl^ymR@f#Te#XmT481+=^T&XJ2!f&ifA#`U1C5ew)jjOUEDoDv`Kn_0CFrW zGVjp}SZIndDT`y-kB3J}yy>Z*?;XscZW2v!waE1~de1(n%D2l~=&rZ_xDiOyssZM1 z%KcD7hKNiAhiE zx^x>_mOtzpKB_pcj}iaPCegRq6GY&ccLwBh$9S9X?;26>FfY18Ux^-vYjRY26-({c zV9-%m5?qAe6bG=j{H spatialreg - 1.2-7 + 1.3-1 @@ -72,7 +72,7 @@

    Spatial simultaneous autoregressive SAC model estimation by GMM

    gstsls(formula, data = list(), listw, listw2 = NULL, na.action = na.fail, 
-    zero.policy = NULL, pars=NULL, scaleU=FALSE, control = list(), 
+    zero.policy = attr(listw, "zero.policy"), pars=NULL, scaleU=FALSE, control = list(), 
     verbose=NULL, method="nlminb", robust=FALSE, legacy=FALSE, W2X=TRUE) 
 # S3 method for Gmsar
 impacts(obj, ..., n = NULL, tr = NULL, R = NULL,
@@ -327,7 +327,7 @@ 
    Examples
    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/impacts.html b/docs/reference/impacts.html index 27950c6..6a506b2 100644 --- a/docs/reference/impacts.html +++ b/docs/reference/impacts.html @@ -17,7 +17,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -229,12 +229,12 @@

    Examples

    #> #> Residuals: #> Min 1Q Median 3Q Max -#> -37.4497094 -5.4565567 0.0016388 6.7159553 24.7107975 +#> -37.4497093 -5.4565567 0.0016387 6.7159553 24.7107978 #> #> Type: lag #> Coefficients: (asymptotic standard errors) #> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 46.851430 7.314754 6.4051 1.503e-10 +#> (Intercept) 46.851431 7.314754 6.4051 1.503e-10 #> INC -1.073533 0.310872 -3.4533 0.0005538 #> HOVAL -0.269997 0.090128 -2.9957 0.0027381 #> @@ -247,7 +247,7 @@

    Examples

    #> ML residual variance (sigma squared): 99.164, (sigma: 9.9581) #> Number of observations: 49 #> Number of parameters estimated: 5 -#> AIC: 376.34, (AIC for lm: 382.75) +#> AIC: NA (not available for weighted model), (AIC for lm: 382.75) #> LM test for residual autocorrelation #> test value: 0.19184, p-value: 0.66139 #> @@ -280,7 +280,7 @@

    Examples

    #> ML residual variance (sigma squared): 95.051, (sigma: 9.7494) #> Number of observations: 49 #> Number of parameters estimated: 7 -#> AIC: 378.03, (AIC for lm: 380.2) +#> AIC: NA (not available for weighted model), (AIC for lm: 380.2) #> LM test for residual autocorrelation #> test value: 0.101, p-value: 0.75063 #> @@ -298,10 +298,10 @@

    Examples

    #> Type: mixed #> Coefficients: (asymptotic standard errors) #> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 51.951208 12.577338 4.1305 3.619e-05 +#> (Intercept) 51.951213 12.577339 4.1305 3.619e-05 #> INC -1.038812 0.337656 -3.0765 0.002094 #> HOVAL -0.269345 0.090406 -2.9793 0.002889 -#> lag.INC -0.254653 0.544298 -0.4679 0.639888 +#> lag.INC -0.254653 0.544298 -0.4679 0.639887 #> #> Rho: 0.35028, LR test value: 3.4351, p-value: 0.063823 #> Asymptotic standard error: 0.1617 @@ -312,7 +312,7 @@

    Examples

    #> ML residual variance (sigma squared): 99.846, (sigma: 9.9923) #> Number of observations: 49 #> Number of parameters estimated: 6 -#> AIC: 378.13, (AIC for lm: 379.57) +#> AIC: NA (not available for weighted model), (AIC for lm: 379.57) #> LM test for residual autocorrelation #> test value: 2.5646, p-value: 0.10928 #> @@ -323,12 +323,12 @@

    Examples

    impacts(lobj, listw=listw) #> Impact measures (lag, exact): #> Direct Indirect Total -#> INC -1.1225155 -0.6783818 -1.8008973 +#> INC -1.1225156 -0.6783818 -1.8008973 #> HOVAL -0.2823163 -0.1706152 -0.4529315 impacts(lobj, tr=trMatc) #> Impact measures (lag, trace): #> Direct Indirect Total -#> INC -1.1225155 -0.6783818 -1.8008973 +#> INC -1.1225156 -0.6783818 -1.8008973 #> HOVAL -0.2823163 -0.1706152 -0.4529315 impacts(lobj, tr=trMC) #> Impact measures (lag, trace): @@ -338,131 +338,131 @@

    Examples

    impacts(lobj, evalues=ev) #> Impact measures (lag, evalues): #> Direct Indirect Total -#> INC -1.1225155 -0.6783818 -1.8008973 +#> INC -1.1225156 -0.6783818 -1.8008973 #> HOVAL -0.2823163 -0.1706152 -0.4529315 library(coda) lobjIQ5 <- impacts(lobj, tr=trMatc, R=200, Q=5) summary(lobjIQ5, zstats=TRUE, short=TRUE) #> Impact measures (lag, trace): #> Direct Indirect Total -#> INC -1.1225155 -0.6783818 -1.8008973 +#> INC -1.1225156 -0.6783818 -1.8008973 #> HOVAL -0.2823163 -0.1706152 -0.4529315 #> ======================================================== #> Simulation results ( variance matrix): #> ======================================================== #> Simulated standard errors #> Direct Indirect Total -#> INC 0.3366296 0.4409179 0.6472849 -#> HOVAL 0.1027792 0.1224540 0.2005825 +#> INC 0.3298544 0.3834114 0.5945946 +#> HOVAL 0.1014420 0.1261509 0.2033617 #> #> Simulated z-values: #> Direct Indirect Total -#> INC -3.414037 -1.681159 -2.920691 -#> HOVAL -2.801138 -1.584607 -2.402703 +#> INC -3.417467 -1.886887 -3.112575 +#> HOVAL -2.895853 -1.551116 -2.406724 #> #> Simulated p-values: #> Direct Indirect Total -#> INC 0.00064008 0.092732 0.0034926 -#> HOVAL 0.00509228 0.113056 0.0162744 +#> INC 0.00063207 0.059176 0.0018546 +#> HOVAL 0.00378129 0.120874 0.0160963 summary(lobjIQ5, zstats=TRUE, short=TRUE, reportQ=TRUE) #> Impact measures (lag, trace): #> Direct Indirect Total -#> INC -1.1225155 -0.6783818 -1.8008973 +#> INC -1.1225156 -0.6783818 -1.8008973 #> HOVAL -0.2823163 -0.1706152 -0.4529315 #> ================================= #> Impact components #> $direct #> INC HOVAL -#> Q1 -1.073533437 -0.2699971234 +#> Q1 -1.073533466 -0.2699971236 #> Q2 0.000000000 0.0000000000 -#> Q3 -0.038985418 -0.0098049584 -#> Q4 -0.005269655 -0.0013253352 -#> Q5 -0.003276080 -0.0008239446 +#> Q3 -0.038985415 -0.0098049573 +#> Q4 -0.005269654 -0.0013253350 +#> Q5 -0.003276079 -0.0008239444 #> #> $indirect #> INC HOVAL #> Q1 0.00000000 0.000000000 -#> Q2 -0.43358911 -0.109049060 -#> Q3 -0.13613676 -0.034238835 -#> Q4 -0.06546039 -0.016463500 -#> Q5 -0.02529106 -0.006360783 +#> Q2 -0.43358910 -0.109049054 +#> Q3 -0.13613675 -0.034238831 +#> Q4 -0.06546038 -0.016463497 +#> Q5 -0.02529105 -0.006360781 #> #> $total #> INC HOVAL -#> Q1 -1.07353344 -0.269997123 -#> Q2 -0.43358911 -0.109049060 -#> Q3 -0.17512218 -0.044043793 -#> Q4 -0.07073005 -0.017788835 -#> Q5 -0.02856714 -0.007184727 +#> Q1 -1.07353347 -0.269997124 +#> Q2 -0.43358910 -0.109049054 +#> Q3 -0.17512216 -0.044043788 +#> Q4 -0.07073004 -0.017788832 +#> Q5 -0.02856713 -0.007184726 #> #> ======================================================== #> Simulation results ( variance matrix): #> ======================================================== #> Simulated standard errors #> Direct Indirect Total -#> INC 0.3366296 0.4409179 0.6472849 -#> HOVAL 0.1027792 0.1224540 0.2005825 +#> INC 0.3298544 0.3834114 0.5945946 +#> HOVAL 0.1014420 0.1261509 0.2033617 #> #> Simulated z-values: #> Direct Indirect Total -#> INC -3.414037 -1.681159 -2.920691 -#> HOVAL -2.801138 -1.584607 -2.402703 +#> INC -3.417467 -1.886887 -3.112575 +#> HOVAL -2.895853 -1.551116 -2.406724 #> #> Simulated p-values: #> Direct Indirect Total -#> INC 0.00064008 0.092732 0.0034926 -#> HOVAL 0.00509228 0.113056 0.0162744 +#> INC 0.00063207 0.059176 0.0018546 +#> HOVAL 0.00378129 0.120874 0.0160963 #> ======================================================== #> Simulated impact components z-values: #> $Direct -#> INC HOVAL -#> Q1 -3.327848 -2.7936866 -#> Q2 NaN NaN -#> Q3 -1.702009 -1.5031474 -#> Q4 -1.147968 -1.1167765 -#> Q5 -0.820115 -0.8730749 +#> INC HOVAL +#> Q1 -3.3247602 -2.9008692 +#> Q2 NaN NaN +#> Q3 -1.7540115 -1.4638039 +#> Q4 -1.2444388 -1.0830402 +#> Q5 -0.9405222 -0.8474166 #> #> $Indirect -#> INC HOVAL -#> Q1 NaN NaN -#> Q2 -2.733849 -2.1632321 -#> Q3 -1.702009 -1.5031474 -#> Q4 -1.147968 -1.1167765 -#> Q5 -0.820115 -0.8730749 +#> INC HOVAL +#> Q1 NaN NaN +#> Q2 -2.6977076 -2.1475985 +#> Q3 -1.7540115 -1.4638039 +#> Q4 -1.2444388 -1.0830402 +#> Q5 -0.9405222 -0.8474166 #> #> $Total -#> INC HOVAL -#> Q1 -3.327848 -2.7936866 -#> Q2 -2.733849 -2.1632321 -#> Q3 -1.702009 -1.5031474 -#> Q4 -1.147968 -1.1167765 -#> Q5 -0.820115 -0.8730749 +#> INC HOVAL +#> Q1 -3.3247602 -2.9008692 +#> Q2 -2.6977076 -2.1475985 +#> Q3 -1.7540115 -1.4638039 +#> Q4 -1.2444388 -1.0830402 +#> Q5 -0.9405222 -0.8474166 #> #> #> Simulated impact components p-values: #> $Direct -#> INC HOVAL -#> Q1 0.0008752 0.0052111 -#> Q2 NA NA -#> Q3 0.0887538 0.1328010 -#> Q4 0.2509819 0.2640899 -#> Q5 0.4121506 0.3826223 +#> INC HOVAL +#> Q1 0.00088495 0.0037213 +#> Q2 NA NA +#> Q3 0.07942853 0.1432475 +#> Q4 0.21333812 0.2787906 +#> Q5 0.34694975 0.3967630 #> #> $Indirect #> INC HOVAL #> Q1 NA NA -#> Q2 0.0062599 0.030523 -#> Q3 0.0887538 0.132801 -#> Q4 0.2509819 0.264090 -#> Q5 0.4121506 0.382622 +#> Q2 0.0069819 0.031746 +#> Q3 0.0794285 0.143248 +#> Q4 0.2133381 0.278791 +#> Q5 0.3469497 0.396763 #> #> $Total -#> INC HOVAL -#> Q1 0.0008752 0.0052111 -#> Q2 0.0062599 0.0305233 -#> Q3 0.0887538 0.1328010 -#> Q4 0.2509819 0.2640899 -#> Q5 0.4121506 0.3826223 +#> INC HOVAL +#> Q1 0.00088495 0.0037213 +#> Q2 0.00698187 0.0317457 +#> Q3 0.07942853 0.1432475 +#> Q4 0.21333812 0.2787906 +#> Q5 0.34694975 0.3967630 #> impacts(mobj, listw=listw) #> Impact measures (mixed, exact): @@ -489,13 +489,9 @@

    Examples

    #> Direct Indirect Total #> INC -1.0968247 -0.8939687 -1.9907934 #> HOVAL -0.2781941 -0.1363596 -0.4145537 -# \dontrun{ +if (FALSE) { try(impacts(mobj, evalues=ev), silent=TRUE) -#> Impact measures (mixed, evalues): -#> Direct Indirect Total -#> INC -1.0418080 -1.4804246 -2.52223255 -#> HOVAL -0.2836325 0.2302055 -0.05342697 -# } +} summary(impacts(mobj, tr=trMatc, R=200), short=TRUE, zstats=TRUE) #> Impact measures (mixed, trace): #> Direct Indirect Total @@ -528,21 +524,21 @@

    Examples

    #> ======================================================== #> Simulated standard errors #> Direct Indirect Total -#> INC 0.3782124 0.6845199 0.7659469 -#> HOVAL 0.1031391 0.1954851 0.2548427 +#> INC 0.3752297 0.6525338 0.7183806 +#> HOVAL 0.1078220 0.1277117 0.1990840 #> #> Simulated z-values: -#> Direct Indirect Total -#> INC -2.898476 -1.2616836 -2.558777 -#> HOVAL -2.880290 -0.8992903 -1.855531 +#> Direct Indirect Total +#> INC -2.902700 -1.247249 -2.649084 +#> HOVAL -2.637881 -1.168002 -2.177921 #> #> Simulated p-values: #> Direct Indirect Total -#> INC 0.0037498 0.20706 0.010504 -#> HOVAL 0.0039731 0.36850 0.063520 +#> INC 0.0036996 0.21231 0.008071 +#> HOVAL 0.0083426 0.24281 0.029412 xobj <- lmSLX(CRIME ~ INC + HOVAL, columbus, listw) summary(impacts(xobj)) -#> Impact measures (SlX, estimable, n-k): +#> Impact measures (SlX, glht, n-k): #> Direct Indirect Total #> INC -1.1081273 -1.3834468 -2.49157410 #> HOVAL -0.2949095 0.2261538 -0.06875574 @@ -564,7 +560,7 @@

    Examples

    #> eobj <- errorsarlm(CRIME ~ INC + HOVAL, columbus, listw, etype="emixed") summary(impacts(eobj), adjust_k=TRUE) -#> Impact measures (SDEM, estimable, n): +#> Impact measures (SDEM, glht, n): #> Direct Indirect Total #> INC -1.0695301 -1.1967736 -2.2663036 #> HOVAL -0.2803441 0.1467585 -0.1335856 @@ -576,143 +572,29 @@

    Examples

    #> ======================================================== #> Z-values: #> Direct Indirect Total -#> INC -3.293714 -2.1034125 -3.6497667 +#> INC -3.293714 -2.1034124 -3.6497665 #> HOVAL -3.053548 0.7306064 -0.5749284 #> #> p-values: #> Direct Indirect Total #> INC 0.00098873 0.03543 0.00026248 -#> HOVAL 0.00226152 0.46502 0.56533972 +#> HOVAL 0.00226152 0.46502 0.56533971 #> -# \dontrun{ +if (FALSE) { mobj1 <- lagsarlm(CRIME ~ INC + HOVAL, columbus, listw, type="mixed", method="Matrix", control=list(fdHess=TRUE)) summary(mobj1) -#> -#> Call:lagsarlm(formula = CRIME ~ INC + HOVAL, data = columbus, listw = listw, -#> type = "mixed", method = "Matrix", control = list(fdHess = TRUE)) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -37.15904 -6.62594 -0.39823 6.57561 23.62757 -#> -#> Type: mixed -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 45.592896 14.240041 3.2017 0.0013660 -#> INC -0.939088 0.343069 -2.7373 0.0061942 -#> HOVAL -0.299605 0.090393 -3.3145 0.0009182 -#> lag.INC -0.618375 0.607708 -1.0176 0.3088903 -#> lag.HOVAL 0.266615 0.181524 1.4688 0.1418998 -#> -#> Rho: 0.38251, LR test value: 4.1648, p-value: 0.041272 -#> Asymptotic standard error: 0.17375 -#> z-value: 2.2015, p-value: 0.027702 -#> Wald statistic: 4.8465, p-value: 0.027702 -#> -#> Log likelihood: -182.0161 for mixed model -#> ML residual variance (sigma squared): 95.051, (sigma: 9.7494) -#> Number of observations: 49 -#> Number of parameters estimated: 7 -#> AIC: 378.03, (AIC for lm: 380.2) -#> LM test for residual autocorrelation -#> test value: 0.101, p-value: 0.75063 -#> set.seed(1) summary(impacts(mobj1, tr=trMatc, R=1000), zstats=TRUE, short=TRUE) -#> Impact measures (mixed, trace): -#> Direct Indirect Total -#> INC -1.0418080 -1.4804246 -2.52223255 -#> HOVAL -0.2836325 0.2302055 -0.05342697 -#> ======================================================== -#> Simulation results ( variance matrix): -#> ======================================================== -#> Simulated standard errors -#> Direct Indirect Total -#> INC 0.34686331 0.8324743 0.8959688 -#> HOVAL 0.09085583 0.3131242 0.3483785 -#> -#> Simulated z-values: -#> Direct Indirect Total -#> INC -3.023902 -1.8284275 -2.8695190 -#> HOVAL -3.120537 0.7910739 -0.1028036 -#> -#> Simulated p-values: -#> Direct Indirect Total -#> INC 0.0024954 0.067485 0.004111 -#> HOVAL 0.0018052 0.428901 0.918119 summary(impacts(mobj, tr=trMatc, R=1000), zstats=TRUE, short=TRUE) -#> Impact measures (mixed, trace): -#> Direct Indirect Total -#> INC -1.0418080 -1.4804246 -2.52223255 -#> HOVAL -0.2836325 0.2302055 -0.05342697 -#> ======================================================== -#> Simulation results ( variance matrix): -#> ======================================================== -#> Simulated standard errors -#> Direct Indirect Total -#> INC 0.3123750 0.8291512 0.8821167 -#> HOVAL 0.0922758 0.2961092 0.3267773 -#> -#> Simulated z-values: -#> Direct Indirect Total -#> INC -3.330304 -1.8060414 -2.8769263 -#> HOVAL -3.099811 0.8140556 -0.1376722 -#> -#> Simulated p-values: -#> Direct Indirect Total -#> INC 0.00086751 0.070912 0.0040157 -#> HOVAL 0.00193644 0.415613 0.8904995 mobj2 <- lagsarlm(CRIME ~ INC + HOVAL, columbus, listw, type="mixed", method="Matrix", control=list(fdHess=TRUE, optimHess=TRUE)) summary(impacts(mobj2, tr=trMatc, R=1000), zstats=TRUE, short=TRUE) -#> Impact measures (mixed, trace): -#> Direct Indirect Total -#> INC -1.0418080 -1.4804246 -2.52223255 -#> HOVAL -0.2836325 0.2302055 -0.05342697 -#> ======================================================== -#> Simulation results ( variance matrix): -#> ======================================================== -#> Simulated standard errors -#> Direct Indirect Total -#> INC 0.32853332 0.8044059 0.8615603 -#> HOVAL 0.09291975 0.3169361 0.3571304 -#> -#> Simulated z-values: -#> Direct Indirect Total -#> INC -3.231204 -1.8690203 -2.9771674 -#> HOVAL -3.026494 0.7323897 -0.1374858 -#> -#> Simulated p-values: -#> Direct Indirect Total -#> INC 0.0012327 0.06162 0.0029093 -#> HOVAL 0.0024741 0.46393 0.8906468 mobj3 <- lagsarlm(CRIME ~ INC + HOVAL, columbus, listw, type="mixed", method="spam", control=list(fdHess=TRUE)) summary(impacts(mobj3, tr=trMatc, R=1000), zstats=TRUE, short=TRUE) -#> Impact measures (mixed, trace): -#> Direct Indirect Total -#> INC -1.0418080 -1.4804246 -2.52223255 -#> HOVAL -0.2836325 0.2302055 -0.05342697 -#> ======================================================== -#> Simulation results ( variance matrix): -#> ======================================================== -#> Simulated standard errors -#> Direct Indirect Total -#> INC 0.32866009 0.8664620 0.9215792 -#> HOVAL 0.09837849 0.3110899 0.3492674 -#> -#> Simulated z-values: -#> Direct Indirect Total -#> INC -3.190433 -1.7325351 -2.766712 -#> HOVAL -2.890221 0.7694814 -0.128720 -#> -#> Simulated p-values: -#> Direct Indirect Total -#> INC 0.0014206 0.083178 0.0056625 -#> HOVAL 0.0038497 0.441608 0.8975792 -# } -# \dontrun{ +} +if (FALSE) { data(boston, package="spData") Wb <- as(spdep::nb2listw(boston.soi), "CsparseMatrix") trMatb <- trW(Wb, type="mult") @@ -721,126 +603,7 @@

    Examples

    data=boston.c, spdep::nb2listw(boston.soi), type="mixed", method="Matrix", control=list(fdHess=TRUE), trs=trMatb) summary(gp2mMi) -#> -#> Call:lagsarlm(formula = log(CMEDV) ~ CRIM + ZN + INDUS + CHAS + I(NOX^2) + -#> I(RM^2) + AGE + log(DIS) + log(RAD) + TAX + PTRATIO + B + -#> log(LSTAT), data = boston.c, listw = spdep::nb2listw(boston.soi), -#> type = "mixed", method = "Matrix", trs = trMatb, control = list(fdHess = TRUE)) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -0.6316833 -0.0629790 -0.0090776 0.0682421 0.6991072 -#> -#> Type: mixed -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 1.89816218 0.24400182 7.7793 7.327e-15 -#> CRIM -0.00571021 0.00093857 -6.0839 1.173e-09 -#> ZN 0.00069091 0.00051874 1.3319 0.1828931 -#> INDUS -0.00111343 0.00307380 -0.3622 0.7171778 -#> CHAS1 -0.04163225 0.02738839 -1.5201 0.1284937 -#> I(NOX^2) -0.01034950 0.19358633 -0.0535 0.9573639 -#> I(RM^2) 0.00794979 0.00102109 7.7856 6.883e-15 -#> AGE -0.00128789 0.00048929 -2.6322 0.0084838 -#> log(DIS) -0.12404108 0.09510145 -1.3043 0.1921304 -#> log(RAD) 0.05863502 0.02257529 2.5973 0.0093957 -#> TAX -0.00049084 0.00012146 -4.0412 5.317e-05 -#> PTRATIO -0.01319853 0.00595331 -2.2170 0.0266227 -#> B 0.00056383 0.00011084 5.0867 3.643e-07 -#> log(LSTAT) -0.24724454 0.02265149 -10.9152 < 2.2e-16 -#> lag.CRIM -0.00464215 0.00173900 -2.6694 0.0075978 -#> lag.ZN -0.00037937 0.00070703 -0.5366 0.5915659 -#> lag.INDUS 0.00025064 0.00385911 0.0649 0.9482165 -#> lag.CHAS1 0.12518252 0.04083949 3.0652 0.0021750 -#> lag.I(NOX^2) -0.38640401 0.22253428 -1.7364 0.0824967 -#> lag.I(RM^2) -0.00451252 0.00148919 -3.0302 0.0024440 -#> lag.AGE 0.00149678 0.00068470 2.1860 0.0288128 -#> lag.log(DIS) -0.00453785 0.10056961 -0.0451 0.9640105 -#> lag.log(RAD) -0.00940702 0.03127787 -0.3008 0.7636001 -#> lag.TAX 0.00041083 0.00017859 2.3004 0.0214237 -#> lag.PTRATIO 0.00060355 0.00789994 0.0764 0.9391011 -#> lag.B -0.00050781 0.00014107 -3.5996 0.0003187 -#> lag.log(LSTAT) 0.09846781 0.03574182 2.7550 0.0058697 -#> -#> Rho: 0.59578, LR test value: 181.68, p-value: < 2.22e-16 -#> Asymptotic standard error: 0.037474 -#> z-value: 15.899, p-value: < 2.22e-16 -#> Wald statistic: 252.76, p-value: < 2.22e-16 -#> -#> Log likelihood: 300.6131 for mixed model -#> ML residual variance (sigma squared): 0.016011, (sigma: 0.12654) -#> Number of observations: 506 -#> Number of parameters estimated: 29 -#> AIC: -543.23, (AIC for lm: -363.55) -#> LM test for residual autocorrelation -#> test value: 29.772, p-value: 4.8604e-08 -#> summary(impacts(gp2mMi, tr=trMatb, R=1000), zstats=TRUE, short=TRUE) -#> Impact measures (mixed, trace): -#> Direct Indirect Total -#> CRIM -0.0074555753 -1.815485e-02 -0.0256104225 -#> ZN 0.0006979073 7.279849e-05 0.0007707058 -#> INDUS -0.0012029822 -9.314672e-04 -0.0021344494 -#> CHAS1 -0.0198526436 2.265454e-01 0.2066927455 -#> I(NOX^2) -0.0955268264 -8.859909e-01 -0.9815177559 -#> I(RM^2) 0.0079983430 5.050177e-04 0.0085033608 -#> AGE -0.0011296134 1.646365e-03 0.0005167515 -#> log(DIS) -0.1410601687 -1.770278e-01 -0.3180879407 -#> log(RAD) 0.0641735548 5.761026e-02 0.1217838138 -#> TAX -0.0004651543 2.672120e-04 -0.0001979424 -#> PTRATIO -0.0147737151 -1.638465e-02 -0.0311583673 -#> B 0.0005265343 -3.879424e-04 0.0001385920 -#> log(LSTAT) -0.2578403213 -1.102144e-01 -0.3680547068 -#> ======================================================== -#> Simulation results ( variance matrix): -#> ======================================================== -#> Simulated standard errors -#> Direct Indirect Total -#> CRIM 0.0010076798 0.0036288635 0.0040404924 -#> ZN 0.0004892696 0.0012374985 0.0012596482 -#> INDUS 0.0028820758 0.0060784278 0.0057782835 -#> CHAS1 0.0271174438 0.0816949094 0.0902306757 -#> I(NOX^2) 0.1798525308 0.2954129372 0.2426994803 -#> I(RM^2) 0.0011004204 0.0031441374 0.0036256801 -#> AGE 0.0004759027 0.0012824325 0.0013506324 -#> log(DIS) 0.0884514493 0.1106832532 0.0757292416 -#> log(RAD) 0.0197638019 0.0513249132 0.0502885291 -#> TAX 0.0001125321 0.0003293654 0.0003385003 -#> PTRATIO 0.0054150585 0.0127889718 0.0122254626 -#> B 0.0001047613 0.0002376555 0.0002352788 -#> log(LSTAT) 0.0224736606 0.0610705732 0.0660452868 -#> -#> Simulated z-values: -#> Direct Indirect Total -#> CRIM -7.4241338 -5.0475149 -6.3848388 -#> ZN 1.4320070 0.1005269 0.6549761 -#> INDUS -0.4184563 -0.1088975 -0.3232704 -#> CHAS1 -0.7260662 2.7907466 2.3085356 -#> I(NOX^2) -0.5230875 -3.0572802 -4.1089447 -#> I(RM^2) 7.3364331 0.1789518 2.3818456 -#> AGE -2.4211451 1.2544530 0.3380060 -#> log(DIS) -1.5778892 -1.6651491 -4.2766929 -#> log(RAD) 3.2376717 1.1761160 2.4727856 -#> TAX -4.1072661 0.7673289 -0.6188112 -#> PTRATIO -2.7323833 -1.2913715 -2.5611570 -#> B 5.0195551 -1.6302856 0.5882763 -#> log(LSTAT) -11.4429268 -1.7773204 -5.5372071 -#> -#> Simulated p-values: -#> Direct Indirect Total -#> CRIM 1.1346e-13 4.4759e-07 1.7158e-10 -#> ZN 0.1521418 0.9199260 0.512483 -#> INDUS 0.6756135 0.9132838 0.746490 -#> CHAS1 0.4677982 0.0052587 0.020969 -#> I(NOX^2) 0.6009133 0.0022336 3.9747e-05 -#> I(RM^2) 2.1938e-13 0.8579755 0.017226 -#> AGE 0.0154717 0.2096774 0.735359 -#> log(DIS) 0.1145911 0.0958830 1.8969e-05 -#> log(RAD) 0.0012051 0.2395485 0.013406 -#> TAX 4.0037e-05 0.4428860 0.536041 -#> PTRATIO 0.0062878 0.1965749 0.010432 -#> B 5.1791e-07 0.1030411 0.556347 -#> log(LSTAT) < 2.22e-16 0.0755155 3.0733e-08 #data(house, package="spData") #lw <- spdep::nb2listw(LO_nb) #form <- formula(log(price) ~ age + I(age^2) + I(age^3) + log(lotsize) + @@ -858,7 +621,7 @@

    Examples

    #summary(mobj) #moobj <- impacts(mobj, tr=trMat, R=1000) #summary(moobj, zstats=TRUE, short=TRUE) -# } +}     @@ -873,7 +636,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/index.html b/docs/reference/index.html index a3d447d..4e93c3e 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -17,7 +17,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -184,7 +184,7 @@

    All functions
    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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z!$GH%r;o5C{#gm3*mf&Nw^D~8hv|I9ZuV;d59zS1^J?IuMUi|Q&X_F?oua~pKjC6C zTfb|Jmta4enTuE-+GihC<2+fM^9$}k~N?}`9 z&wz>r<*7LY_6pY;-{TEI(AvZ`@{gZM8-4z)9RK(8za^kM@`Of8BN8E=V>|QbIZ#qj KqU3{7!2biZ?ne~> diff --git a/docs/reference/invIrM.html b/docs/reference/invIrM.html index 80d0a2e..79aba82 100644 --- a/docs/reference/invIrM.html +++ b/docs/reference/invIrM.html @@ -17,7 +17,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -174,21 +174,13 @@

    Examples

    col=c("black", "brown", "green", "orange", "pink", "red"), lty=1, bty="n") } -# \dontrun{ +if (FALSE) { x <- matrix(rnorm(length(nb7rt)), ncol=1) system.time(e <- invIrM(nb7rt, rho=0.9, method="chol", feasible=TRUE) %*% x) -#> user system elapsed -#> 0.005 0.000 0.004 system.time(e <- invIrM(nb7rt, rho=0.9, method="chol", feasible=NULL) %*% x) -#> user system elapsed -#> 0.005 0.000 0.005 system.time(e <- invIrM(nb7rt, rho=0.9, method="solve", feasible=TRUE) %*% x) -#> user system elapsed -#> 0.002 0.000 0.002 system.time(e <- invIrM(nb7rt, rho=0.9, method="solve", feasible=NULL) %*% x) -#> user system elapsed -#> 0.003 0.000 0.002 -# } +}     @@ -203,7 +195,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/lagmess.html b/docs/reference/lagmess.html index 4e86d42..7a721cc 100644 --- a/docs/reference/lagmess.html +++ b/docs/reference/lagmess.html @@ -17,7 +17,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -86,7 +86,7 @@

    Arguments

    is called.

    listw
    -

    a listw object created for example by nb2listw

    +

    a listw object created for example by spdep::nb2listw()

    zero.policy

    default NULL, use global option value; if TRUE assign zero to the lagged value of zones without @@ -118,9 +118,9 @@

    Arguments

    Details

    The underlying spatial lag model:

    $$y = \rho W y + X \beta + \varepsilon$$

    -

    where \(\rho\) is the spatial parameter may be fitted by maximum likelihood. In that case, the log likelihood function includes the logartithm of cumbersome Jacobian term \(|I - \rho W|\). If we rewrite the model as:

    +

    where \(\rho\) is the spatial parameter may be fitted by maximum likelihood. In that case, the log likelihood function includes the logarithm of cumbersome Jacobian term \(|I - \rho W|\). If we rewrite the model as:

    $$S y = X \beta + \varepsilon$$

    -

    we see that in the ML case \(S y = (I - \rho W) y\). If W is row-stochastic, S may be expressed as a linear combination of row-stochastic matrices. By pre-computing the matrix \([y Wy, W^2y, ..., W^{q-1}y]\), the term \(S y (\alpha)\) can readily be found by numerical optimization using the matrix exponential approach. \(\alpha\) and \(\rho\) are related as \(\rho = 1 - \exp{\alpha}\), conditional on the number of matrix power terms taken q.

    +

    we see that in the ML case \(S y = (I - \rho W) y\). If W is row-stochastic, S may be expressed as a linear combination of row-stochastic matrices. By pre-computing the matrix \([y, Wy, W^2y, ..., W^{q-1}y]\), the term \(S y (\alpha)\) can readily be found by numerical optimization using the matrix exponential approach. \(\alpha\) and \(\rho\) are related as \(\rho = 1 - \exp{\alpha}\), conditional on the number of matrix power terms taken q.

    Value

    @@ -244,7 +244,7 @@

    Examples

    #> system.time(obj2 <- lagmess(log(PRICE) ~ PATIO + log(AGE) + log(SQFT), data=baltimore, listw=lw)) #> user system elapsed -#> 0.060 0.000 0.061 +#> 0.034 0.000 0.035 (x <- summary(obj2)) #> Matrix exponential spatial lag model: #> @@ -274,15 +274,22 @@

    Examples

    #> coef(x) #> Estimate Std. Error t value Pr(>|t|) -#> (Intercept) 1.5463761 0.21451319 7.208770 1.038762e-11 +#> (Intercept) 1.5463761 0.21451319 7.208769 1.038762e-11 #> PATIO 0.2582874 0.08689079 2.972552 3.303312e-03 #> log(AGE) -0.1481738 0.03525174 -4.203305 3.912250e-05 #> log(SQFT) 0.3009659 0.07159771 4.203569 3.908038e-05 -system.time(obj2a <- lagmess(log(PRICE) ~ PATIO + log(AGE) + log(SQFT), data=baltimore, listw=lw, - use_expm=TRUE)) -#> user system elapsed -#> 0.684 0.001 0.688 +has_expm <- require("expm", quietly=TRUE) +#> +#> Attaching package: ‘expm’ +#> The following object is masked from ‘package:Matrix’: +#> +#> expm +if (has_expm) { +system.time( +obj2a <- lagmess(log(PRICE) ~ PATIO + log(AGE) + log(SQFT), data=baltimore, listw=lw, use_expm=TRUE) +) summary(obj2a) +} #> Matrix exponential spatial lag model: #> (calculated with expm) #> @@ -337,7 +344,7 @@

    Examples

    #> ML residual variance (sigma squared): 0.1589, (sigma: 0.39862) #> Number of observations: 211 #> Number of parameters estimated: 6 -#> AIC: 232.85, (AIC for lm: 283.51) +#> AIC: NA (not available for weighted model), (AIC for lm: 283.51) #> LM test for residual autocorrelation #> test value: 8.7942, p-value: 0.0030219 #> @@ -364,7 +371,7 @@

    Examples

    #> CRIM -7.1045e-03 9.6236e-04 -7.3824 1.554e-13 #> ZN 3.7985e-04 3.8510e-04 0.9864 0.3239507 #> INDUS 1.2572e-03 1.7986e-03 0.6990 0.4845472 -#> CHAS1 7.3677e-03 2.5416e-02 0.2899 0.7719059 +#> CHAS1 7.3677e-03 2.5416e-02 0.2899 0.7719057 #> I(NOX^2) -2.6892e-01 8.8026e-02 -3.0550 0.0022508 #> I(RM^2) 6.7243e-03 1.0039e-03 6.6985 2.106e-11 #> AGE -2.7682e-04 4.0062e-04 -0.6910 0.4895829 @@ -384,7 +391,7 @@

    Examples

    #> ML residual variance (sigma squared): 0.019276, (sigma: 0.13884) #> Number of observations: 506 #> Number of parameters estimated: 16 -#> AIC: -496.02, (AIC for lm: -283.96) +#> AIC: NA (not available for weighted model), (AIC for lm: -283.96) #> LM test for residual autocorrelation #> test value: 10.74, p-value: 0.0010486 #> @@ -444,7 +451,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/lextrB.html b/docs/reference/lextrB.html index 17635f4..66dd4ea 100644 --- a/docs/reference/lextrB.html +++ b/docs/reference/lextrB.html @@ -17,7 +17,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -172,25 +172,22 @@

    Examples

    eigs(B, k=1, which="LR")$values } #> [1] 1 -# \dontrun{ +if (FALSE) { ab.listw <- spdep::nb2listw(boston.soi, style="S") er <- range(eigenw(similar.listw(ab.listw))) er -#> [1] -0.723495 1.110373 res_1 <- lextrS(ab.listw) c(res_1) -#> lambda_n lambda_1 -#> -0.7230376 1.1103694 -# } +} if (run) { B <- as(similar.listw(ab.listw), "CsparseMatrix") eigs(B, k=1, which="SR")$values } -#> [1] -0.723495 +#> [1] -0.9708644 if (run) { eigs(B, k=1, which="LR")$values } -#> [1] 1.110373 +#> [1] 1 @@ -205,7 +202,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/predict.sarlm.html b/docs/reference/predict.sarlm.html index 9b4fe07..5769d17 100644 --- a/docs/reference/predict.sarlm.html +++ b/docs/reference/predict.sarlm.html @@ -21,7 +21,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -227,108 +227,108 @@

    Examples

    type="mixed") print(p1 <- predict(COL.mix.eig)) #> This method assumes the response is known - see manual page -#> fit trend signal -#> 1001 26.044311 14.8543508 11.189960 -#> 1002 44.034234 29.2632112 14.771023 -#> 1003 43.511934 25.8193818 17.692553 -#> 1004 37.656561 16.4555583 21.201002 -#> 1005 10.902976 0.3664066 10.536570 -#> 1006 36.829798 24.2905246 12.539274 -#> 1007 44.290467 27.0386615 17.251806 -#> 1008 38.853571 21.5342393 17.319331 -#> 1009 50.870854 29.5092783 21.361576 -#> 1010 16.401300 5.6029104 10.798389 -#> 1011 36.354390 28.6415353 7.712855 -#> 1012 20.452836 12.4607277 7.992108 -#> 1013 20.324088 14.4173433 5.906745 -#> 1014 19.243496 10.2606419 8.982854 -#> 1015 19.747775 12.2556861 7.492089 -#> 1016 6.962527 -2.0137491 8.976276 -#> 1017 7.452143 -6.3808928 13.833036 -#> 1018 28.481587 14.2125594 14.269028 -#> 1019 43.351392 28.0442064 15.307186 -#> 1020 50.359682 30.6608153 19.698867 -#> 1021 38.905226 24.7490977 14.156128 -#> 1022 44.724478 28.8314299 15.893048 -#> 1023 37.888974 23.7778863 14.111087 -#> 1024 45.527017 26.9163190 18.610698 -#> 1025 32.429571 17.1892401 15.240331 -#> 1026 26.490842 14.8893980 11.601444 -#> 1027 35.629158 23.4577209 12.171437 -#> 1028 35.574326 21.9001006 13.674226 -#> 1029 38.598639 23.1818442 15.416795 -#> 1030 36.602053 14.8614072 21.740646 -#> 1031 50.320031 30.1013982 20.218633 -#> 1032 53.698863 31.2094168 22.489447 -#> 1033 49.364208 26.5151201 22.849088 -#> 1034 46.262357 25.5538226 20.708534 -#> 1035 39.177121 15.7689329 23.408188 -#> 1036 54.984344 32.6590841 22.325260 -#> 1037 51.611458 33.1290203 18.482438 -#> 1038 51.998831 30.7428313 21.256000 -#> 1039 43.651605 27.4107880 16.240817 -#> 1040 44.196841 25.9409252 18.255916 -#> 1041 49.310592 29.5106497 19.799943 -#> 1042 37.995310 15.7039024 22.291408 -#> 1043 46.908709 28.2687603 18.639948 -#> 1044 28.976789 19.5389223 9.437867 -#> 1045 25.343793 17.1838200 8.159973 -#> 1046 24.006252 16.0103703 7.995882 -#> 1047 25.034907 18.4616473 6.573260 -#> 1048 10.478529 3.3573578 7.121171 -#> 1049 13.495623 5.3356502 8.159973 +#> fit trend signal +#> 1001 26.044310 14.854350 11.189960 +#> 1002 44.034234 29.263210 14.771023 +#> 1003 43.511934 25.819381 17.692554 +#> 1004 37.656561 16.455557 21.201004 +#> 1005 10.902976 0.366406 10.536570 +#> 1006 36.829798 24.290524 12.539274 +#> 1007 44.290467 27.038660 17.251807 +#> 1008 38.853571 21.534238 17.319332 +#> 1009 50.870854 29.509277 21.361577 +#> 1010 16.401300 5.602910 10.798390 +#> 1011 36.354390 28.641535 7.712855 +#> 1012 20.452836 12.460727 7.992109 +#> 1013 20.324088 14.417343 5.906745 +#> 1014 19.243496 10.260642 8.982854 +#> 1015 19.747775 12.255686 7.492089 +#> 1016 6.962527 -2.013749 8.976277 +#> 1017 7.452143 -6.380893 13.833037 +#> 1018 28.481587 14.212559 14.269028 +#> 1019 43.351392 28.044206 15.307187 +#> 1020 50.359682 30.660814 19.698868 +#> 1021 38.905226 24.749097 14.156129 +#> 1022 44.724478 28.831429 15.893049 +#> 1023 37.888974 23.777886 14.111088 +#> 1024 45.527017 26.916318 18.610699 +#> 1025 32.429571 17.189239 15.240332 +#> 1026 26.490842 14.889397 11.601445 +#> 1027 35.629157 23.457720 12.171437 +#> 1028 35.574326 21.900100 13.674226 +#> 1029 38.598639 23.181843 15.416796 +#> 1030 36.602053 14.861406 21.740647 +#> 1031 50.320031 30.101397 20.218634 +#> 1032 53.698863 31.209416 22.489448 +#> 1033 49.364208 26.515119 22.849089 +#> 1034 46.262357 25.553822 20.708535 +#> 1035 39.177121 15.768932 23.408190 +#> 1036 54.984344 32.659083 22.325261 +#> 1037 51.611458 33.129019 18.482439 +#> 1038 51.998831 30.742830 21.256001 +#> 1039 43.651605 27.410787 16.240818 +#> 1040 44.196841 25.940924 18.255917 +#> 1041 49.310593 29.510649 19.799944 +#> 1042 37.995310 15.703901 22.291409 +#> 1043 46.908709 28.268759 18.639949 +#> 1044 28.976789 19.538922 9.437868 +#> 1045 25.343793 17.183819 8.159973 +#> 1046 24.006252 16.010370 7.995883 +#> 1047 25.034907 18.461647 6.573260 +#> 1048 10.478529 3.357357 7.121172 +#> 1049 13.495623 5.335650 8.159973 print(p2 <- predict(COL.mix.eig, newdata=COL.OLD, listw=lw, pred.type = "TS", legacy.mixed = TRUE)) -#> fit trend signal -#> 1001 29.038788 14.8543508 14.184437 -#> 1002 46.227075 29.2632112 16.963864 -#> 1003 45.640479 25.8193818 19.821097 -#> 1004 36.643520 16.4555583 20.187962 -#> 1005 14.819940 0.3664066 14.453533 -#> 1006 38.764777 24.2905246 14.474252 -#> 1007 45.715716 27.0386615 18.677055 -#> 1008 37.514611 21.5342393 15.980372 -#> 1009 49.324228 29.5092783 19.814950 -#> 1010 17.510607 5.6029104 11.907696 -#> 1011 34.973608 28.6415353 6.332072 -#> 1012 21.079100 12.4607277 8.618372 -#> 1013 19.704134 14.4173433 5.286791 -#> 1014 16.365521 10.2606419 6.104879 -#> 1015 17.063856 12.2556861 4.808170 -#> 1016 6.190282 -2.0137491 8.204031 -#> 1017 5.967260 -6.3808928 12.348153 -#> 1018 29.250462 14.2125594 15.037902 -#> 1019 41.530036 28.0442064 13.485830 -#> 1020 49.344770 30.6608153 18.683954 -#> 1021 39.508818 24.7490977 14.759720 -#> 1022 42.772692 28.8314299 13.941262 -#> 1023 37.114901 23.7778863 13.337015 -#> 1024 43.622499 26.9163190 16.706180 -#> 1025 33.247197 17.1892401 16.057957 -#> 1026 30.301331 14.8893980 15.411933 -#> 1027 38.316063 23.4577209 14.858342 -#> 1028 36.886068 21.9001006 14.985967 -#> 1029 38.970564 23.1818442 15.788720 -#> 1030 33.014615 14.8614072 18.153208 -#> 1031 48.209875 30.1013982 18.108477 -#> 1032 50.808064 31.2094168 19.598647 -#> 1033 44.555996 26.5151201 18.040876 -#> 1034 43.232773 25.5538226 17.678951 -#> 1035 35.009061 15.7689329 19.240128 -#> 1036 52.113364 32.6590841 19.454280 -#> 1037 52.189015 33.1290203 19.059995 -#> 1038 51.631805 30.7428313 20.888973 -#> 1039 46.543565 27.4107880 19.132776 -#> 1040 45.036095 25.9409252 19.095170 -#> 1041 45.907835 29.5106497 16.397185 -#> 1042 35.337110 15.7039024 19.633208 -#> 1043 43.948398 28.2687603 15.679638 -#> 1044 32.091257 19.5389223 12.552334 -#> 1045 29.647005 17.1838200 12.463185 -#> 1046 26.375304 16.0103703 10.364934 -#> 1047 27.235807 18.4616473 8.774160 -#> 1048 14.785518 3.3573578 11.428160 -#> 1049 17.798835 5.3356502 12.463185 +#> fit trend signal +#> 1001 29.038788 14.854350 14.184438 +#> 1002 46.227075 29.263210 16.963865 +#> 1003 45.640479 25.819381 19.821099 +#> 1004 36.643520 16.455557 20.187963 +#> 1005 14.819940 0.366406 14.453534 +#> 1006 38.764777 24.290524 14.474253 +#> 1007 45.715716 27.038660 18.677056 +#> 1008 37.514611 21.534238 15.980372 +#> 1009 49.324228 29.509277 19.814951 +#> 1010 17.510606 5.602910 11.907697 +#> 1011 34.973607 28.641535 6.332073 +#> 1012 21.079100 12.460727 8.618373 +#> 1013 19.704134 14.417343 5.286791 +#> 1014 16.365521 10.260642 6.104879 +#> 1015 17.063856 12.255686 4.808170 +#> 1016 6.190282 -2.013749 8.204032 +#> 1017 5.967260 -6.380893 12.348154 +#> 1018 29.250462 14.212559 15.037903 +#> 1019 41.530036 28.044206 13.485830 +#> 1020 49.344769 30.660814 18.683955 +#> 1021 39.508818 24.749097 14.759721 +#> 1022 42.772692 28.831429 13.941263 +#> 1023 37.114901 23.777886 13.337016 +#> 1024 43.622499 26.916318 16.706181 +#> 1025 33.247197 17.189239 16.057958 +#> 1026 30.301331 14.889397 15.411934 +#> 1027 38.316063 23.457720 14.858343 +#> 1028 36.886068 21.900100 14.985968 +#> 1029 38.970564 23.181843 15.788721 +#> 1030 33.014615 14.861406 18.153209 +#> 1031 48.209875 30.101397 18.108478 +#> 1032 50.808064 31.209416 19.598648 +#> 1033 44.555996 26.515119 18.040877 +#> 1034 43.232773 25.553822 17.678952 +#> 1035 35.009061 15.768932 19.240129 +#> 1036 52.113364 32.659083 19.454281 +#> 1037 52.189015 33.129019 19.059996 +#> 1038 51.631805 30.742830 20.888974 +#> 1039 46.543564 27.410787 19.132778 +#> 1040 45.036095 25.940924 19.095171 +#> 1041 45.907835 29.510649 16.397186 +#> 1042 35.337110 15.703901 19.633209 +#> 1043 43.948398 28.268759 15.679639 +#> 1044 32.091257 19.538922 12.552335 +#> 1045 29.647005 17.183819 12.463186 +#> 1046 26.375304 16.010370 10.364935 +#> 1047 27.235807 18.461647 8.774160 +#> 1048 14.785518 3.357357 11.428161 +#> 1049 17.798836 5.335650 12.463186 AIC(COL.mix.eig) #> [1] 376.787 sqrt(deviance(COL.mix.eig)/length(COL.nb)) @@ -377,15 +377,15 @@

    Examples

    p3 <- predict(COL.mix.eig, newdata=COL.OLD, listw=lw, pred.type = "TS", legacy=FALSE, legacy.mixed = TRUE) -all.equal(p2, p3, check.attributes=FALSE) +all.equal(p2, p3, check.attributes=FALSE) #> [1] TRUE p4 <- predict(COL.mix.eig, newdata=COL.OLD, listw=lw, pred.type = "TS", legacy=FALSE, power=TRUE, legacy.mixed = TRUE) -all.equal(p2, p4, check.attributes=FALSE) +all.equal(p2, p4, check.attributes=FALSE) #> [1] TRUE p5 <- predict(COL.mix.eig, newdata=COL.OLD, listw=lw, pred.type = "TS", legacy=TRUE, power=TRUE, legacy.mixed = TRUE) -all.equal(p2, p5, check.attributes=FALSE) +all.equal(p2, p5, check.attributes=FALSE) #> [1] TRUE @@ -401,7 +401,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/sarlm_tests.html b/docs/reference/sarlm_tests.html index c6817e2..17f50d2 100644 --- a/docs/reference/sarlm_tests.html +++ b/docs/reference/sarlm_tests.html @@ -17,7 +17,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -253,7 +253,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/set.mcOption.html b/docs/reference/set.mcOption.html index 69cb155..e9c25d7 100644 --- a/docs/reference/set.mcOption.html +++ b/docs/reference/set.mcOption.html @@ -17,7 +17,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -111,9 +111,9 @@

    Examples

    #> [1] "cluster" "cores" "mc" "rlecuyerSeed" "verbose" #> [6] "zeroPolicy" library(parallel) -nc <- detectCores(logical=FALSE) +nc <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L nc -#> [1] 4 +#> [1] 5 # set nc to 1L here if (nc > 1L) nc <- 1L #nc <- ifelse(nc > 2L, 2L, nc) @@ -126,7 +126,7 @@

    Examples

    if(.Platform$OS.type == "windows") { # forking not permitted on Windows - start cluster # removed for Github actions 210502 -# \dontrun{ +if (FALSE) { print(get.mcOption()) cl <- makeCluster(get.coresOption()) print(clusterEvalQ(cl, exists("mom_calc"))) @@ -137,7 +137,7 @@

    Examples

    set.ClusterOption(NULL) print(clusterEvalQ(cl, exists("mom_calc"))) stopCluster(cl) -# } +} } else { mcOpt <- get.mcOption() print(mcOpt) @@ -185,7 +185,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/set.spChkOption.html b/docs/reference/set.spChkOption.html index dda29f9..889f6b5 100644 --- a/docs/reference/set.spChkOption.html +++ b/docs/reference/set.spChkOption.html @@ -17,7 +17,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -117,7 +117,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/similar.listw.html b/docs/reference/similar.listw.html index 319bddc..7229b44 100644 --- a/docs/reference/similar.listw.html +++ b/docs/reference/similar.listw.html @@ -17,7 +17,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -132,7 +132,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/sparse_mat.html b/docs/reference/sparse_mat.html index aebbf3f..e31d897 100644 --- a/docs/reference/sparse_mat.html +++ b/docs/reference/sparse_mat.html @@ -17,7 +17,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -115,7 +115,7 @@

    See also

    Examples

    - 
    # \dontrun{
+    
    if (FALSE) {
 require(sf, quietly=TRUE)
 columbus <- st_read(system.file("shapes/columbus.shp", package="spData")[1], quiet=TRUE)
 #require(spdep, quietly=TRUE)
@@ -125,24 +125,6 @@ 
    Examples
    
   col.sp <- as.spam.listw(col.listw)
   str(col.sp)
 }
-#> Spam version 2.9-1 (2022-08-07) is loaded.
-#> Type 'help( Spam)' or 'demo( spam)' for a short introduction 
-#> and overview of this package.
-#> Help for individual functions is also obtained by adding the
-#> suffix '.spam' to the function name, e.g. 'help( chol.spam)'.
-#> 
-#> Attaching package: ‘spam’
-#> The following object is masked from ‘package:Matrix’:
-#> 
-#>     det
-#> The following objects are masked from ‘package:base’:
-#> 
-#>     backsolve, forwardsolve
-#> Formal class 'spam' [package "spam"] with 4 slots
-#>   ..@ entries    : num [1:230] 0.5 0.5 0.333 0.333 0.333 ...
-#>   ..@ colindices : int [1:230] 2 3 1 3 4 1 2 4 5 2 ...
-#>   ..@ rowpointers: int [1:50] 1 3 6 10 14 21 23 27 33 41 ...
-#>   ..@ dimension  : int [1:2] 49 49
 suppressMessages(nyadjmat <- as.matrix(foreign::read.dbf(system.file(
  "misc/nyadjwts.dbf", package="spData")[1])[-1]))
 nyadjlw <- spdep::mat2listw(nyadjmat)
@@ -154,14 +136,11 @@ 
    Examples
    
 I <- Diagonal(n)
 rho <- 0.1
 c(determinant(I - rho * W_S, logarithm=TRUE)$modulus)
-#> [1] -9.587255
 sum(log(1 - rho * eigenw(listw_NY)))
-#> [1] -9.587255
 nW <- - W_S
 nChol <- Cholesky(nW, Imult=8)
 n * log(rho) + (2 * c(determinant(update(nChol, nW, 1/rho))$modulus))
-#> [1] 99.8069
-# }
+}
 nb7rt <- spdep::cell2nb(7, 7, torus=TRUE)
 x <- matrix(sample(rnorm(500*length(nb7rt))), nrow=length(nb7rt))
 lw <- spdep::nb2listw(nb7rt)
@@ -179,46 +158,33 @@ 
    Examples
    
 W <- as(lw, "CsparseMatrix")
 system.time(e <- invIrM(nb7rt, rho=0.98, method="solve", feasible=NULL) %*% x)
 #>    user  system elapsed 
-#>   0.004   0.000   0.004 
+#>   0.003   0.000   0.003 
 system.time(ee <- powerWeights(W, rho=0.98, X=x))
 #> Warning: not converged within order iterations
 #>    user  system elapsed 
-#>   0.212   0.003   0.224 
+#>   0.185   0.004   0.189 
 str(attr(ee, "internal"))
 #> List of 5
-#>  $ series: num [1:250] 0.287 0.234 0.201 0.178 0.16 ...
+#>  $ series: num [1:250] 0.286 0.233 0.199 0.175 0.157 ...
 #>  $ order : num 250
 #>  $ tol   : num 4.05e-10
 #>  $ iter  : num 250
 #>  $ conv  : logi FALSE
-all.equal(e, as(ee, "matrix"), check.attributes=FALSE)
-#> [1] "Mean relative difference: 0.0060747"
-# \dontrun{
+all.equal(e, as(ee, "matrix"), check.attributes=FALSE)
+#> [1] "Mean relative difference: 0.00604604"
+if (FALSE) {
 system.time(ee <- powerWeights(W, rho=0.9, X=x))
-#>    user  system elapsed 
-#>   0.167   0.002   0.179 
 system.time(ee <- powerWeights(W, rho=0.98, order=1000, X=x))
-#>    user  system elapsed 
-#>   0.806   0.005   0.856 
-all.equal(e, as(ee, "matrix"), check.attributes=FALSE)
-#> [1] TRUE
+all.equal(e, as(ee, "matrix"), check.attributes=FALSE)
 nb60rt <- spdep::cell2nb(60, 60, torus=TRUE)
 W <- as(spdep::nb2listw(nb60rt), "CsparseMatrix")
 set.seed(1)
 x <- matrix(rnorm(dim(W)[1]), ncol=1)
 system.time(ee <- powerWeights(W, rho=0.3, X=x))
-#>    user  system elapsed 
-#>   0.021   0.000   0.022 
 str(as(ee, "matrix"))
-#>  num [1:3600, 1] -0.383 0.207 -0.731 1.552 0.32 ...
-#>  - attr(*, "dimnames")=List of 2
-#>   ..$ : chr [1:3600] "1:1" "2:1" "3:1" "4:1" ...
-#>   ..$ : NULL
 obj <- errorsarlm(as(ee, "matrix")[,1] ~ 1, listw=spdep::nb2listw(nb60rt), method="Matrix")
 coefficients(obj)
-#>      lambda (Intercept) 
-#>  0.30639880  0.01380415 
-# }
+}
    @@ -233,7 +199,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/spautolm.html b/docs/reference/spautolm.html index 614f4b5..c2b0db3 100644 --- a/docs/reference/spautolm.html +++ b/docs/reference/spautolm.html @@ -17,7 +17,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -329,55 +329,12 @@

    See also

    Examples

    require("sf", quietly=TRUE)
 nydata <- st_read(system.file("shapes/NY8_bna_utm18.gpkg", package="spData")[1], quiet=TRUE)
-# \dontrun{
+if (FALSE) {
 lm0 <- lm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata)
 summary(lm0)
-#> 
-#> Call:
-#> lm(formula = Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data = nydata)
-#> 
-#> Residuals:
-#>     Min      1Q  Median      3Q     Max 
-#> -1.7417 -0.3957 -0.0326  0.3353  4.1398 
-#> 
-#> Coefficients:
-#>             Estimate Std. Error t value Pr(>|t|)    
-#> (Intercept) -0.51728    0.15856  -3.262  0.00124 ** 
-#> PEXPOSURE    0.04884    0.03506   1.393  0.16480    
-#> PCTAGE65P    3.95089    0.60550   6.525 3.22e-10 ***
-#> PCTOWNHOME  -0.56004    0.17031  -3.288  0.00114 ** 
-#> ---
-#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-#> 
-#> Residual standard error: 0.6571 on 277 degrees of freedom
-#> Multiple R-squared:  0.1932,	Adjusted R-squared:  0.1844 
-#> F-statistic:  22.1 on 3 and 277 DF,  p-value: 7.306e-13
-#> 
 lm0w <- lm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata, weights=POP8)
 summary(lm0w)
-#> 
-#> Call:
-#> lm(formula = Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data = nydata, 
-#>     weights = POP8)
-#> 
-#> Weighted Residuals:
-#>      Min       1Q   Median       3Q      Max 
-#> -129.067  -14.714    5.817   25.624   70.723 
-#> 
-#> Coefficients:
-#>             Estimate Std. Error t value Pr(>|t|)    
-#> (Intercept) -0.77837    0.14116  -5.514 8.03e-08 ***
-#> PEXPOSURE    0.07626    0.02731   2.792  0.00560 ** 
-#> PCTAGE65P    3.85656    0.57126   6.751 8.60e-11 ***
-#> PCTOWNHOME  -0.39869    0.15305  -2.605  0.00968 ** 
-#> ---
-#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-#> 
-#> Residual standard error: 33.5 on 277 degrees of freedom
-#> Multiple R-squared:  0.1977,	Adjusted R-squared:  0.189 
-#> F-statistic: 22.75 on 3 and 277 DF,  p-value: 3.382e-13
-#> 
-# }
+}
 suppressMessages(nyadjmat <- as.matrix(foreign::read.dbf(system.file(
  "misc/nyadjwts.dbf", package="spData")[1])[-1]))
 suppressMessages(ID <- as.character(names(foreign::read.dbf(system.file(
@@ -386,263 +343,43 @@ 
    Examples
    
 #> [1] TRUE
 #require("spdep", quietly=TRUE)
 nyadjlw <- spdep::mat2listw(nyadjmat, as.character(nydata$AREAKEY))
+#> Warning: style is M (missing); style should be set to a valid value
 listw_NY <- spdep::nb2listw(nyadjlw$neighbours, style="B")
 eigs <- eigenw(listw_NY)
-# \dontrun{
+if (FALSE) {
 esar0 <- errorsarlm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata,
  listw=listw_NY)
 summary(esar0)
-#> 
-#> Call:errorsarlm(formula = Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, 
-#>     data = nydata, listw = listw_NY)
-#> 
-#> Residuals:
-#>      Min       1Q   Median       3Q      Max 
-#> -1.56754 -0.38239 -0.02643  0.33109  4.01219 
-#> 
-#> Type: error 
-#> Coefficients: (asymptotic standard errors) 
-#>              Estimate Std. Error z value  Pr(>|z|)
-#> (Intercept) -0.618193   0.176784 -3.4969 0.0004707
-#> PEXPOSURE    0.071014   0.042051  1.6888 0.0912635
-#> PCTAGE65P    3.754200   0.624722  6.0094 1.862e-09
-#> PCTOWNHOME  -0.419890   0.191329 -2.1946 0.0281930
-#> 
-#> Lambda: 0.040487, LR test value: 5.2438, p-value: 0.022026
-#> Asymptotic standard error: 0.016214
-#>     z-value: 2.4971, p-value: 0.01252
-#> Wald statistic: 6.2356, p-value: 0.01252
-#> 
-#> Log likelihood: -276.1069 for error model
-#> ML residual variance (sigma squared): 0.41388, (sigma: 0.64333)
-#> Number of observations: 281 
-#> Number of parameters estimated: 6 
-#> AIC: 564.21, (AIC for lm: 567.46)
-#> 
 system.time(esar1f <- spautolm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME,
  data=nydata, listw=listw_NY, family="SAR", method="eigen",
  control=list(pre_eig=eigs)))
-#>    user  system elapsed 
-#>   0.272   0.000   0.274 
 res <- summary(esar1f)
 print(res)
-#> 
-#> Call: 
-#> spautolm(formula = Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data = nydata, 
-#>     listw = listw_NY, family = "SAR", method = "eigen", control = list(pre_eig = eigs))
-#> 
-#> Residuals:
-#>      Min       1Q   Median       3Q      Max 
-#> -1.56754 -0.38239 -0.02643  0.33109  4.01219 
-#> 
-#> Coefficients: 
-#>              Estimate Std. Error z value  Pr(>|z|)
-#> (Intercept) -0.618193   0.176784 -3.4969 0.0004707
-#> PEXPOSURE    0.071014   0.042051  1.6888 0.0912635
-#> PCTAGE65P    3.754200   0.624722  6.0094 1.862e-09
-#> PCTOWNHOME  -0.419890   0.191329 -2.1946 0.0281930
-#> 
-#> Lambda: 0.040487 LR test value: 5.2438 p-value: 0.022026 
-#> Numerical Hessian standard error of lambda: 0.017209 
-#> 
-#> Log likelihood: -276.1069 
-#> ML residual variance (sigma squared): 0.41388, (sigma: 0.64333)
-#> Number of observations: 281 
-#> Number of parameters estimated: 6 
-#> AIC: 564.21
-#> 
 coef(res)
-#>                Estimate Std. Error   z value     Pr(>|z|)
-#> (Intercept) -0.61819272 0.17678351 -3.496891 4.707136e-04
-#> PEXPOSURE    0.07101384 0.04205063  1.688770 9.126351e-02
-#> PCTAGE65P    3.75419997 0.62472153  6.009397 1.862141e-09
-#> PCTOWNHOME  -0.41988961 0.19132936 -2.194591 2.819298e-02
 sqrt(diag(res$resvar))
-#> (Intercept)   PEXPOSURE   PCTAGE65P  PCTOWNHOME 
-#>  0.17678351  0.04205063  0.62472153  0.19132936 
 sqrt(diag(esar1f$fit$imat)*esar1f$fit$s2)
-#> (Intercept)   PEXPOSURE   PCTAGE65P  PCTOWNHOME 
-#>  0.17678351  0.04205063  0.62472153  0.19132936 
 sqrt(diag(esar1f$fdHess))
-#> [1] 0.01720868 0.18535631 0.04389387 0.63003835 0.20373413
 system.time(esar1M <- spautolm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME,
  data=nydata, listw=listw_NY, family="SAR", method="Matrix"))
-#>    user  system elapsed 
-#>   0.323   0.000   0.325 
 summary(esar1M)
-#> 
-#> Call: 
-#> spautolm(formula = Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data = nydata, 
-#>     listw = listw_NY, family = "SAR", method = "Matrix")
-#> 
-#> Residuals:
-#>       Min        1Q    Median        3Q       Max 
-#> -3.406132 -0.561646 -0.092662  0.474796  5.384405 
-#> 
-#> Coefficients: 
-#>              Estimate Std. Error z value  Pr(>|z|)
-#> (Intercept) -0.414826   0.102166 -4.0603 4.901e-05
-#> PEXPOSURE    0.015081   0.017772  0.8486    0.3961
-#> PCTAGE65P    5.159749   0.476498 10.8285 < 2.2e-16
-#> PCTOWNHOME  -0.892387   0.099241 -8.9921 < 2.2e-16
-#> 
-#> Lambda: -0.38889 LR test value: 254.73 p-value: < 2.22e-16 
-#> Numerical Hessian standard error of lambda: 0.044857 
-#> 
-#> Log likelihood: -151.3662 
-#> ML residual variance (sigma squared): 0.95111, (sigma: 0.97525)
-#> Number of observations: 281 
-#> Number of parameters estimated: 6 
-#> AIC: 314.73
-#> 
 system.time(esar1M <- spautolm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME,
  data=nydata, listw=listw_NY, family="SAR", method="Matrix",
  control=list(super=TRUE)))
-#>    user  system elapsed 
-#>   0.282   0.000   0.283 
 summary(esar1M)
-#> 
-#> Call: 
-#> spautolm(formula = Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data = nydata, 
-#>     listw = listw_NY, family = "SAR", method = "Matrix", control = list(super = TRUE))
-#> 
-#> Residuals:
-#>       Min        1Q    Median        3Q       Max 
-#> -3.178535 -0.521860 -0.074421  0.414212  5.184465 
-#> 
-#> Coefficients: 
-#>              Estimate Std. Error z value  Pr(>|z|)
-#> (Intercept) -0.411041   0.104426 -3.9362 8.279e-05
-#> PEXPOSURE    0.015768   0.018366  0.8585    0.3906
-#> PCTAGE65P    5.070130   0.483332 10.4900 < 2.2e-16
-#> PCTOWNHOME  -0.880883   0.102093 -8.6282 < 2.2e-16
-#> 
-#> Lambda: -0.33146 LR test value: 247.44 p-value: < 2.22e-16 
-#> Numerical Hessian standard error of lambda: 0.030659 
-#> 
-#> Log likelihood: -155.0108 
-#> ML residual variance (sigma squared): 0.82436, (sigma: 0.90795)
-#> Number of observations: 281 
-#> Number of parameters estimated: 6 
-#> AIC: 322.02
-#> 
 esar1wf <- spautolm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata,
  listw=listw_NY, weights=POP8, family="SAR", method="eigen",
  control=list(pre_eig=eigs))
 summary(esar1wf)
-#> 
-#> Call: 
-#> spautolm(formula = Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data = nydata, 
-#>     listw = listw_NY, weights = POP8, family = "SAR", method = "eigen", 
-#>     control = list(pre_eig = eigs))
-#> 
-#> Residuals:
-#>      Min       1Q   Median       3Q      Max 
-#> -1.48488 -0.26823  0.09489  0.46552  4.28343 
-#> 
-#> Coefficients: 
-#>              Estimate Std. Error z value  Pr(>|z|)
-#> (Intercept) -0.797063   0.144054 -5.5331 3.146e-08
-#> PEXPOSURE    0.080545   0.028334  2.8428  0.004473
-#> PCTAGE65P    3.816731   0.576037  6.6258 3.453e-11
-#> PCTOWNHOME  -0.380778   0.156507 -2.4330  0.014975
-#> 
-#> Lambda: 0.0095636 LR test value: 0.32665 p-value: 0.56764 
-#> Numerical Hessian standard error of lambda: 0.016466 
-#> 
-#> Log likelihood: -251.6017 
-#> ML residual variance (sigma squared): 1104.1, (sigma: 33.229)
-#> Number of observations: 281 
-#> Number of parameters estimated: 6 
-#> AIC: 515.2
-#> 
 system.time(esar1wM <- spautolm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME,
  data=nydata, listw=listw_NY, weights=POP8, family="SAR", method="Matrix"))
-#>    user  system elapsed 
-#>   0.320   0.000   0.322 
 summary(esar1wM)
-#> 
-#> Call: 
-#> spautolm(formula = Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data = nydata, 
-#>     listw = listw_NY, weights = POP8, family = "SAR", method = "Matrix")
-#> 
-#> Residuals:
-#>       Min        1Q    Median        3Q       Max 
-#> -2.561100 -0.374524  0.057405  0.591094  5.700142 
-#> 
-#> Coefficients: 
-#>              Estimate Std. Error z value  Pr(>|z|)
-#> (Intercept) -0.578546   0.090006 -6.4279 1.294e-10
-#> PEXPOSURE    0.035402   0.013959  2.5361   0.01121
-#> PCTAGE65P    4.651137   0.421285 11.0404 < 2.2e-16
-#> PCTOWNHOME  -0.666898   0.091443 -7.2931 3.029e-13
-#> 
-#> Lambda: -0.34423 LR test value: 264.24 p-value: < 2.22e-16 
-#> Numerical Hessian standard error of lambda: 0.036776 
-#> 
-#> Log likelihood: -119.6468 
-#> ML residual variance (sigma squared): 2129.1, (sigma: 46.142)
-#> Number of observations: 281 
-#> Number of parameters estimated: 6 
-#> AIC: 251.29
-#> 
 esar1wlu <- spautolm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata,
  listw=listw_NY, weights=POP8, family="SAR", method="LU")
 summary(esar1wlu)
-#> 
-#> Call: 
-#> spautolm(formula = Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data = nydata, 
-#>     listw = listw_NY, weights = POP8, family = "SAR", method = "LU")
-#> 
-#> Residuals:
-#>      Min       1Q   Median       3Q      Max 
-#> -1.48488 -0.26823  0.09489  0.46552  4.28343 
-#> 
-#> Coefficients: 
-#>              Estimate Std. Error z value  Pr(>|z|)
-#> (Intercept) -0.797063   0.144054 -5.5331 3.146e-08
-#> PEXPOSURE    0.080545   0.028334  2.8428  0.004473
-#> PCTAGE65P    3.816731   0.576037  6.6258 3.453e-11
-#> PCTOWNHOME  -0.380778   0.156507 -2.4330  0.014975
-#> 
-#> Lambda: 0.0095636 LR test value: 0.32665 p-value: 0.56764 
-#> Numerical Hessian standard error of lambda: 0.016522 
-#> 
-#> Log likelihood: -251.6017 
-#> ML residual variance (sigma squared): 1104.1, (sigma: 33.229)
-#> Number of observations: 281 
-#> Number of parameters estimated: 6 
-#> AIC: 515.2
-#> 
 esar1wch <- spautolm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata,
  listw=listw_NY, weights=POP8, family="SAR", method="Chebyshev")
 summary(esar1wch)
-#> 
-#> Call: 
-#> spautolm(formula = Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data = nydata, 
-#>     listw = listw_NY, weights = POP8, family = "SAR", method = "Chebyshev")
-#> 
-#> Residuals:
-#>      Min       1Q   Median       3Q      Max 
-#> -4.39831 -0.86303  0.12117  1.09320  8.78570 
-#> 
-#> Coefficients: 
-#>              Estimate Std. Error z value  Pr(>|z|)
-#> (Intercept) -0.538555   0.087573 -6.1498  7.76e-10
-#> PEXPOSURE    0.029782   0.013074  2.2780   0.02273
-#> PCTAGE65P    4.896346   0.419359 11.6758 < 2.2e-16
-#> PCTOWNHOME  -0.737829   0.087569 -8.4257 < 2.2e-16
-#> 
-#> Lambda: -1 LR test value: 236970 p-value: < 2.22e-16 
-#> Numerical Hessian standard error of lambda: NaN 
-#> 
-#> Log likelihood: 118232.5 
-#> ML residual variance (sigma squared): 9336.4, (sigma: 96.625)
-#> Number of observations: 281 
-#> Number of parameters estimated: 6 
-#> AIC: -236450
-#> 
-# }
+}
 ecar1f <- spautolm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata,
  listw=listw_NY, family="CAR", method="eigen",
  control=list(pre_eig=eigs))
@@ -664,7 +401,7 @@ 
    Examples
    
 #> PCTOWNHOME  -0.382789   0.195564 -1.9574 0.0503053
 #> 
 #> Lambda: 0.084123 LR test value: 5.8009 p-value: 0.016018 
-#> Numerical Hessian standard error of lambda: 0.030868 
+#> Numerical Hessian standard error of lambda: 0.030872 
 #> 
 #> Log likelihood: -275.8283 
 #> ML residual variance (sigma squared): 0.40758, (sigma: 0.63842)
@@ -672,38 +409,11 @@ 
    Examples
    
 #> Number of parameters estimated: 6 
 #> AIC: 563.66
 #> 
-# \dontrun{
+if (FALSE) {
 system.time(ecar1M <- spautolm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME,
  data=nydata, listw=listw_NY, family="CAR", method="Matrix"))
-#>    user  system elapsed 
-#>   0.349   0.000   0.350 
 summary(ecar1M)
-#> 
-#> Call: 
-#> spautolm(formula = Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data = nydata, 
-#>     listw = listw_NY, family = "CAR", method = "Matrix")
-#> 
-#> Residuals:
-#>       Min        1Q    Median        3Q       Max 
-#> -3.449951 -0.633777 -0.072436  0.550248  6.039594 
-#> 
-#> Coefficients: 
-#>              Estimate Std. Error z value  Pr(>|z|)
-#> (Intercept) -0.402788   0.112612 -3.5768 0.0003479
-#> PEXPOSURE    0.020131   0.020783  0.9686 0.3327222
-#> PCTAGE65P    4.632644   0.500526  9.2555 < 2.2e-16
-#> PCTOWNHOME  -0.812981   0.112867 -7.2030 5.891e-13
-#> 
-#> Lambda: -0.5 LR test value: 228.48 p-value: < 2.22e-16 
-#> Numerical Hessian standard error of lambda: NaN 
-#> 
-#> Log likelihood: -164.4897 
-#> ML residual variance (sigma squared): 0.50386, (sigma: 0.70983)
-#> Number of observations: 281 
-#> Number of parameters estimated: 6 
-#> AIC: 340.98
-#> 
-# }
+}
 ecar1wf <- spautolm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata,
  listw=listw_NY, weights=POP8, family="CAR", method="eigen",
  control=list(pre_eig=eigs))
@@ -726,47 +436,20 @@ 
    Examples
    
 #> PCTOWNHOME  -0.386820   0.157436 -2.4570  0.014010
 #> 
 #> Lambda: 0.022419 LR test value: 0.38785 p-value: 0.53343 
-#> Numerical Hessian standard error of lambda: 0.038543 
+#> Numerical Hessian standard error of lambda: 0.038977 
 #> 
 #> Log likelihood: -251.5711 
 #> ML residual variance (sigma squared): 1102.9, (sigma: 33.21)
 #> Number of observations: 281 
 #> Number of parameters estimated: 6 
-#> AIC: 515.14
+#> AIC: NA (not available for weighted model)
 #> 
-# \dontrun{
+if (FALSE) {
 system.time(ecar1wM <- spautolm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME,
  data=nydata, listw=listw_NY, weights=POP8, family="CAR", method="Matrix"))
-#>    user  system elapsed 
-#>   0.342   0.000   0.344 
 summary(ecar1wM)
-#> 
-#> Call: 
-#> spautolm(formula = Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data = nydata, 
-#>     listw = listw_NY, weights = POP8, family = "CAR", method = "Matrix")
-#> 
-#> Residuals:
-#>      Min       1Q   Median       3Q      Max 
-#> -1.98144 -0.15716  0.37342  1.08857  7.10495 
-#> 
-#> Coefficients: 
-#>              Estimate Std. Error z value  Pr(>|z|)
-#> (Intercept) -0.714952   0.093905 -7.6136 2.665e-14
-#> PEXPOSURE    0.041467   0.015279  2.7139   0.00665
-#> PCTAGE65P    4.149207   0.425446  9.7526 < 2.2e-16
-#> PCTOWNHOME  -0.478396   0.097030 -4.9304 8.205e-07
-#> 
-#> Lambda: -0.5 LR test value: 262.65 p-value: < 2.22e-16 
-#> Numerical Hessian standard error of lambda: NaN 
-#> 
-#> Log likelihood: -120.4395 
-#> ML residual variance (sigma squared): 1159.2, (sigma: 34.046)
-#> Number of observations: 281 
-#> Number of parameters estimated: 6 
-#> AIC: 252.88
-#> 
-# }
-# \dontrun{
+}
+if (FALSE) {
 require("sf", quietly=TRUE)
 nc.sids <- st_read(system.file("shapes/sids.shp", package="spData")[1], quiet=TRUE)
 ft.SID74 <- sqrt(1000)*(sqrt(nc.sids$SID74/nc.sids$BIR74) +
@@ -777,21 +460,18 @@ 
    Examples
    
 sids.nhbr30.dist <- spdep::nbdists(sids.nhbr30, cbind(nc.sids$east, nc.sids$north))
 sids.nhbr <- spdep::listw2sn(spdep::nb2listw(sids.nhbr30,
  glist=sids.nhbr30.dist, style="B", zero.policy=TRUE))
-#> Warning: zero sum general weights
 dij <- sids.nhbr[,3]
 n <- nc.sids$BIR74
 el1 <- min(dij)/dij
 el2 <- sqrt(n[sids.nhbr$to]/n[sids.nhbr$from])
 sids.nhbr$weights <- el1*el2
 sids.nhbr.listw <- spdep::sn2listw(sids.nhbr)
-#> Warning: 56, 87 are not origins
 both <- factor(paste(nc.sids$L_id, nc.sids$M_id, sep=":"))
 ft.NWBIR74 <- sqrt(1000)*(sqrt(nc.sids$NWBIR74/nc.sids$BIR74) +
  sqrt((nc.sids$NWBIR74+1)/nc.sids$BIR74))
 mdata <- data.frame(both, ft.NWBIR74, ft.SID74, BIR74=nc.sids$BIR74)
 outl <- which.max(rstandard(lm_nc))
 as.character(nc.sids$NAME[outl])
-#> [1] "Anson"
 mdata.4 <- mdata[-outl,]
 W <- spdep::listw2mat(sids.nhbr.listw)
 W.4 <- W[-outl, -outl]
@@ -799,395 +479,50 @@ 
    Examples
    
 esarI <- errorsarlm(ft.SID74 ~ 1, data=mdata, listw=sids.nhbr.listw,
  zero.policy=TRUE)
 summary(esarI)
-#> 
-#> Call:errorsarlm(formula = ft.SID74 ~ 1, data = mdata, listw = sids.nhbr.listw, 
-#>     zero.policy = TRUE)
-#> 
-#> Residuals:
-#>       Min        1Q    Median        3Q       Max 
-#> -1.887117 -0.636573 -0.043429  0.448767  3.406724 
-#> 
-#> Type: error 
-#> Regions with no neighbours included:
-#>  56 87 
-#> Coefficients: (asymptotic standard errors) 
-#>             Estimate Std. Error z value  Pr(>|z|)
-#> (Intercept)  2.97463    0.13011  22.862 < 2.2e-16
-#> 
-#> Lambda: 0.66864, LR test value: 10.214, p-value: 0.0013939
-#> Asymptotic standard error: 0.11473
-#>     z-value: 5.8278, p-value: 5.6147e-09
-#> Wald statistic: 33.964, p-value: 5.6147e-09
-#> 
-#> Log likelihood: -133.8616 for error model
-#> ML residual variance (sigma squared): 0.81932, (sigma: 0.90516)
-#> Number of observations: 100 
-#> Number of parameters estimated: 3 
-#> AIC: 273.72, (AIC for lm: 281.94)
-#> 
 esarIa <- spautolm(ft.SID74 ~ 1, data=mdata, listw=sids.nhbr.listw,
  family="SAR")
 summary(esarIa)
-#> 
-#> Call: spautolm(formula = ft.SID74 ~ 1, data = mdata, listw = sids.nhbr.listw, 
-#>     family = "SAR")
-#> 
-#> Residuals:
-#>       Min        1Q    Median        3Q       Max 
-#> -1.887117 -0.636573 -0.043429  0.448767  3.406724 
-#> 
-#> Coefficients: 
-#>             Estimate Std. Error z value  Pr(>|z|)
-#> (Intercept)  2.97463    0.13011  22.862 < 2.2e-16
-#> 
-#> Lambda: 0.66864 LR test value: 10.214 p-value: 0.0013939 
-#> Numerical Hessian standard error of lambda: 0.16506 
-#> 
-#> Log likelihood: -133.8616 
-#> ML residual variance (sigma squared): 0.81932, (sigma: 0.90516)
-#> Number of observations: 100 
-#> Number of parameters estimated: 3 
-#> AIC: 273.72
-#> 
 esarIV <- errorsarlm(ft.SID74 ~ ft.NWBIR74, data=mdata, listw=sids.nhbr.listw,
  zero.policy=TRUE)
 summary(esarIV)
-#> 
-#> Call:
-#> errorsarlm(formula = ft.SID74 ~ ft.NWBIR74, data = mdata, listw = sids.nhbr.listw, 
-#>     zero.policy = TRUE)
-#> 
-#> Residuals:
-#>       Min        1Q    Median        3Q       Max 
-#> -2.123648 -0.573163  0.017859  0.468022  2.693604 
-#> 
-#> Type: error 
-#> Regions with no neighbours included:
-#>  56 87 
-#> Coefficients: (asymptotic standard errors) 
-#>             Estimate Std. Error z value  Pr(>|z|)
-#> (Intercept) 1.549443   0.219230  7.0677 1.576e-12
-#> ft.NWBIR74  0.041974   0.006171  6.8018 1.033e-11
-#> 
-#> Lambda: 0.18465, LR test value: 0.50496, p-value: 0.47733
-#> Asymptotic standard error: 0.20648
-#>     z-value: 0.89424, p-value: 0.37119
-#> Wald statistic: 0.79967, p-value: 0.37119
-#> 
-#> Log likelihood: -117.7464 for error model
-#> ML residual variance (sigma squared): 0.61546, (sigma: 0.78451)
-#> Number of observations: 100 
-#> Number of parameters estimated: 4 
-#> AIC: 243.49, (AIC for lm: 242)
-#> 
 esarIVa <- spautolm(ft.SID74 ~ ft.NWBIR74, data=mdata, listw=sids.nhbr.listw,
  family="SAR")
 summary(esarIVa)
-#> 
-#> Call: 
-#> spautolm(formula = ft.SID74 ~ ft.NWBIR74, data = mdata, listw = sids.nhbr.listw, 
-#>     family = "SAR")
-#> 
-#> Residuals:
-#>       Min        1Q    Median        3Q       Max 
-#> -2.123648 -0.573163  0.017859  0.468022  2.693604 
-#> 
-#> Coefficients: 
-#>             Estimate Std. Error z value  Pr(>|z|)
-#> (Intercept) 1.549443   0.219230  7.0677 1.576e-12
-#> ft.NWBIR74  0.041974   0.006171  6.8018 1.033e-11
-#> 
-#> Lambda: 0.18465 LR test value: 0.50496 p-value: 0.47733 
-#> Numerical Hessian standard error of lambda: 0.25591 
-#> 
-#> Log likelihood: -117.7464 
-#> ML residual variance (sigma squared): 0.61546, (sigma: 0.78451)
-#> Number of observations: 100 
-#> Number of parameters estimated: 4 
-#> AIC: 243.49
-#> 
 esarIaw <- spautolm(ft.SID74 ~ 1, data=mdata, listw=sids.nhbr.listw,
  weights=BIR74, family="SAR")
 summary(esarIaw)
-#> 
-#> Call: spautolm(formula = ft.SID74 ~ 1, data = mdata, listw = sids.nhbr.listw, 
-#>     weights = BIR74, family = "SAR")
-#> 
-#> Residuals:
-#>       Min        1Q    Median        3Q       Max 
-#> -1.867485 -0.568644  0.019717  0.502197  3.498013 
-#> 
-#> Coefficients: 
-#>             Estimate Std. Error z value  Pr(>|z|)
-#> (Intercept) 2.852052   0.090271  31.594 < 2.2e-16
-#> 
-#> Lambda: 0.7338 LR test value: 12.917 p-value: 0.00032554 
-#> Numerical Hessian standard error of lambda: 0.13887 
-#> 
-#> Log likelihood: -130.0975 
-#> ML residual variance (sigma squared): 1539.4, (sigma: 39.236)
-#> Number of observations: 100 
-#> Number of parameters estimated: 3 
-#> AIC: 266.19
-#> 
 esarIIaw <- spautolm(ft.SID74 ~ both - 1, data=mdata, listw=sids.nhbr.listw,
  weights=BIR74, family="SAR")
 summary(esarIIaw)
-#> 
-#> Call: 
-#> spautolm(formula = ft.SID74 ~ both - 1, data = mdata, listw = sids.nhbr.listw, 
-#>     weights = BIR74, family = "SAR")
-#> 
-#> Residuals:
-#>       Min        1Q    Median        3Q       Max 
-#> -2.590809 -0.432976  0.016736  0.357284  3.536718 
-#> 
-#> Coefficients: 
-#>         Estimate Std. Error z value  Pr(>|z|)
-#> both1:2  2.05545    0.22184  9.2654 < 2.2e-16
-#> both1:3  2.87260    0.16181 17.7531 < 2.2e-16
-#> both1:4  4.16365    0.34330 12.1283 < 2.2e-16
-#> both2:1  2.47255    0.29757  8.3090 < 2.2e-16
-#> both2:2  2.15307    0.21172 10.1692 < 2.2e-16
-#> both2:3  2.64235    0.17296 15.2770 < 2.2e-16
-#> both2:4  3.26604    0.28287 11.5459 < 2.2e-16
-#> both3:1  3.11277    0.34166  9.1107 < 2.2e-16
-#> both3:2  2.76541    0.15667 17.6508 < 2.2e-16
-#> both3:3  2.86582    0.18593 15.4134 < 2.2e-16
-#> both3:4  3.18142    0.21617 14.7169 < 2.2e-16
-#> both4:3  3.69333    0.23348 15.8188 < 2.2e-16
-#> 
-#> Lambda: 0.32136 LR test value: 1.4004 p-value: 0.23666 
-#> Numerical Hessian standard error of lambda: 0.25465 
-#> 
-#> Log likelihood: -109.8922 
-#> ML residual variance (sigma squared): 1071.6, (sigma: 32.735)
-#> Number of observations: 100 
-#> Number of parameters estimated: 14 
-#> AIC: 247.78
-#> 
 esarIVaw <- spautolm(ft.SID74 ~ ft.NWBIR74, data=mdata,
  listw=sids.nhbr.listw, weights=BIR74, family="SAR")
 summary(esarIVaw)
-#> 
-#> Call: 
-#> spautolm(formula = ft.SID74 ~ ft.NWBIR74, data = mdata, listw = sids.nhbr.listw, 
-#>     weights = BIR74, family = "SAR")
-#> 
-#> Residuals:
-#>      Min       1Q   Median       3Q      Max 
-#> -2.00956 -0.45229  0.12547  0.55952  2.92223 
-#> 
-#> Coefficients: 
-#>              Estimate Std. Error z value  Pr(>|z|)
-#> (Intercept) 1.5769279  0.2501334  6.3043 2.894e-10
-#> ft.NWBIR74  0.0368573  0.0069413  5.3099 1.097e-07
-#> 
-#> Lambda: 0.3839 LR test value: 1.9983 p-value: 0.15747 
-#> Numerical Hessian standard error of lambda: 0.25769 
-#> 
-#> Log likelihood: -119.5648 
-#> ML residual variance (sigma squared): 1295.8, (sigma: 35.997)
-#> Number of observations: 100 
-#> Number of parameters estimated: 4 
-#> AIC: 247.13
-#> 
 ecarIaw <- spautolm(ft.SID74 ~ 1, data=mdata.4, listw=sids.nhbr.listw.4,
  weights=BIR74, family="CAR")
-#> Warning: Non-symmetric spatial weights in CAR model
 summary(ecarIaw)
-#> 
-#> Call: 
-#> spautolm(formula = ft.SID74 ~ 1, data = mdata.4, listw = sids.nhbr.listw.4, 
-#>     weights = BIR74, family = "CAR")
-#> 
-#> Residuals:
-#>       Min        1Q    Median        3Q       Max 
-#> -2.009350 -0.638915 -0.060761  0.428526  2.019409 
-#> 
-#> Coefficients: 
-#>             Estimate Std. Error z value  Pr(>|z|)
-#> (Intercept) 2.942864   0.095304  30.879 < 2.2e-16
-#> 
-#> Lambda: 0.86832 LR test value: 23.003 p-value: 1.6172e-06 
-#> Numerical Hessian standard error of lambda: 0.048102 
-#> 
-#> Log likelihood: -118.7564 
-#> ML residual variance (sigma squared): 1264, (sigma: 35.553)
-#> Number of observations: 99 
-#> Number of parameters estimated: 3 
-#> AIC: 243.51
-#> 
 ecarIIaw <- spautolm(ft.SID74 ~ both - 1, data=mdata.4,
  listw=sids.nhbr.listw.4, weights=BIR74, family="CAR")
-#> Warning: Non-symmetric spatial weights in CAR model
-#> Warning: NaNs produced
 summary(ecarIIaw)
-#> 
-#> Call: 
-#> spautolm(formula = ft.SID74 ~ both - 1, data = mdata.4, listw = sids.nhbr.listw.4, 
-#>     weights = BIR74, family = "CAR")
-#> 
-#> Residuals:
-#>       Min        1Q    Median        3Q       Max 
-#> -2.564067 -0.461531 -0.020982  0.384458  2.054255 
-#> 
-#> Coefficients: 
-#>         Estimate Std. Error z value  Pr(>|z|)
-#> both1:2  2.06282    0.20065 10.2806 < 2.2e-16
-#> both1:3  2.91982    0.14171 20.6048 < 2.2e-16
-#> both1:4  4.12159    0.30076 13.7037 < 2.2e-16
-#> both2:1  2.58281    0.27014  9.5611 < 2.2e-16
-#> both2:2  2.17549    0.18265 11.9104 < 2.2e-16
-#> both2:3  2.67030    0.15355 17.3910 < 2.2e-16
-#> both2:4  3.10806    0.24748 12.5588 < 2.2e-16
-#> both3:1  2.93237    0.30007  9.7724 < 2.2e-16
-#> both3:2  2.65317    0.14139 18.7646 < 2.2e-16
-#> both3:3  2.91685    0.17134 17.0234 < 2.2e-16
-#> both3:4  3.20447    0.20402 15.7063 < 2.2e-16
-#> both4:3  3.80672    0.20831 18.2742 < 2.2e-16
-#> 
-#> Lambda: 0.22163 LR test value: 1.3827 p-value: 0.23964 
-#> Numerical Hessian standard error of lambda: NaN 
-#> 
-#> Log likelihood: -99.2181 
-#> ML residual variance (sigma squared): 890.66, (sigma: 29.844)
-#> Number of observations: 99 
-#> Number of parameters estimated: 14 
-#> AIC: 226.44
-#> 
 ecarIVaw <- spautolm(ft.SID74 ~ ft.NWBIR74, data=mdata.4,
  listw=sids.nhbr.listw.4, weights=BIR74, family="CAR")
-#> Warning: Non-symmetric spatial weights in CAR model
 summary(ecarIVaw)
-#> 
-#> Call: 
-#> spautolm(formula = ft.SID74 ~ ft.NWBIR74, data = mdata.4, listw = sids.nhbr.listw.4, 
-#>     weights = BIR74, family = "CAR")
-#> 
-#> Residuals:
-#>      Min       1Q   Median       3Q      Max 
-#> -1.99259 -0.44794  0.15464  0.60748  1.95751 
-#> 
-#> Coefficients: 
-#>             Estimate Std. Error z value  Pr(>|z|)
-#> (Intercept) 1.434705   0.225521  6.3618 1.995e-10
-#> ft.NWBIR74  0.040903   0.006299  6.4936 8.382e-11
-#> 
-#> Lambda: 0.22724 LR test value: 1.1936 p-value: 0.2746 
-#> Numerical Hessian standard error of lambda: 0.55494 
-#> 
-#> Log likelihood: -114.0196 
-#> ML residual variance (sigma squared): 1201, (sigma: 34.655)
-#> Number of observations: 99 
-#> Number of parameters estimated: 4 
-#> AIC: 236.04
-#> 
 nc.sids$fitIV <- append(fitted.values(ecarIVaw), NA, outl-1)
 plot(nc.sids[,"fitIV"], nbreaks=12) # Cressie 1993, p. 565
-
-# }
-# \dontrun{
+}
+if (FALSE) {
 data(oldcol, package="spdep")
 COL.errW.eig <- errorsarlm(CRIME ~ INC + HOVAL, data=COL.OLD,
  spdep::nb2listw(COL.nb, style="W"))
 summary(COL.errW.eig)
-#> 
-#> Call:
-#> errorsarlm(formula = CRIME ~ INC + HOVAL, data = COL.OLD, listw = spdep::nb2listw(COL.nb, 
-#>     style = "W"))
-#> 
-#> Residuals:
-#>       Min        1Q    Median        3Q       Max 
-#> -34.81174  -6.44031  -0.72142   7.61476  23.33626 
-#> 
-#> Type: error 
-#> Coefficients: (asymptotic standard errors) 
-#>              Estimate Std. Error z value  Pr(>|z|)
-#> (Intercept) 59.893219   5.366163 11.1613 < 2.2e-16
-#> INC         -0.941312   0.330569 -2.8476 0.0044057
-#> HOVAL       -0.302250   0.090476 -3.3407 0.0008358
-#> 
-#> Lambda: 0.56179, LR test value: 7.9935, p-value: 0.0046945
-#> Asymptotic standard error: 0.13387
-#>     z-value: 4.1966, p-value: 2.7098e-05
-#> Wald statistic: 17.611, p-value: 2.7098e-05
-#> 
-#> Log likelihood: -183.3805 for error model
-#> ML residual variance (sigma squared): 95.575, (sigma: 9.7762)
-#> Number of observations: 49 
-#> Number of parameters estimated: 5 
-#> AIC: 376.76, (AIC for lm: 382.75)
-#> 
 COL.errW.sar <- spautolm(CRIME ~ INC + HOVAL, data=COL.OLD,
  spdep::nb2listw(COL.nb, style="W"))
 summary(COL.errW.sar)
-#> 
-#> Call: 
-#> spautolm(formula = CRIME ~ INC + HOVAL, data = COL.OLD, listw = spdep::nb2listw(COL.nb, 
-#>     style = "W"))
-#> 
-#> Residuals:
-#>       Min        1Q    Median        3Q       Max 
-#> -34.81174  -6.44031  -0.72142   7.61476  23.33626 
-#> 
-#> Coefficients: 
-#>              Estimate Std. Error z value  Pr(>|z|)
-#> (Intercept) 59.893219   5.366163 11.1613 < 2.2e-16
-#> INC         -0.941312   0.330569 -2.8476 0.0044057
-#> HOVAL       -0.302250   0.090476 -3.3407 0.0008358
-#> 
-#> Lambda: 0.56179 LR test value: 7.9935 p-value: 0.0046945 
-#> Numerical Hessian standard error of lambda: 0.15242 
-#> 
-#> Log likelihood: -183.3805 
-#> ML residual variance (sigma squared): 95.575, (sigma: 9.7762)
-#> Number of observations: 49 
-#> Number of parameters estimated: 5 
-#> AIC: 376.76
-#> 
 data(boston, package="spData")
 gp1 <- spautolm(log(CMEDV) ~ CRIM + ZN + INDUS + CHAS + I(NOX^2)
  + I(RM^2) + AGE + log(DIS) + log(RAD) + TAX + PTRATIO + B + log(LSTAT), 
  data=boston.c, spdep::nb2listw(boston.soi), family="SMA")
 summary(gp1)
-#> 
-#> Call: spautolm(formula = log(CMEDV) ~ CRIM + ZN + INDUS + CHAS + I(NOX^2) + 
-#>     I(RM^2) + AGE + log(DIS) + log(RAD) + TAX + PTRATIO + B + 
-#>     log(LSTAT), data = boston.c, listw = spdep::nb2listw(boston.soi), 
-#>     family = "SMA")
-#> 
-#> Residuals:
-#>        Min         1Q     Median         3Q        Max 
-#> -0.5847694 -0.0713881  0.0012284  0.0827517  0.6071219 
-#> 
-#> Coefficients: 
-#>                Estimate  Std. Error  z value  Pr(>|z|)
-#> (Intercept)  4.28501607  0.15367176  27.8842 < 2.2e-16
-#> CRIM        -0.00718807  0.00106298  -6.7622 1.359e-11
-#> ZN           0.00023008  0.00051897   0.4433 0.6575185
-#> INDUS        0.00047300  0.00263339   0.1796 0.8574551
-#> CHAS1        0.01020698  0.02872047   0.3554 0.7222970
-#> I(NOX^2)    -0.44885530  0.13675913  -3.2821 0.0010304
-#> I(RM^2)      0.00638094  0.00110330   5.7835 7.316e-09
-#> AGE         -0.00043973  0.00051336  -0.8566 0.3916862
-#> log(DIS)    -0.15650578  0.03856337  -4.0584 4.941e-05
-#> log(RAD)     0.07583760  0.02016468   3.7609 0.0001693
-#> TAX         -0.00049364  0.00012162  -4.0588 4.933e-05
-#> PTRATIO     -0.02494959  0.00538791  -4.6307 3.645e-06
-#> B            0.00048517  0.00010944   4.4334 9.277e-06
-#> log(LSTAT)  -0.32961379  0.02353891 -14.0029 < 2.2e-16
-#> 
-#> Lambda: 0.61991 LR test value: 144.28 p-value: < 2.22e-16 
-#> Numerical Hessian standard error of lambda: 0.044359 
-#> 
-#> Log likelihood: 229.1208 
-#> ML residual variance (sigma squared): 0.02596, (sigma: 0.16112)
-#> Number of observations: 506 
-#> Number of parameters estimated: 16 
-#> AIC: -426.24
-#> 
-# }
+}
    @@ -1202,7 +537,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

    diff --git a/docs/reference/stsls.html b/docs/reference/stsls.html index 9aadaf9..ed6230a 100644 --- a/docs/reference/stsls.html +++ b/docs/reference/stsls.html @@ -17,7 +17,7 @@ spatialreg - 1.2-7 + 1.3-1 @@ -196,7 +196,7 @@

    Examples

    #> Type: lag #> Coefficients: (asymptotic standard errors) #> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) 45.079251 7.177347 6.2808 3.369e-10 +#> (Intercept) 45.079250 7.177347 6.2808 3.369e-10 #> INC -1.031616 0.305143 -3.3808 0.0007229 #> HOVAL -0.265926 0.088499 -3.0049 0.0026570 #> @@ -209,9 +209,9 @@

    Examples

    #> ML residual variance (sigma squared): 95.494, (sigma: 9.7721) #> Number of observations: 49 #> Number of parameters estimated: 5 -#> AIC: 374.78, (AIC for lm: 382.75) +#> AIC: NA (not available for weighted model), (AIC for lm: 382.75) #> LM test for residual autocorrelation -#> test value: 0.31955, p-value: 0.57188 +#> test value: 0.31954, p-value: 0.57188 #> #> Correlation of coefficients #> sigma rho (Intercept) INC @@ -262,19 +262,19 @@

    Examples

    #> Simulation results ( variance matrix): #> ======================================================== #> Simulated standard errors -#> Direct Indirect Total -#> INC 0.39668006 0.9695180 1.0529050 -#> HOVAL 0.09757127 0.3123932 0.3649017 +#> Direct Indirect Total +#> INC 0.4250637 0.7860257 0.9598058 +#> HOVAL 0.1068486 0.6601511 0.7218709 #> #> Simulated z-values: -#> Direct Indirect Total -#> INC -2.768354 -0.8583926 -1.833383 -#> HOVAL -2.954457 -0.8425926 -1.511339 +#> Direct Indirect Total +#> INC -2.474018 -1.0625140 -1.9657924 +#> HOVAL -2.826222 -0.4999244 -0.8755077 #> #> Simulated p-values: #> Direct Indirect Total -#> INC 0.0056340 0.39068 0.066746 -#> HOVAL 0.0031322 0.39946 0.130702 +#> INC 0.0133603 0.28800 0.049323 +#> HOVAL 0.0047101 0.61713 0.381298 ev <- eigenw(lw) loobj2 <- impacts(COL.lag.stsls, R=200, evalues=ev) summary(loobj2, zstats=TRUE, short=TRUE) @@ -287,23 +287,23 @@

    Examples

    #> ======================================================== #> Simulated standard errors #> Direct Indirect Total -#> INC 0.3841092 0.7160775 0.8241613 -#> HOVAL 0.1008357 0.4126274 0.4698167 +#> INC 0.3647280 1.2688013 1.3886153 +#> HOVAL 0.1175608 0.8263993 0.9011554 #> #> Simulated z-values: -#> Direct Indirect Total -#> INC -2.895776 -1.1602746 -2.357719 -#> HOVAL -2.743940 -0.6064747 -1.121576 +#> Direct Indirect Total +#> INC -2.955756 -0.7150236 -1.4296758 +#> HOVAL -2.404673 -0.4145933 -0.6939034 #> #> Simulated p-values: -#> Direct Indirect Total -#> INC 0.0037822 0.24594 0.018388 -#> HOVAL 0.0060707 0.54420 0.262043 +#> Direct Indirect Total +#> INC 0.003119 0.47459 0.15281 +#> HOVAL 0.016187 0.67844 0.48774 require(coda) HPDinterval(loobj1) -#> lower upper -#> INC -1.7799556 -0.35225230 -#> HOVAL -0.4587508 -0.09515137 +#> lower upper +#> INC -1.8854657 -0.2997080 +#> HOVAL -0.4662763 -0.1121351 #> attr(,"Probability") #> [1] 0.95 COL.lag.stslsW <- stsls(CRIME ~ INC + HOVAL, data=COL.OLD, lw, W2X=FALSE) @@ -431,7 +431,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

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    Examples

    W <- as(listw, "CsparseMatrix") system.time(trMat <- trW(W, type="mult")) #> user system elapsed -#> 0.014 0.000 0.015 +#> 0.002 0.000 0.003 str(trMat) #> num [1:30] 0 10.91 3.65 5.62 3.66 ... -#> - attr(*, "timings")= Named num [1:2] 0.014 0.015 +#> - attr(*, "timings")= Named num [1:2] 0.002 0.003 #> ..- attr(*, "names")= chr [1:2] "user.self" "elapsed" #> - attr(*, "type")= chr "mult" #> - attr(*, "n")= int 49 set.seed(1100) system.time(trMC <- trW(W, type="MC")) #> user system elapsed -#> 0.011 0.000 0.011 +#> 0.006 0.000 0.006 str(trMC) #> num [1:30] 0 10.91 3.69 5.36 3.64 ... #> - attr(*, "sd")= num [1:30] NA NA 0.598 0.495 0.489 ... -#> - attr(*, "timings")= Named num [1:2] 0.011 0.011 +#> - attr(*, "timings")= Named num [1:2] 0.006 0.006 #> ..- attr(*, "names")= chr [1:2] "user.self" "elapsed" #> - attr(*, "type")= chr "MC" #> - attr(*, "n")= int 49 @@ -169,52 +169,45 @@

    Examples

    W <- forceSymmetric(as(listwS, "CsparseMatrix")) system.time(trmom <- trW(listw=listwS, m=24, type="moments")) #> user system elapsed -#> 0.003 0.000 0.003 +#> 0.002 0.000 0.002 str(trmom) #> num [1:24] 0 10.91 3.65 5.62 3.66 ... -#> - attr(*, "timings")= Named num [1:2] 0.003 0.003 +#> - attr(*, "timings")= Named num [1:2] 0.002 0.002 #> ..- attr(*, "names")= chr [1:2] "user.self" "elapsed" #> - attr(*, "type")= chr "moments" #> - attr(*, "n")= int 49 -all.equal(trMat[1:24], trmom, check.attributes=FALSE) +all.equal(trMat[1:24], trmom, check.attributes=FALSE) #> [1] TRUE system.time(trMat <- trW(W, m=24, type="mult")) #> user system elapsed -#> 0.011 0.000 0.011 +#> 0.002 0.000 0.003 str(trMat) #> num [1:24] 0 10.91 3.65 5.62 3.66 ... -#> - attr(*, "timings")= Named num [1:2] 0.011 0.011 +#> - attr(*, "timings")= Named num [1:2] 0.002 0.003 #> ..- attr(*, "names")= chr [1:2] "user.self" "elapsed" #> - attr(*, "type")= chr "mult" #> - attr(*, "n")= int 49 -all.equal(trMat, trmom, check.attributes=FALSE) +all.equal(trMat, trmom, check.attributes=FALSE) #> [1] TRUE set.seed(1) system.time(trMC <- trW(W, m=24, type="MC")) #> user system elapsed -#> 0.013 0.000 0.013 +#> 0.007 0.000 0.007 str(trMC) #> num [1:24] 0 10.91 2.44 4.97 2.82 ... #> - attr(*, "sd")= num [1:24] NA NA 0.618 0.501 0.451 ... -#> - attr(*, "timings")= Named num [1:2] 0.013 0.013 +#> - attr(*, "timings")= Named num [1:2] 0.007 0.007 #> ..- attr(*, "names")= chr [1:2] "user.self" "elapsed" #> - attr(*, "type")= chr "MC" #> - attr(*, "n")= int 49 -# \dontrun{ +if (FALSE) { data(boston, package="spData") listw <- spdep::nb2listw(boston.soi) listwS <- similar.listw(listw) system.time(trmom <- trW(listw=listwS, m=24, type="moments")) -#> user system elapsed -#> 0.146 0.000 0.146 str(trmom) -#> num [1:24] 0 124.2 32.7 63.7 33.2 ... -#> - attr(*, "timings")= Named num [1:2] 0.146 0.146 -#> ..- attr(*, "names")= chr [1:2] "user.self" "elapsed" -#> - attr(*, "type")= chr "moments" -#> - attr(*, "n")= int 506 library(parallel) -nc <- detectCores(logical=FALSE) +nc <- max(2L, detectCores(logical=FALSE), na.rm = TRUE)-1L # set nc to 1L here if (nc > 1L) nc <- 1L coresOpt <- get.coresOption() @@ -224,16 +217,13 @@

    Examples

    set.ClusterOption(cl) } system.time(trmomp <- trW(listw=listwS, m=24, type="moments")) -#> user system elapsed -#> 0.144 0.000 0.144 if(!get.mcOption()) { set.ClusterOption(NULL) stopCluster(cl) } -all.equal(trmom, trmomp, check.attributes=FALSE) -#> [1] TRUE +all.equal(trmom, trmomp, check.attributes=FALSE) invisible(set.coresOption(coresOpt)) -# } +}     @@ -248,7 +238,7 @@

    Examples

    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.