From 5cc60d42de78f7a72ae3ceb799f2ef370577e7bf Mon Sep 17 00:00:00 2001 From: Roger Bivand Date: Mon, 2 Dec 2024 11:07:05 +0100 Subject: [PATCH 1/2] remove residual ESRI Shapefile --- DESCRIPTION | 2 +- NEWS.md | 3 +++ inst/tinytest/test_TR_20_07_20.R | 3 +-- 3 files changed, 5 insertions(+), 3 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 8ccbf79..3cb725b 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: spatialreg Version: 1.3-6 -Date: 2024-11-19 +Date: 2024-12-02 Title: Spatial Regression Analysis Encoding: UTF-8 Authors@R: c(person("Roger", "Bivand", role = c("cre", "aut"), email = "Roger.Bivand@nhh.no", comment=c(ORCID="0000-0003-2392-6140")), diff --git a/NEWS.md b/NEWS.md index fd189e9..d7047a4 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,6 +1,9 @@ # Version 1.3-6 (development) +* Remove remaining `spData` ESRI shapefile use + * #56 add Anselin-Kelejian (1997) test to `stsls`, reported in its summary method, analogous to the reporting in the summary method of `lagsarlm` of the Lagrange multiplier test, both for residual spatial autocorrelation + * adding missing man page anchors # Version 1.3-5 (2024-08-19) diff --git a/inst/tinytest/test_TR_20_07_20.R b/inst/tinytest/test_TR_20_07_20.R index 329884f..9c593f2 100644 --- a/inst/tinytest/test_TR_20_07_20.R +++ b/inst/tinytest/test_TR_20_07_20.R @@ -4,8 +4,7 @@ library(sf) library(spatialreg) -nc <- st_read(system.file("shapes/sids.shp", package="spData")[1], quiet=TRUE) -st_crs(nc) <- "EPSG:4267" +nc <- st_read(system.file("shapes/sids.gpkg", package="spData")[1], quiet=TRUE) row.names(nc) <- as.character(nc$FIPSNO) nc$ft.SID74 <- sqrt(1000)*(sqrt(nc$SID74/nc$BIR74) + sqrt((nc$SID74+1)/nc$BIR74)) nc$ft.NWBIR74 <- sqrt(1000)*(sqrt(nc$NWBIR74/nc$BIR74) + sqrt((nc$NWBIR74+1)/nc$BIR74)) From 3348fb5226d7ddf7e1a0a8024a92b15d8db8f9b0 Mon Sep 17 00:00:00 2001 From: Roger Bivand Date: Mon, 2 Dec 2024 11:17:01 +0100 Subject: [PATCH 2/2] update docs --- docs/articles/nb_igraph.html | 6 +- docs/articles/sids_models.html | 2 +- docs/news/index.html | 5 +- docs/pkgdown.yml | 2 +- docs/reference/MCMCsamp.html | 594 +------------------- docs/reference/ME.html | 117 +--- docs/reference/ML_models.html | 896 ++----------------------------- docs/reference/SET_MCMC.html | 563 +------------------ docs/reference/SLX.html | 56 +- docs/reference/aple.html | 15 +- docs/reference/aple.mc.html | 30 +- docs/reference/aple.plot.html | 59 +- docs/reference/eigenw.html | 8 +- docs/reference/impacts.html | 249 +-------- docs/reference/invIrM.html | 12 +- docs/reference/lagmess.html | 2 +- docs/reference/lextrB.html | 11 +- docs/reference/set.mcOption.html | 4 +- docs/reference/sparse_mat.html | 49 +- docs/reference/spautolm.html | 459 +--------------- docs/reference/stsls.html | 38 +- docs/reference/trW.html | 28 +- docs/search.json | 2 +- 23 files changed, 141 insertions(+), 3066 deletions(-) diff --git a/docs/articles/nb_igraph.html b/docs/articles/nb_igraph.html index a6d43e0..0bf8f88 100644 --- a/docs/articles/nb_igraph.html +++ b/docs/articles/nb_igraph.html @@ -134,7 +134,7 @@ 

## Loading required package: spData
## Loading required package: Matrix
## Loading required package: sf
-
## Linking to GEOS 3.13.0, GDAL 3.10.0, PROJ 9.5.0; sf_use_s2() is TRUE
+
## Linking to GEOS 3.13.0, GDAL 3.10.0, PROJ 9.5.1; sf_use_s2() is TRUE

Getting some data @@ -153,9 +153,9 @@

Getting some datasf_extSoftVersion() }

##           GEOS           GDAL         proj.4 GDAL_with_GEOS     USE_PROJ_H 
-##       "3.13.0"       "3.10.0"        "9.5.0"         "true"         "true" 
+##       "3.13.0"       "3.10.0"        "9.5.1"         "true"         "true" 
 ##           PROJ 
-##        "9.5.0"
+## "9.5.1" 
 library(sf)
 columbus <- st_read(system.file("shapes/columbus.gpkg", package="spData")[1])
diff --git a/docs/articles/sids_models.html b/docs/articles/sids_models.html index 73ad9e4..ed31aa4 100644 --- a/docs/articles/sids_models.html +++ b/docs/articles/sids_models.html @@ -94,7 +94,7 @@

Getting the data into R

We will be using the spdep and spatialreg packages, here version: spdep, version -1.3-7.1, 2024-10-13, the sf package and the +1.3-8, 2024-11-25, the sf package and the tmap package. The data from the sources referred to above is documented in the help page for the nc.sids data set in diff --git a/docs/news/index.html b/docs/news/index.html index a792432..14ab65d 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -40,8 +40,9 @@

Changelog

Version 1.3-6 (development)

-
  • #56 add Anselin-Kelejian (1997) test to stsls, reported in its summary method, analogous to the reporting in the summary method of lagsarlm of the Lagrange multiplier test, both for residual spatial autocorrelation
    • -
  • adding missing man page anchors
    • +

    • Remove remaining spData ESRI shapefile use

      • +

    • #56 add Anselin-Kelejian (1997) test to stsls, reported in its summary method, analogous to the reporting in the summary method of lagsarlm of the Lagrange multiplier test, both for residual spatial autocorrelation

      • +

    • adding missing man page anchors

Version 1.3-5 (2024-08-19)

CRAN release: 2024-08-19

diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 9559662..b349ae2 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -5,7 +5,7 @@ articles: nb_igraph: nb_igraph.html sids_models: sids_models.html SpatialFiltering: SpatialFiltering.html -last_built: 2024-11-19T11:32Z +last_built: 2024-12-02T10:09Z urls: reference: https://r-spatial.github.io/spatialreg/reference article: https://r-spatial.github.io/spatialreg/articles diff --git a/docs/reference/MCMCsamp.html b/docs/reference/MCMCsamp.html index d578bc0..3412689 100644 --- a/docs/reference/MCMCsamp.html +++ b/docs/reference/MCMCsamp.html @@ -170,228 +170,28 @@

Examples#> PCTAGE65P 2.672002 3.33982 3.67753 4.04290 4.74392 #> PCTOWNHOME -0.845780 -0.56384 -0.41154 -0.21860 0.07637 #> -# \dontrun{ +if (FALSE) { # \dontrun{ esar1fw <- spautolm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata, listw=listw_NY, weights=POP8, family="SAR", method="eigen") summary(esar1fw) -#> -#> Call: -#> spautolm(formula = Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data = nydata, -#> listw = listw_NY, weights = POP8, family = "SAR", method = "eigen") -#> -#> 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: NA (not available for weighted model) -#> res <- MCMCsamp(esar1fw, mcmc=5000, burnin=500, listw=listw_NY) summary(res) -#> -#> Iterations = 1:5000 -#> Thinning interval = 1 -#> Number of chains = 1 -#> Sample size per chain = 5000 -#> -#> 1. Empirical mean and standard deviation for each variable, -#> plus standard error of the mean: -#> -#> Mean SD Naive SE Time-series SE -#> lambda 0.01296 0.01568 0.0002218 0.0008193 -#> (Intercept) -0.79417 0.15064 0.0021303 0.0085373 -#> PEXPOSURE 0.07886 0.02974 0.0004206 0.0016661 -#> PCTAGE65P 3.79201 0.58160 0.0082250 0.0316924 -#> PCTOWNHOME -0.38114 0.16875 0.0023864 0.0100745 -#> -#> 2. Quantiles for each variable: -#> -#> 2.5% 25% 50% 75% 97.5% -#> lambda -0.01785 0.00281 0.01326 0.02352 0.04254 -#> (Intercept) -1.09757 -0.89163 -0.79274 -0.69957 -0.47937 -#> PEXPOSURE 0.02125 0.05871 0.07958 0.09926 0.13413 -#> PCTAGE65P 2.68865 3.36618 3.80173 4.17535 4.89445 -#> PCTOWNHOME -0.70327 -0.49343 -0.37753 -0.27046 -0.03549 -#> ecar1f <- spautolm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata, listw=listw_NY, family="CAR", method="eigen") summary(ecar1f) -#> -#> Call: -#> spautolm(formula = Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data = nydata, -#> listw = listw_NY, family = "CAR", method = "eigen") -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -1.539732 -0.384311 -0.030646 0.335126 3.808848 -#> -#> Coefficients: -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) -0.648362 0.181129 -3.5796 0.0003442 -#> PEXPOSURE 0.077899 0.043692 1.7829 0.0745986 -#> PCTAGE65P 3.703830 0.627185 5.9055 3.516e-09 -#> 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: 0.030872 -#> -#> Log likelihood: -275.8283 -#> ML residual variance (sigma squared): 0.40758, (sigma: 0.63842) -#> Number of observations: 281 -#> Number of parameters estimated: 6 -#> AIC: 563.66 -#> res <- MCMCsamp(ecar1f, mcmc=5000, burnin=500, listw=listw_NY) summary(res) -#> -#> Iterations = 1:5000 -#> Thinning interval = 1 -#> Number of chains = 1 -#> Sample size per chain = 5000 -#> -#> 1. Empirical mean and standard deviation for each variable, -#> plus standard error of the mean: -#> -#> Mean SD Naive SE Time-series SE -#> lambda 0.08485 0.03007 0.0004252 0.001841 -#> (Intercept) -0.66321 0.21541 0.0030463 0.014591 -#> PEXPOSURE 0.08242 0.04990 0.0007057 0.003038 -#> PCTAGE65P 3.65269 0.64308 0.0090945 0.039341 -#> PCTOWNHOME -0.35759 0.22515 0.0031840 0.014851 -#> -#> 2. Quantiles for each variable: -#> -#> 2.5% 25% 50% 75% 97.5% -#> lambda 0.021075 0.06565 0.08492 0.1070 0.1398 -#> (Intercept) -1.173329 -0.78618 -0.63961 -0.5233 -0.2863 -#> PEXPOSURE -0.008489 0.04927 0.07836 0.1125 0.1967 -#> PCTAGE65P 2.400330 3.17755 3.65477 4.0908 4.9428 -#> PCTOWNHOME -0.761115 -0.51656 -0.37324 -0.2168 0.1507 -#> esar1fw <- spautolm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata, listw=listw_NY, weights=POP8, family="SAR", method="eigen") summary(esar1fw) -#> -#> Call: -#> spautolm(formula = Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data = nydata, -#> listw = listw_NY, weights = POP8, family = "SAR", method = "eigen") -#> -#> 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: NA (not available for weighted model) -#> res <- MCMCsamp(esar1fw, mcmc=5000, burnin=500, listw=listw_NY) summary(res) -#> -#> Iterations = 1:5000 -#> Thinning interval = 1 -#> Number of chains = 1 -#> Sample size per chain = 5000 -#> -#> 1. Empirical mean and standard deviation for each variable, -#> plus standard error of the mean: -#> -#> Mean SD Naive SE Time-series SE -#> lambda 0.01421 0.01648 0.000233 0.0009767 -#> (Intercept) -0.79603 0.15920 0.002251 0.0097286 -#> PEXPOSURE 0.08092 0.03097 0.000438 0.0018774 -#> PCTAGE65P 3.77070 0.60576 0.008567 0.0381835 -#> PCTOWNHOME -0.37884 0.17038 0.002410 0.0102914 -#> -#> 2. Quantiles for each variable: -#> -#> 2.5% 25% 50% 75% 97.5% -#> lambda -0.01732 0.002764 0.01370 0.02629 0.04646 -#> (Intercept) -1.12961 -0.899430 -0.80307 -0.69685 -0.48183 -#> PEXPOSURE 0.02262 0.059634 0.08016 0.10062 0.14212 -#> PCTAGE65P 2.58384 3.361796 3.81098 4.18747 4.90361 -#> PCTOWNHOME -0.69813 -0.495245 -0.38500 -0.26182 -0.03602 -#> ecar1fw <- spautolm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata, listw=listw_NY, weights=POP8, family="CAR", method="eigen") summary(ecar1fw) -#> -#> Call: -#> spautolm(formula = Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data = nydata, -#> listw = listw_NY, weights = POP8, family = "CAR", method = "eigen") -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -1.491042 -0.270906 0.081435 0.451556 4.198134 -#> -#> Coefficients: -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) -0.790154 0.144862 -5.4545 4.910e-08 -#> PEXPOSURE 0.081922 0.028593 2.8651 0.004169 -#> PCTAGE65P 3.825858 0.577720 6.6223 3.536e-11 -#> 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: 0.038977 -#> -#> Log likelihood: -251.5711 -#> ML residual variance (sigma squared): 1102.9, (sigma: 33.21) -#> Number of observations: 281 -#> Number of parameters estimated: 6 -#> AIC: NA (not available for weighted model) -#> res <- MCMCsamp(ecar1fw, mcmc=5000, burnin=500, listw=listw_NY) summary(res) -#> -#> Iterations = 1:5000 -#> Thinning interval = 1 -#> Number of chains = 1 -#> Sample size per chain = 5000 -#> -#> 1. Empirical mean and standard deviation for each variable, -#> plus standard error of the mean: -#> -#> Mean SD Naive SE Time-series SE -#> lambda 0.03646 0.04020 0.0005686 0.002593 -#> (Intercept) -0.83219 0.15755 0.0022281 0.009562 -#> PEXPOSURE 0.09116 0.03438 0.0004863 0.002139 -#> PCTAGE65P 3.74091 0.59000 0.0083439 0.035836 -#> PCTOWNHOME -0.34906 0.17055 0.0024119 0.010630 -#> -#> 2. Quantiles for each variable: -#> -#> 2.5% 25% 50% 75% 97.5% -#> lambda -0.04545 0.009909 0.04055 0.06473 0.108790 -#> (Intercept) -1.15043 -0.936965 -0.82876 -0.72797 -0.532623 -#> PEXPOSURE 0.02618 0.068120 0.08997 0.11245 0.165457 -#> PCTAGE65P 2.56456 3.350294 3.72433 4.13057 4.958447 -#> PCTOWNHOME -0.69327 -0.461490 -0.34685 -0.24507 -0.001185 -#> -# } +} # } esar0 <- errorsarlm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata, listw=listw_NY) summary(esar0) @@ -434,417 +234,53 @@

Examples#> plus standard error of the mean: #> #> Mean SD Naive SE Time-series SE -#> lambda 0.04682 0.01895 0.0005993 0.002739 -#> (Intercept) -0.65775 0.21896 0.0069243 0.031633 -#> PEXPOSURE 0.08543 0.05260 0.0016634 0.007626 -#> PCTAGE65P 3.65044 0.57371 0.0181422 0.060983 -#> PCTOWNHOME -0.36116 0.23745 0.0075089 0.036472 +#> lambda 0.04086 0.01436 0.0004541 0.001639 +#> (Intercept) -0.62555 0.18275 0.0057791 0.024443 +#> PEXPOSURE 0.06900 0.04034 0.0012758 0.004965 +#> PCTAGE65P 3.68791 0.64881 0.0205172 0.077680 +#> PCTOWNHOME -0.38263 0.19769 0.0062513 0.025188 #> #> 2. Quantiles for each variable: #> -#> 2.5% 25% 50% 75% 97.5% -#> lambda 0.0109828 0.03340 0.04839 0.05958 0.08139 -#> (Intercept) -1.0771095 -0.81427 -0.62781 -0.51408 -0.23029 -#> PEXPOSURE -0.0004135 0.04738 0.07927 0.11564 0.20607 -#> PCTAGE65P 2.5911599 3.20769 3.63005 4.09097 4.74100 -#> PCTOWNHOME -0.8037868 -0.54136 -0.35322 -0.20277 0.17058 +#> 2.5% 25% 50% 75% 97.5% +#> lambda 0.014717 0.03100 0.04103 0.05272 0.065715 +#> (Intercept) -0.972641 -0.75486 -0.63637 -0.50400 -0.198074 +#> PEXPOSURE -0.004903 0.03891 0.06820 0.09560 0.152374 +#> PCTAGE65P 2.382993 3.31371 3.67406 4.12972 4.919868 +#> PCTOWNHOME -0.779951 -0.49064 -0.38080 -0.25234 -0.003732 #> -# \dontrun{ +if (FALSE) { # \dontrun{ esar0w <- errorsarlm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata, listw=listw_NY, weights=POP8) 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) -#> res <- MCMCsamp(esar0w, mcmc=5000, burnin=500, listw=listw_NY) summary(res) -#> -#> Iterations = 1:5000 -#> Thinning interval = 1 -#> Number of chains = 1 -#> Sample size per chain = 5000 -#> -#> 1. Empirical mean and standard deviation for each variable, -#> plus standard error of the mean: -#> -#> Mean SD Naive SE Time-series SE -#> lambda 0.01177 0.01571 0.0002221 0.0008781 -#> (Intercept) -0.79575 0.14274 0.0020186 0.0078442 -#> PEXPOSURE 0.08036 0.02929 0.0004143 0.0017545 -#> PCTAGE65P 3.81500 0.57129 0.0080793 0.0330363 -#> PCTOWNHOME -0.38426 0.15508 0.0021932 0.0087813 -#> -#> 2. Quantiles for each variable: -#> -#> 2.5% 25% 50% 75% 97.5% -#> lambda -0.01899 0.00175 0.01220 0.02213 0.04328 -#> (Intercept) -1.07330 -0.89063 -0.79366 -0.70020 -0.51399 -#> PEXPOSURE 0.02200 0.06082 0.08123 0.10067 0.13675 -#> PCTAGE65P 2.70738 3.41885 3.80976 4.20150 4.94589 -#> PCTOWNHOME -0.70906 -0.47931 -0.38839 -0.28703 -0.06345 -#> esar1 <- errorsarlm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata, listw=listw_NY, etype="emixed") summary(esar1) -#> -#> Call:errorsarlm(formula = Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, -#> data = nydata, listw = listw_NY, etype = "emixed") -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -1.81562 -0.37641 -0.02224 0.33638 4.00054 -#> -#> Type: error -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) -1.118019 0.247425 -4.5186 6.225e-06 -#> PEXPOSURE 0.218279 0.079245 2.7545 0.005879 -#> PCTAGE65P 3.416477 0.645587 5.2920 1.210e-07 -#> PCTOWNHOME 0.036593 0.249835 0.1465 0.883551 -#> lag.(Intercept) 0.121515 0.057636 2.1083 0.035003 -#> lag.PEXPOSURE -0.035075 0.015943 -2.2000 0.027808 -#> lag.PCTAGE65P 0.263096 0.220118 1.1953 0.231989 -#> lag.PCTOWNHOME -0.155680 0.059213 -2.6291 0.008560 -#> -#> Lambda: 0.022723, LR test value: 1.6846, p-value: 0.19432 -#> Asymptotic standard error: 0.017169 -#> z-value: 1.3235, p-value: 0.18567 -#> Wald statistic: 1.7516, p-value: 0.18567 -#> -#> Log likelihood: -269.5398 for error model -#> ML residual variance (sigma squared): 0.39759, (sigma: 0.63055) -#> Number of observations: 281 -#> Number of parameters estimated: 10 -#> AIC: 559.08, (AIC for lm: 558.76) -#> res <- MCMCsamp(esar1, mcmc=5000, burnin=500, listw=listw_NY) summary(res) -#> -#> Iterations = 1:5000 -#> Thinning interval = 1 -#> Number of chains = 1 -#> Sample size per chain = 5000 -#> -#> 1. Empirical mean and standard deviation for each variable, -#> plus standard error of the mean: -#> -#> Mean SD Naive SE Time-series SE -#> lambda 0.02791 0.01696 0.0002398 0.001257 -#> (Intercept) -1.11089 0.24295 0.0034359 0.019066 -#> PEXPOSURE 0.20998 0.08066 0.0011407 0.006616 -#> PCTAGE65P 3.44550 0.61861 0.0087484 0.045120 -#> PCTOWNHOME 0.03249 0.25058 0.0035437 0.022380 -#> lag.(Intercept) 0.11788 0.05850 0.0008273 0.004548 -#> lag.PEXPOSURE -0.03381 0.01626 0.0002299 0.001358 -#> lag.PCTAGE65P 0.25893 0.23503 0.0033238 0.019521 -#> lag.PCTOWNHOME -0.15337 0.05824 0.0008236 0.004496 -#> -#> 2. Quantiles for each variable: -#> -#> 2.5% 25% 50% 75% 97.5% -#> lambda -0.004792 0.01604 0.02829 0.03980 0.0616941 -#> (Intercept) -1.549205 -1.28823 -1.12499 -0.91520 -0.6509996 -#> PEXPOSURE 0.063165 0.15363 0.20577 0.26455 0.3711420 -#> PCTAGE65P 2.335975 3.01533 3.44864 3.91005 4.6644758 -#> PCTOWNHOME -0.505045 -0.11695 0.05420 0.19038 0.5171339 -#> lag.(Intercept) 0.006027 0.07616 0.11689 0.16220 0.2310606 -#> lag.PEXPOSURE -0.064845 -0.04488 -0.03385 -0.02339 -0.0009086 -#> lag.PCTAGE65P -0.195440 0.09430 0.24778 0.43584 0.6912104 -#> lag.PCTOWNHOME -0.263176 -0.19213 -0.15386 -0.11592 -0.0317401 -#> lsar0 <- lagsarlm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata, listw=listw_NY) summary(lsar0) -#> -#> Call:lagsarlm(formula = Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data = nydata, -#> listw = listw_NY) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -1.586752 -0.391580 -0.022469 0.338017 4.029430 -#> -#> Type: lag -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) -0.514495 0.156154 -3.2948 0.000985 -#> PEXPOSURE 0.047627 0.034509 1.3801 0.167542 -#> PCTAGE65P 3.648198 0.599046 6.0900 1.129e-09 -#> PCTOWNHOME -0.414601 0.169554 -2.4453 0.014475 -#> -#> Rho: 0.038893, LR test value: 6.9683, p-value: 0.0082967 -#> Asymptotic standard error: 0.015053 -#> z-value: 2.5837, p-value: 0.0097755 -#> Wald statistic: 6.6754, p-value: 0.0097755 -#> -#> Log likelihood: -275.2447 for lag model -#> ML residual variance (sigma squared): 0.41166, (sigma: 0.6416) -#> Number of observations: 281 -#> Number of parameters estimated: 6 -#> AIC: 562.49, (AIC for lm: 567.46) -#> LM test for residual autocorrelation -#> test value: 1.4633, p-value: 0.22641 -#> res <- MCMCsamp(lsar0, mcmc=5000, burnin=500, listw=listw_NY) summary(res) -#> -#> Iterations = 1:5000 -#> Thinning interval = 1 -#> Number of chains = 1 -#> Sample size per chain = 5000 -#> -#> 1. Empirical mean and standard deviation for each variable, -#> plus standard error of the mean: -#> -#> Mean SD Naive SE Time-series SE -#> rho 0.03924 0.01531 0.0002166 0.0009369 -#> (Intercept) -0.51482 0.16721 0.0023647 0.0103798 -#> PEXPOSURE 0.05057 0.03374 0.0004771 0.0019411 -#> PCTAGE65P 3.58543 0.66827 0.0094508 0.0442173 -#> PCTOWNHOME -0.41083 0.18638 0.0026358 0.0118147 -#> -#> 2. Quantiles for each variable: -#> -#> 2.5% 25% 50% 75% 97.5% -#> rho 0.01129 0.02805 0.03879 0.05012 0.07010 -#> (Intercept) -0.84632 -0.62884 -0.50852 -0.39309 -0.20739 -#> PEXPOSURE -0.01768 0.02886 0.04981 0.07205 0.11808 -#> PCTAGE65P 2.28982 3.12634 3.60888 4.03227 4.88549 -#> PCTOWNHOME -0.74664 -0.54787 -0.40497 -0.28735 -0.03815 -#> lsar1 <- lagsarlm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata, listw=listw_NY, type="mixed") summary(lsar1) -#> -#> Call:lagsarlm(formula = Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data = nydata, -#> listw = listw_NY, type = "mixed") -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -1.799308 -0.390125 -0.021371 0.346128 3.965251 -#> -#> Type: mixed -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) -1.131233 0.249631 -4.5316 5.853e-06 -#> PEXPOSURE 0.218364 0.079301 2.7536 0.005894 -#> PCTAGE65P 3.361158 0.654123 5.1384 2.771e-07 -#> PCTOWNHOME 0.071903 0.253967 0.2831 0.777085 -#> lag.(Intercept) 0.132544 0.056175 2.3595 0.018300 -#> lag.PEXPOSURE -0.035239 0.015536 -2.2681 0.023322 -#> lag.PCTAGE65P 0.161685 0.223690 0.7228 0.469798 -#> lag.PCTOWNHOME -0.140681 0.058529 -2.4036 0.016234 -#> -#> Rho: 0.026981, LR test value: 2.558, p-value: 0.10974 -#> Asymptotic standard error: 0.016766 -#> z-value: 1.6093, p-value: 0.10755 -#> Wald statistic: 2.5899, p-value: 0.10755 -#> -#> Log likelihood: -269.1031 for mixed model -#> ML residual variance (sigma squared): 0.39587, (sigma: 0.62918) -#> Number of observations: 281 -#> Number of parameters estimated: 10 -#> AIC: 558.21, (AIC for lm: 558.76) -#> LM test for residual autocorrelation -#> test value: 4.908, p-value: 0.026732 -#> res <- MCMCsamp(lsar1, mcmc=5000, burnin=500, listw=listw_NY) summary(res) -#> -#> Iterations = 1:5000 -#> Thinning interval = 1 -#> Number of chains = 1 -#> Sample size per chain = 5000 -#> -#> 1. Empirical mean and standard deviation for each variable, -#> plus standard error of the mean: -#> -#> Mean SD Naive SE Time-series SE -#> rho 0.02471 0.01629 0.0002304 0.001219 -#> (Intercept) -1.12989 0.24291 0.0034353 0.017687 -#> PEXPOSURE 0.21704 0.08665 0.0012254 0.007356 -#> PCTAGE65P 3.40551 0.61299 0.0086689 0.043441 -#> PCTOWNHOME 0.04146 0.25683 0.0036321 0.019859 -#> lag.(Intercept) 0.12868 0.05650 0.0007991 0.004535 -#> lag.PEXPOSURE -0.03472 0.01732 0.0002449 0.001526 -#> lag.PCTAGE65P 0.17214 0.20763 0.0029364 0.015671 -#> lag.PCTOWNHOME -0.13618 0.06186 0.0008749 0.005326 -#> -#> 2. Quantiles for each variable: -#> -#> 2.5% 25% 50% 75% 97.5% -#> rho -0.008938 0.01339 0.02554 0.03617 0.054650 -#> (Intercept) -1.604105 -1.28894 -1.12504 -0.97119 -0.660270 -#> PEXPOSURE 0.053725 0.15881 0.21725 0.27236 0.389018 -#> PCTAGE65P 2.213387 2.99625 3.42281 3.85452 4.551724 -#> PCTOWNHOME -0.451879 -0.14346 0.04721 0.22476 0.545593 -#> lag.(Intercept) 0.019705 0.09016 0.13030 0.16767 0.243720 -#> lag.PEXPOSURE -0.067460 -0.04719 -0.03527 -0.02388 0.003145 -#> lag.PCTAGE65P -0.238603 0.04557 0.17728 0.29777 0.562834 -#> lag.PCTOWNHOME -0.246111 -0.18197 -0.13460 -0.09357 -0.018730 -#> ssar0 <- sacsarlm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata, listw=listw_NY) summary(ssar0) -#> -#> Call:sacsarlm(formula = Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data = nydata, -#> listw = listw_NY) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -1.468382 -0.375687 -0.034996 0.314714 3.833950 -#> -#> Type: sac -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) -0.386572 0.123188 -3.1381 0.001701 -#> PEXPOSURE 0.026684 0.024013 1.1112 0.266479 -#> PCTAGE65P 3.089824 0.562851 5.4896 4.029e-08 -#> PCTOWNHOME -0.323052 0.137449 -2.3503 0.018756 -#> -#> Rho: 0.089451 -#> Asymptotic standard error: 0.019427 -#> z-value: 4.6046, p-value: 4.1325e-06 -#> Lambda: -0.08192 -#> Asymptotic standard error: 0.033201 -#> z-value: -2.4674, p-value: 0.01361 -#> -#> LR test value: 10.114, p-value: 0.0063661 -#> -#> Log likelihood: -273.672 for sac model -#> ML residual variance (sigma squared): 0.3766, (sigma: 0.61368) -#> Number of observations: 281 -#> Number of parameters estimated: 7 -#> AIC: 561.34, (AIC for lm: 567.46) -#> res <- MCMCsamp(ssar0, mcmc=5000, burnin=500, listw=listw_NY) summary(res) -#> -#> Iterations = 1:5000 -#> Thinning interval = 1 -#> Number of chains = 1 -#> Sample size per chain = 5000 -#> -#> 1. Empirical mean and standard deviation for each variable, -#> plus standard error of the mean: -#> -#> Mean SD Naive SE Time-series SE -#> rho -0.04897 0.07268 0.001028 0.02171 -#> lambda 0.07158 0.07109 0.001005 0.01911 -#> (Intercept) -0.79253 0.30171 0.004267 0.05929 -#> PEXPOSURE 0.12518 0.08005 0.001132 0.01428 -#> PCTAGE65P 3.33373 0.62239 0.008802 0.04114 -#> PCTOWNHOME -0.24558 0.24700 0.003493 0.03231 -#> -#> 2. Quantiles for each variable: -#> -#> 2.5% 25% 50% 75% 97.5% -#> rho -0.148205 -0.10220 -0.07484 -0.0003653 0.09879 -#> lambda -0.097739 0.03709 0.10543 0.1218063 0.13809 -#> (Intercept) -1.371833 -1.01584 -0.80342 -0.5327538 -0.26199 -#> PEXPOSURE -0.001867 0.05674 0.12102 0.1859304 0.27450 -#> PCTAGE65P 2.139989 2.91933 3.33752 3.7143648 4.58803 -#> PCTOWNHOME -0.743597 -0.40260 -0.25567 -0.0792356 0.24964 -#> ssar1 <- sacsarlm(Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data=nydata, listw=listw_NY, type="sacmixed") summary(ssar1) -#> -#> Call:sacsarlm(formula = Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME, data = nydata, -#> listw = listw_NY, type = "sacmixed") -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -1.633958 -0.363826 -0.019927 0.348238 3.655509 -#> -#> Type: sacmixed -#> Coefficients: (asymptotic standard errors) -#> Estimate Std. Error z value Pr(>|z|) -#> (Intercept) -1.133298 0.247495 -4.5791 4.670e-06 -#> PEXPOSURE 0.206963 0.074480 2.7788 0.005456 -#> PCTAGE65P 3.083983 0.671081 4.5955 4.316e-06 -#> PCTOWNHOME 0.174800 0.256280 0.6821 0.495196 -#> lag.(Intercept) 0.153427 0.050817 3.0192 0.002534 -#> lag.PEXPOSURE -0.033400 0.013817 -2.4173 0.015634 -#> lag.PCTAGE65P -0.079738 0.222144 -0.3589 0.719634 -#> lag.PCTOWNHOME -0.102502 0.056760 -1.8059 0.070940 -#> -#> Rho: 0.092495 -#> Asymptotic standard error: 0.023829 -#> z-value: 3.8817, p-value: 0.00010375 -#> Lambda: -0.091069 -#> Asymptotic standard error: 0.038431 -#> z-value: -2.3697, p-value: 0.017804 -#> -#> LR test value: 22.379, p-value: 0.0010335 -#> -#> Log likelihood: -267.5392 for sacmixed model -#> ML residual variance (sigma squared): 0.35617, (sigma: 0.5968) -#> Number of observations: 281 -#> Number of parameters estimated: 11 -#> AIC: 557.08, (AIC for lm: 567.46) -#> res <- MCMCsamp(ssar1, mcmc=5000, burnin=500, listw=listw_NY) summary(res) -#> -#> Iterations = 1:5000 -#> Thinning interval = 1 -#> Number of chains = 1 -#> Sample size per chain = 5000 -#> -#> 1. Empirical mean and standard deviation for each variable, -#> plus standard error of the mean: -#> -#> Mean SD Naive SE Time-series SE -#> rho -0.005935 0.06247 0.0008835 0.012391 -#> lambda 0.025149 0.06578 0.0009302 0.012564 -#> (Intercept) -1.104966 0.25946 0.0036694 0.020691 -#> PEXPOSURE 0.220056 0.08072 0.0011415 0.006564 -#> PCTAGE65P 3.448064 0.68477 0.0096841 0.053253 -#> PCTOWNHOME 0.002352 0.25423 0.0035954 0.018914 -#> lag.(Intercept) 0.107623 0.06651 0.0009406 0.007925 -#> lag.PEXPOSURE -0.034004 0.01609 0.0002276 0.001352 -#> lag.PCTAGE65P 0.279208 0.31780 0.0044943 0.037387 -#> lag.PCTOWNHOME -0.134012 0.06451 0.0009123 0.005533 -#> -#> 2. Quantiles for each variable: -#> -#> 2.5% 25% 50% 75% 97.5% -#> rho -0.10755 -0.05711 -0.014110 0.05341 0.099104 -#> lambda -0.09950 -0.03206 0.037324 0.08249 0.118177 -#> (Intercept) -1.60962 -1.28203 -1.096564 -0.93087 -0.597130 -#> PEXPOSURE 0.05429 0.16818 0.217623 0.27874 0.368060 -#> PCTAGE65P 2.11661 3.00219 3.455553 3.93036 4.829667 -#> PCTOWNHOME -0.46367 -0.19124 -0.002169 0.17287 0.544393 -#> lag.(Intercept) -0.02153 0.05985 0.109887 0.15841 0.231289 -#> lag.PEXPOSURE -0.06423 -0.04610 -0.033598 -0.02331 -0.001144 -#> lag.PCTAGE65P -0.29223 0.02822 0.266936 0.50401 0.926973 -#> lag.PCTOWNHOME -0.26326 -0.17692 -0.130161 -0.08812 -0.009731 -#> -# } +} # }