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Examples

g2+g1 } -#> Time for computation = 0.470000 sec +#> Time for computation = 0.541000 sec diff --git a/reference/create.dbMEM.model-1.png b/reference/create.dbMEM.model-1.png index bea3f5e..80b2f9b 100644 Binary files a/reference/create.dbMEM.model-1.png and b/reference/create.dbMEM.model-1.png differ diff --git a/reference/create.dbMEM.model.html b/reference/create.dbMEM.model.html index 1cb049a..889e798 100644 --- a/reference/create.dbMEM.model.html +++ b/reference/create.dbMEM.model.html @@ -167,78 +167,78 @@

Examples

result } -#> dbMEM.1 dbMEM.2 dbMEM.3 dbMEM.4 dbMEM.5 dbMEM.6 -#> S1 -0.8088104 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S2 -0.8301859 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S3 1.3288196 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S4 -0.8243590 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S5 -0.3180858 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S6 1.4526215 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S7 0.0000000 -1.2547674 -0.3024004 -0.0094777920 0.0000000 0.0000000 -#> S8 0.0000000 -0.4461888 1.3432320 0.0000578362 0.0000000 0.0000000 -#> S9 0.0000000 1.0511326 -0.2893761 -1.8563424185 0.0000000 0.0000000 -#> S10 0.0000000 1.2758693 0.4437657 -0.0096801229 0.0000000 0.0000000 -#> S11 0.0000000 -0.9854069 1.0291819 0.0095725707 0.0000000 0.0000000 -#> S12 0.0000000 -0.6741798 -1.9154497 -0.0191633414 0.0000000 0.0000000 -#> S13 0.0000000 1.0335410 -0.3089535 1.8850332679 0.0000000 0.0000000 -#> S14 0.0000000 0.0000000 0.0000000 0.0000000000 1.3373145 -1.0616086 -#> S15 0.0000000 0.0000000 0.0000000 0.0000000000 -0.8917131 0.2133824 -#> S16 0.0000000 0.0000000 0.0000000 0.0000000000 1.3416111 0.3774076 -#> S17 0.0000000 0.0000000 0.0000000 0.0000000000 -0.9038420 0.1347745 -#> S18 0.0000000 0.0000000 0.0000000 0.0000000000 -0.8941315 -1.3502651 -#> S19 0.0000000 0.0000000 0.0000000 0.0000000000 0.0107611 1.6863092 -#> S20 0.0000000 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S21 0.0000000 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S22 0.0000000 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S23 0.0000000 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S24 0.0000000 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S25 0.0000000 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S26 0.0000000 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S27 0.0000000 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S28 0.0000000 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S29 0.0000000 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S30 0.0000000 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S31 0.0000000 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S32 0.0000000 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S33 0.0000000 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S34 0.0000000 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S35 0.0000000 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> dbMEM.7 dbMEM.8 dbMEM.9 dbMEM.10 -#> S1 0.0000000 0.0000000 0.0000000 0.00000000 -#> S2 0.0000000 0.0000000 0.0000000 0.00000000 -#> S3 0.0000000 0.0000000 0.0000000 0.00000000 -#> S4 0.0000000 0.0000000 0.0000000 0.00000000 -#> S5 0.0000000 0.0000000 0.0000000 0.00000000 -#> S6 0.0000000 0.0000000 0.0000000 0.00000000 -#> S7 0.0000000 0.0000000 0.0000000 0.00000000 -#> S8 0.0000000 0.0000000 0.0000000 0.00000000 -#> S9 0.0000000 0.0000000 0.0000000 0.00000000 -#> S10 0.0000000 0.0000000 0.0000000 0.00000000 -#> S11 0.0000000 0.0000000 0.0000000 0.00000000 -#> S12 0.0000000 0.0000000 0.0000000 0.00000000 -#> S13 0.0000000 0.0000000 0.0000000 0.00000000 -#> S14 0.0000000 0.0000000 0.0000000 0.00000000 -#> S15 0.0000000 0.0000000 0.0000000 0.00000000 -#> S16 0.0000000 0.0000000 0.0000000 0.00000000 -#> S17 0.0000000 0.0000000 0.0000000 0.00000000 -#> S18 0.0000000 0.0000000 0.0000000 0.00000000 -#> S19 0.0000000 0.0000000 0.0000000 0.00000000 -#> S20 -0.4719266 0.0000000 0.0000000 0.00000000 -#> S21 1.0902133 0.0000000 0.0000000 0.00000000 -#> S22 -0.9630547 0.0000000 0.0000000 0.00000000 -#> S23 -0.9681890 0.0000000 0.0000000 0.00000000 -#> S24 1.3129570 0.0000000 0.0000000 0.00000000 -#> S25 0.0000000 1.0949007 0.0000000 0.00000000 -#> S26 0.0000000 -0.9670319 0.0000000 0.00000000 -#> S27 0.0000000 1.3090464 0.0000000 0.00000000 -#> S28 0.0000000 -0.4734303 0.0000000 0.00000000 -#> S29 0.0000000 -0.9634848 0.0000000 0.00000000 -#> S30 0.0000000 0.0000000 1.0447081 0.55187472 -#> S31 0.0000000 0.0000000 -1.0726326 1.37763116 -#> S32 0.0000000 0.0000000 -0.5511238 -1.70425755 -#> S33 0.0000000 0.0000000 -1.2946839 -0.01453703 -#> S34 0.0000000 0.0000000 0.8304130 -0.76515180 -#> S35 0.0000000 0.0000000 1.0433192 0.55444051 +#> dbMEM.1 dbMEM.2 dbMEM.3 dbMEM.4 dbMEM.5 dbMEM.6 +#> S1 -1.0426410 -0.560773845 0.00000000 0.0000000 0.00000000 0.0000000 +#> S2 -1.0405920 -0.564540538 0.00000000 0.0000000 0.00000000 0.0000000 +#> S3 0.5385387 1.714887979 0.00000000 0.0000000 0.00000000 0.0000000 +#> S4 1.2944807 0.006894797 0.00000000 0.0000000 0.00000000 0.0000000 +#> S5 1.0824626 -1.358434905 0.00000000 0.0000000 0.00000000 0.0000000 +#> S6 -0.8322491 0.761966511 0.00000000 0.0000000 0.00000000 0.0000000 +#> S7 0.0000000 0.000000000 0.60367639 -0.1868201 0.00000000 0.0000000 +#> S8 0.0000000 0.000000000 0.60570305 -0.1820122 0.00000000 0.0000000 +#> S9 0.0000000 0.000000000 0.59688978 -0.1755042 0.00000000 0.0000000 +#> S10 0.0000000 0.000000000 -0.95147128 -0.7042591 0.00000000 0.0000000 +#> S11 0.0000000 0.000000000 1.06391594 1.7145220 0.00000000 0.0000000 +#> S12 0.0000000 0.000000000 -1.96793887 1.0626174 0.00000000 0.0000000 +#> S13 0.0000000 0.000000000 0.04922499 -1.5285437 0.00000000 0.0000000 +#> S14 0.0000000 0.000000000 0.00000000 0.0000000 1.45348729 0.6254895 +#> S15 0.0000000 0.000000000 0.00000000 0.0000000 -1.02765443 0.8147607 +#> S16 0.0000000 0.000000000 0.00000000 0.0000000 -1.02787548 0.8144025 +#> S17 0.0000000 0.000000000 0.00000000 0.0000000 -0.09597001 -1.9883616 +#> S18 0.0000000 0.000000000 0.00000000 0.0000000 -0.52333308 -0.5156629 +#> S19 0.0000000 0.000000000 0.00000000 0.0000000 1.22134570 0.2493718 +#> S20 0.0000000 0.000000000 0.00000000 0.0000000 0.00000000 0.0000000 +#> S21 0.0000000 0.000000000 0.00000000 0.0000000 0.00000000 0.0000000 +#> S22 0.0000000 0.000000000 0.00000000 0.0000000 0.00000000 0.0000000 +#> S23 0.0000000 0.000000000 0.00000000 0.0000000 0.00000000 0.0000000 +#> S24 0.0000000 0.000000000 0.00000000 0.0000000 0.00000000 0.0000000 +#> S25 0.0000000 0.000000000 0.00000000 0.0000000 0.00000000 0.0000000 +#> S26 0.0000000 0.000000000 0.00000000 0.0000000 0.00000000 0.0000000 +#> S27 0.0000000 0.000000000 0.00000000 0.0000000 0.00000000 0.0000000 +#> S28 0.0000000 0.000000000 0.00000000 0.0000000 0.00000000 0.0000000 +#> S29 0.0000000 0.000000000 0.00000000 0.0000000 0.00000000 0.0000000 +#> S30 0.0000000 0.000000000 0.00000000 0.0000000 0.00000000 0.0000000 +#> S31 0.0000000 0.000000000 0.00000000 0.0000000 0.00000000 0.0000000 +#> S32 0.0000000 0.000000000 0.00000000 0.0000000 0.00000000 0.0000000 +#> S33 0.0000000 0.000000000 0.00000000 0.0000000 0.00000000 0.0000000 +#> S34 0.0000000 0.000000000 0.00000000 0.0000000 0.00000000 0.0000000 +#> S35 0.0000000 0.000000000 0.00000000 0.0000000 0.00000000 0.0000000 +#> dbMEM.7 dbMEM.8 dbMEM.9 dbMEM.10 +#> S1 0.0000000 0.0000000 0.0000000 0.000000000 +#> S2 0.0000000 0.0000000 0.0000000 0.000000000 +#> S3 0.0000000 0.0000000 0.0000000 0.000000000 +#> S4 0.0000000 0.0000000 0.0000000 0.000000000 +#> S5 0.0000000 0.0000000 0.0000000 0.000000000 +#> S6 0.0000000 0.0000000 0.0000000 0.000000000 +#> S7 0.0000000 0.0000000 0.0000000 0.000000000 +#> S8 0.0000000 0.0000000 0.0000000 0.000000000 +#> S9 0.0000000 0.0000000 0.0000000 0.000000000 +#> S10 0.0000000 0.0000000 0.0000000 0.000000000 +#> S11 0.0000000 0.0000000 0.0000000 0.000000000 +#> S12 0.0000000 0.0000000 0.0000000 0.000000000 +#> S13 0.0000000 0.0000000 0.0000000 0.000000000 +#> S14 0.0000000 0.0000000 0.0000000 0.000000000 +#> S15 0.0000000 0.0000000 0.0000000 0.000000000 +#> S16 0.0000000 0.0000000 0.0000000 0.000000000 +#> S17 0.0000000 0.0000000 0.0000000 0.000000000 +#> S18 0.0000000 0.0000000 0.0000000 0.000000000 +#> S19 0.0000000 0.0000000 0.0000000 0.000000000 +#> S20 1.5343487 0.0000000 0.0000000 0.000000000 +#> S21 -0.6284451 0.0000000 0.0000000 0.000000000 +#> S22 -1.0796112 0.0000000 0.0000000 0.000000000 +#> S23 -0.6446458 0.0000000 0.0000000 0.000000000 +#> S24 0.8183534 0.0000000 0.0000000 0.000000000 +#> S25 0.0000000 -0.3774129 0.0000000 0.000000000 +#> S26 0.0000000 0.7205735 0.0000000 0.000000000 +#> S27 0.0000000 -1.8073283 0.0000000 0.000000000 +#> S28 0.0000000 0.7306073 0.0000000 0.000000000 +#> S29 0.0000000 0.7335603 0.0000000 0.000000000 +#> S30 0.0000000 0.0000000 0.5230897 1.730402441 +#> S31 0.0000000 0.0000000 -1.0367954 -0.571712277 +#> S32 0.0000000 0.0000000 -0.8385537 0.749420473 +#> S33 0.0000000 0.0000000 1.0946391 -1.337973385 +#> S34 0.0000000 0.0000000 1.2943232 0.001741763 +#> S35 0.0000000 0.0000000 -1.0367029 -0.571879015 diff --git a/reference/dbmem.html b/reference/dbmem.html index f3810c3..1e7c50c 100644 --- a/reference/dbmem.html +++ b/reference/dbmem.html @@ -220,7 +220,7 @@

Examples

tmp[1:10,1:6] } #> User-provided truncation threshold = 1.012 -#> Time to compute dbMEMs = 0.018000 sec +#> Time to compute dbMEMs = 0.020000 sec diff --git a/reference/envspace.test.html b/reference/envspace.test.html index 214ed8d..0d2267e 100644 --- a/reference/envspace.test.html +++ b/reference/envspace.test.html @@ -314,10 +314,10 @@

Examples

} #> Warning: zero sum general weights #> Procedure stopped (adjR2thresh criteria) adjR2cum = 0.452381 with 11 variables (> 0.446534) -#> Procedure stopped (alpha criteria): pvalue for variable 14 is 0.062000 (> 0.050000) -#> Procedure stopped (alpha criteria): pvalue for variable 14 is 0.085000 (> 0.050000) -#> Procedure stopped (alpha criteria): pvalue for variable 13 is 0.070000 (> 0.050000) -#> Procedure stopped (alpha criteria): pvalue for variable 16 is 0.063000 (> 0.050000) +#> Procedure stopped (alpha criteria): pvalue for variable 14 is 0.068000 (> 0.050000) +#> Procedure stopped (alpha criteria): pvalue for variable 14 is 0.067000 (> 0.050000) +#> Procedure stopped (alpha criteria): pvalue for variable 13 is 0.058000 (> 0.050000) +#> Procedure stopped (alpha criteria): pvalue for variable 16 is 0.070000 (> 0.050000) #> Monte-Carlo test #> Call: as.randtest(sim = E.b, obs = R2.b, alter = alternative) @@ -329,7 +329,7 @@

Examples

#> Alternative hypothesis: greater #> #> Std.Obs Expectation Variance -#> -2.929908523 0.414013769 0.004592953 +#> -2.847141524 0.408750580 0.004609442 # } diff --git a/reference/forward.sel.html b/reference/forward.sel.html index 54a1dde..9b022d7 100644 --- a/reference/forward.sel.html +++ b/reference/forward.sel.html @@ -194,10 +194,10 @@

Examples

#> Testing variable 1 #> Testing variable 2 #> Testing variable 3 -#> Procedure stopped (alpha criteria): pvalue for variable 3 is 0.980000 (> 0.500000) -#> variables order R2 R2Cum AdjR2Cum F pvalue -#> 1 V2 2 0.12059517 0.1205952 0.01066956 1.0970617 0.37 -#> 2 V1 1 0.08719318 0.2077883 -0.01855784 0.7704409 0.50 +#> Procedure stopped (alpha criteria): pvalue for variable 3 is 0.790000 (> 0.500000) +#> variables order R2 R2Cum AdjR2Cum F pvalue +#> 1 V3 3 0.2869527 0.2869527 0.1978218 3.219452 0.03 +#> 2 V2 2 0.1769297 0.4638824 0.3107060 2.310143 0.10 diff --git a/reference/forward.sel.par.html b/reference/forward.sel.par.html index 4e9a3ec..7ebf153 100644 --- a/reference/forward.sel.par.html +++ b/reference/forward.sel.par.html @@ -182,9 +182,7 @@

Examples

forward.sel.par(y,x, alpha = 0.5) #> The variables in response matrix Y have been standardized -#> Procedure stopped (alpha criterion): pvalue for variable 2 is 0.6937551 -#> variable order R2 R2cum AdjR2Cum F pval -#> 1 V3 3 0.1929672 0.1929672 0.09208805 1.912856 0.1136588 +#> Error in forward.sel.par(y, x, alpha = 0.5): Procedure stopped (alpha criterion): pvalue for variable 1 is 0.583931995268155 diff --git a/reference/listw.candidates-1.png b/reference/listw.candidates-1.png index 262e0fd..40e5729 100644 Binary files a/reference/listw.candidates-1.png and b/reference/listw.candidates-1.png differ diff --git a/reference/listw.candidates-2.png b/reference/listw.candidates-2.png index ffd7db0..959a7e0 100644 Binary files a/reference/listw.candidates-2.png and b/reference/listw.candidates-2.png differ diff --git a/reference/listw.candidates-3.png b/reference/listw.candidates-3.png index 262e0fd..40e5729 100644 Binary files a/reference/listw.candidates-3.png and b/reference/listw.candidates-3.png differ diff --git a/reference/listw.candidates.html b/reference/listw.candidates.html index 41e6001..2eb2171 100644 --- a/reference/listw.candidates.html +++ b/reference/listw.candidates.html @@ -216,7 +216,7 @@

Examples

weights = c("binary", "flin")) names(candidates) #> [1] "Gabriel_Binary" "Gabriel_Linear" "MST_Binary" -#> [4] "MST_Linear" "Dnear17.46_Binary" "Dnear17.46_Linear" +#> [4] "MST_Linear" "Dnear20.02_Binary" "Dnear20.02_Linear" plot(candidates[[1]], xy) plot(candidates[[3]], xy) diff --git a/reference/listw.select.html b/reference/listw.select.html index 0fd2f57..7020445 100644 --- a/reference/listw.select.html +++ b/reference/listw.select.html @@ -299,13 +299,12 @@

Examples

# See Appendix S3 of Bauman et al. 2018 for more extensive examples and illustrations. } -#> Warning: zero sum general weights -#> Procedure stopped (adjR2thresh criteria) adjR2cum = 0.915682 with 8 variables (> 0.912822) -#> Procedure stopped (adjR2thresh criteria) adjR2cum = 0.926847 with 7 variables (> 0.926704) -#> Procedure stopped (adjR2thresh criteria) adjR2cum = 0.884425 with 20 variables (> 0.881439) -#> Procedure stopped (alpha criteria): pvalue for variable 19 is 0.056667 (> 0.050000) +#> Procedure stopped (alpha criteria): pvalue for variable 12 is 0.056667 (> 0.050000) +#> Procedure stopped (adjR2thresh criteria) adjR2cum = 0.938034 with 17 variables (> 0.937661) +#> Procedure stopped (alpha criteria): pvalue for variable 15 is 0.053333 (> 0.050000) +#> Procedure stopped (adjR2thresh criteria) adjR2cum = 0.846676 with 18 variables (> 0.842630) #> Warning: no non-missing arguments to max; returning -Inf -#> [1] 0.9095298 0.9206866 0.8795889 0.8230682 +#> [1] 0.9237376 0.9353406 0.8308033 0.8418676 # } diff --git a/reference/mfpa-1.png b/reference/mfpa-1.png index 8c3c14b..551edf9 100644 Binary files a/reference/mfpa-1.png and b/reference/mfpa-1.png differ diff --git a/reference/mfpa-3.png b/reference/mfpa-3.png index 523628a..b7c6f12 100644 Binary files a/reference/mfpa-3.png and b/reference/mfpa-3.png differ diff --git a/reference/moran.randtest-1.png b/reference/moran.randtest-1.png index e116c4b..e633bf7 100644 Binary files a/reference/moran.randtest-1.png and b/reference/moran.randtest-1.png differ diff --git a/reference/msr.4thcorner.html b/reference/msr.4thcorner.html index c1ab291..b82a458 100644 --- a/reference/msr.4thcorner.html +++ b/reference/msr.4thcorner.html @@ -175,39 +175,39 @@

Examples

#> --- #> #> Test Stat Obs Std.Obs Alter Pvalue -#> 1 Clay / Lfp r 0.014241856 0.34116806 two-sided 0.84 -#> 2 Silt / Lfp r -0.076256133 -1.31383433 two-sided 0.16 -#> 3 Sand / Lfp r 0.063614861 1.01972615 two-sided 0.32 -#> 4 K2O / Lfp r 0.031979355 0.23640071 two-sided 0.9 -#> 5 Mg++ / Lfp r 0.014982765 0.36363692 two-sided 0.74 -#> 6 Na+/100g / Lfp r 0.093499930 1.16635287 two-sided 0.28 -#> 7 K+ / Lfp r 0.149968927 1.52836116 two-sided 0.12 -#> 8 Conductivity / Lfp r 0.080497038 1.06839106 two-sided 0.3 -#> 9 Retention / Lfp r -0.067943687 -1.10780289 two-sided 0.34 -#> 10 Na+/l / Lfp r 0.063272819 0.91718667 two-sided 0.36 -#> 11 Elevation / Lfp r -0.129537428 -1.46134630 two-sided 0.16 -#> 12 Clay / Min height r 0.061180871 1.52126026 two-sided 0.2 -#> 13 Silt / Min height r -0.052382325 -1.03179111 two-sided 0.36 -#> 14 Sand / Min height r -0.027907397 -0.65552804 two-sided 0.52 -#> 15 K2O / Min height r 0.047550545 1.13392424 two-sided 0.22 -#> 16 Mg++ / Min height r -0.023023055 -0.31112046 two-sided 0.66 -#> 17 Na+/100g / Min height r 0.027191299 0.55103246 two-sided 0.62 -#> 18 K+ / Min height r 0.095029793 1.61696100 two-sided 0.14 -#> 19 Conductivity / Min height r -0.018899856 -0.14195947 two-sided 0.82 -#> 20 Retention / Min height r 0.004165428 0.07211241 two-sided 1 -#> 21 Na+/l / Min height r -0.046119268 -0.58426177 two-sided 0.58 -#> 22 Elevation / Min height r 0.061211127 0.77002516 two-sided 0.44 -#> 23 Clay / Max height r 0.010762151 0.17385694 two-sided 0.82 -#> 24 Silt / Max height r -0.030835126 -0.64606621 two-sided 0.56 -#> 25 Sand / Max height r 0.027229507 0.65099749 two-sided 0.6 -#> 26 K2O / Max height r 0.063923177 1.26449188 two-sided 0.3 -#> 27 Mg++ / Max height r 0.010886210 0.61868203 two-sided 0.62 -#> 28 Na+/100g / Max height r 0.026525395 0.47370113 two-sided 0.66 -#> 29 K+ / Max height r 0.109401849 1.38425669 two-sided 0.16 -#> 30 Conductivity / Max height r 0.002446542 0.24410341 two-sided 0.82 -#> 31 Retention / Max height r -0.005456736 -0.14343797 two-sided 0.86 -#> 32 Na+/l / Max height r -0.018577793 -0.17855453 two-sided 0.82 -#> 33 Elevation / Max height r 0.033495574 0.58694427 two-sided 0.54 +#> 1 Clay / Lfp r 0.014241856 0.15981546 two-sided 0.88 +#> 2 Silt / Lfp r -0.076256133 -1.11761758 two-sided 0.32 +#> 3 Sand / Lfp r 0.063614861 1.03550793 two-sided 0.32 +#> 4 K2O / Lfp r 0.031979355 0.66201896 two-sided 0.58 +#> 5 Mg++ / Lfp r 0.014982765 0.39901360 two-sided 0.64 +#> 6 Na+/100g / Lfp r 0.093499930 1.24792875 two-sided 0.24 +#> 7 K+ / Lfp r 0.149968927 1.60453877 two-sided 0.06 +#> 8 Conductivity / Lfp r 0.080497038 1.00391888 two-sided 0.34 +#> 9 Retention / Lfp r -0.067943687 -1.07681456 two-sided 0.32 +#> 10 Na+/l / Lfp r 0.063272819 0.85693300 two-sided 0.42 +#> 11 Elevation / Lfp r -0.129537428 -1.42071913 two-sided 0.24 +#> 12 Clay / Min height r 0.061180871 1.43938745 two-sided 0.18 +#> 13 Silt / Min height r -0.052382325 -1.12053462 two-sided 0.3 +#> 14 Sand / Min height r -0.027907397 -0.71329513 two-sided 0.5 +#> 15 K2O / Min height r 0.047550545 1.18431368 two-sided 0.24 +#> 16 Mg++ / Min height r -0.023023055 -0.45527181 two-sided 0.68 +#> 17 Na+/100g / Min height r 0.027191299 0.68827226 two-sided 0.64 +#> 18 K+ / Min height r 0.095029793 1.84674253 two-sided 0.08 +#> 19 Conductivity / Min height r -0.018899856 -0.09870727 two-sided 0.98 +#> 20 Retention / Min height r 0.004165428 0.19270013 two-sided 0.88 +#> 21 Na+/l / Min height r -0.046119268 -0.61815501 two-sided 0.58 +#> 22 Elevation / Min height r 0.061211127 0.98645053 two-sided 0.36 +#> 23 Clay / Max height r 0.010762151 0.32004552 two-sided 0.82 +#> 24 Silt / Max height r -0.030835126 -0.64012989 two-sided 0.56 +#> 25 Sand / Max height r 0.027229507 0.50142089 two-sided 0.58 +#> 26 K2O / Max height r 0.063923177 1.35657708 two-sided 0.14 +#> 27 Mg++ / Max height r 0.010886210 0.35786332 two-sided 0.76 +#> 28 Na+/100g / Max height r 0.026525395 0.60061480 two-sided 0.6 +#> 29 K+ / Max height r 0.109401849 1.75428667 two-sided 0.08 +#> 30 Conductivity / Max height r 0.002446542 0.17629178 two-sided 0.9 +#> 31 Retention / Max height r -0.005456736 -0.12966359 two-sided 0.86 +#> 32 Na+/l / Max height r -0.018577793 -0.29114705 two-sided 0.84 +#> 33 Elevation / Max height r 0.033495574 0.59448183 two-sided 0.56 #> Pvalue.adj #> 1 1 #> 2 1 diff --git a/reference/msr.html b/reference/msr.html index bd89702..8d63060 100644 --- a/reference/msr.html +++ b/reference/msr.html @@ -177,32 +177,32 @@

Examples

lw1 <- nb2listw(cell2nb(9, 9)) moran.mc(x1[,1], lw1, 2)$statistic -#> statistic -#> 0.04531878 +#> statistic +#> -0.03639357 ## singleton x1.1 <- msr(x1[,1], lw1, nrepet = 9, method = "singleton") apply(x1.1, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic) -#> [1] 0.04531878 0.04531878 0.04531878 0.04531878 0.04531878 0.04531878 0.04531878 -#> [8] 0.04531878 0.04531878 +#> [1] -0.03639357 -0.03639357 -0.03639357 -0.03639357 -0.03639357 -0.03639357 +#> [7] -0.03639357 -0.03639357 -0.03639357 ## triplet x1.2 <- msr(x1[,1], lw1, nrepet = 9, method = "triplet") apply(x1.2, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic) -#> [1] 0.04531878 0.04531878 0.04531878 0.04531878 0.04531878 0.04531878 0.04531878 -#> [8] 0.04531878 0.04531878 +#> [1] -0.03639357 -0.03639357 -0.03639357 -0.03639357 -0.03639357 -0.03639357 +#> [7] -0.03639357 -0.03639357 -0.03639357 ## pair x1.3 <- msr(x1[,1], lw1, nrepet = 9, method = "pair") apply(x1.3, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic) -#> [1] 0.03715568 0.04275092 0.03674746 0.04385720 0.04670547 0.03513355 0.04130996 -#> [8] 0.04125028 0.03844362 +#> [1] -0.03221325 -0.02994351 -0.03814300 -0.03681758 -0.03739308 -0.03199512 +#> [7] -0.03304550 -0.03573920 -0.03205419 ## pair with cor.fixed x1.4 <- msr(x1[,1], lw1, nrepet = 9, cor.fixed = 0.5) apply(x1.4, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic) -#> [1] 0.03601004 0.04344145 0.04372924 0.03797438 0.04209478 0.04314482 0.03686685 -#> [8] 0.04262874 0.04513779 +#> [1] -0.03081582 -0.03521671 -0.03645620 -0.03138562 -0.03821900 -0.03679121 +#> [7] -0.03321552 -0.03577997 -0.03207838 cor(x1[,1], x1.4) #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] #> [1,] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 @@ -210,178 +210,178 @@

Examples

## pair preserving correlations for multivariate data x1.5 <- msr(x1, lw1, nrepet = 9, cor.fixed = 0.5) cor(x1) -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.0000000 0.10523057 0.12117802 0.11768437 0.15868367 -#> [2,] 0.1052306 1.00000000 0.04010823 -0.26752332 0.11007336 -#> [3,] 0.1211780 0.04010823 1.00000000 -0.04003409 0.19725909 -#> [4,] 0.1176844 -0.26752332 -0.04003409 1.00000000 0.09007414 -#> [5,] 0.1586837 0.11007336 0.19725909 0.09007414 1.00000000 +#> [,1] [,2] [,3] [,4] [,5] +#> [1,] 1.00000000 0.08956080 0.35454684 -0.11295047 -0.00366931 +#> [2,] 0.08956080 1.00000000 -0.03499248 0.02751043 -0.11022267 +#> [3,] 0.35454684 -0.03499248 1.00000000 -0.06411345 0.03575739 +#> [4,] -0.11295047 0.02751043 -0.06411345 1.00000000 -0.06091456 +#> [5,] -0.00366931 -0.11022267 0.03575739 -0.06091456 1.00000000 lapply(x1.5, cor) #> [[1]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.0000000 0.10523057 0.12117802 0.11768437 0.15868367 -#> [2,] 0.1052306 1.00000000 0.04010823 -0.26752332 0.11007336 -#> [3,] 0.1211780 0.04010823 1.00000000 -0.04003409 0.19725909 -#> [4,] 0.1176844 -0.26752332 -0.04003409 1.00000000 0.09007414 -#> [5,] 0.1586837 0.11007336 0.19725909 0.09007414 1.00000000 +#> [,1] [,2] [,3] [,4] [,5] +#> [1,] 1.00000000 0.08956080 0.35454684 -0.11295047 -0.00366931 +#> [2,] 0.08956080 1.00000000 -0.03499248 0.02751043 -0.11022267 +#> [3,] 0.35454684 -0.03499248 1.00000000 -0.06411345 0.03575739 +#> [4,] -0.11295047 0.02751043 -0.06411345 1.00000000 -0.06091456 +#> [5,] -0.00366931 -0.11022267 0.03575739 -0.06091456 1.00000000 #> #> [[2]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.0000000 0.10523057 0.12117802 0.11768437 0.15868367 -#> [2,] 0.1052306 1.00000000 0.04010823 -0.26752332 0.11007336 -#> [3,] 0.1211780 0.04010823 1.00000000 -0.04003409 0.19725909 -#> [4,] 0.1176844 -0.26752332 -0.04003409 1.00000000 0.09007414 -#> [5,] 0.1586837 0.11007336 0.19725909 0.09007414 1.00000000 +#> [,1] [,2] [,3] [,4] [,5] +#> [1,] 1.00000000 0.08956080 0.35454684 -0.11295047 -0.00366931 +#> [2,] 0.08956080 1.00000000 -0.03499248 0.02751043 -0.11022267 +#> [3,] 0.35454684 -0.03499248 1.00000000 -0.06411345 0.03575739 +#> [4,] -0.11295047 0.02751043 -0.06411345 1.00000000 -0.06091456 +#> [5,] -0.00366931 -0.11022267 0.03575739 -0.06091456 1.00000000 #> #> [[3]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.0000000 0.10523057 0.12117802 0.11768437 0.15868367 -#> [2,] 0.1052306 1.00000000 0.04010823 -0.26752332 0.11007336 -#> [3,] 0.1211780 0.04010823 1.00000000 -0.04003409 0.19725909 -#> [4,] 0.1176844 -0.26752332 -0.04003409 1.00000000 0.09007414 -#> [5,] 0.1586837 0.11007336 0.19725909 0.09007414 1.00000000 +#> [,1] [,2] [,3] [,4] [,5] +#> [1,] 1.00000000 0.08956080 0.35454684 -0.11295047 -0.00366931 +#> [2,] 0.08956080 1.00000000 -0.03499248 0.02751043 -0.11022267 +#> [3,] 0.35454684 -0.03499248 1.00000000 -0.06411345 0.03575739 +#> [4,] -0.11295047 0.02751043 -0.06411345 1.00000000 -0.06091456 +#> [5,] -0.00366931 -0.11022267 0.03575739 -0.06091456 1.00000000 #> #> [[4]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.0000000 0.10523057 0.12117802 0.11768437 0.15868367 -#> [2,] 0.1052306 1.00000000 0.04010823 -0.26752332 0.11007336 -#> [3,] 0.1211780 0.04010823 1.00000000 -0.04003409 0.19725909 -#> [4,] 0.1176844 -0.26752332 -0.04003409 1.00000000 0.09007414 -#> [5,] 0.1586837 0.11007336 0.19725909 0.09007414 1.00000000 +#> [,1] [,2] [,3] [,4] [,5] +#> [1,] 1.00000000 0.08956080 0.35454684 -0.11295047 -0.00366931 +#> [2,] 0.08956080 1.00000000 -0.03499248 0.02751043 -0.11022267 +#> [3,] 0.35454684 -0.03499248 1.00000000 -0.06411345 0.03575739 +#> [4,] -0.11295047 0.02751043 -0.06411345 1.00000000 -0.06091456 +#> [5,] -0.00366931 -0.11022267 0.03575739 -0.06091456 1.00000000 #> #> [[5]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.0000000 0.10523057 0.12117802 0.11768437 0.15868367 -#> [2,] 0.1052306 1.00000000 0.04010823 -0.26752332 0.11007336 -#> [3,] 0.1211780 0.04010823 1.00000000 -0.04003409 0.19725909 -#> [4,] 0.1176844 -0.26752332 -0.04003409 1.00000000 0.09007414 -#> [5,] 0.1586837 0.11007336 0.19725909 0.09007414 1.00000000 +#> [,1] [,2] [,3] [,4] [,5] +#> [1,] 1.00000000 0.08956080 0.35454684 -0.11295047 -0.00366931 +#> [2,] 0.08956080 1.00000000 -0.03499248 0.02751043 -0.11022267 +#> [3,] 0.35454684 -0.03499248 1.00000000 -0.06411345 0.03575739 +#> [4,] -0.11295047 0.02751043 -0.06411345 1.00000000 -0.06091456 +#> [5,] -0.00366931 -0.11022267 0.03575739 -0.06091456 1.00000000 #> #> [[6]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.0000000 0.10523057 0.12117802 0.11768437 0.15868367 -#> [2,] 0.1052306 1.00000000 0.04010823 -0.26752332 0.11007336 -#> [3,] 0.1211780 0.04010823 1.00000000 -0.04003409 0.19725909 -#> [4,] 0.1176844 -0.26752332 -0.04003409 1.00000000 0.09007414 -#> [5,] 0.1586837 0.11007336 0.19725909 0.09007414 1.00000000 +#> [,1] [,2] [,3] [,4] [,5] +#> [1,] 1.00000000 0.08956080 0.35454684 -0.11295047 -0.00366931 +#> [2,] 0.08956080 1.00000000 -0.03499248 0.02751043 -0.11022267 +#> [3,] 0.35454684 -0.03499248 1.00000000 -0.06411345 0.03575739 +#> [4,] -0.11295047 0.02751043 -0.06411345 1.00000000 -0.06091456 +#> [5,] -0.00366931 -0.11022267 0.03575739 -0.06091456 1.00000000 #> #> [[7]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.0000000 0.10523057 0.12117802 0.11768437 0.15868367 -#> [2,] 0.1052306 1.00000000 0.04010823 -0.26752332 0.11007336 -#> [3,] 0.1211780 0.04010823 1.00000000 -0.04003409 0.19725909 -#> [4,] 0.1176844 -0.26752332 -0.04003409 1.00000000 0.09007414 -#> [5,] 0.1586837 0.11007336 0.19725909 0.09007414 1.00000000 +#> [,1] [,2] [,3] [,4] [,5] +#> [1,] 1.00000000 0.08956080 0.35454684 -0.11295047 -0.00366931 +#> [2,] 0.08956080 1.00000000 -0.03499248 0.02751043 -0.11022267 +#> [3,] 0.35454684 -0.03499248 1.00000000 -0.06411345 0.03575739 +#> [4,] -0.11295047 0.02751043 -0.06411345 1.00000000 -0.06091456 +#> [5,] -0.00366931 -0.11022267 0.03575739 -0.06091456 1.00000000 #> #> [[8]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.0000000 0.10523057 0.12117802 0.11768437 0.15868367 -#> [2,] 0.1052306 1.00000000 0.04010823 -0.26752332 0.11007336 -#> [3,] 0.1211780 0.04010823 1.00000000 -0.04003409 0.19725909 -#> [4,] 0.1176844 -0.26752332 -0.04003409 1.00000000 0.09007414 -#> [5,] 0.1586837 0.11007336 0.19725909 0.09007414 1.00000000 +#> [,1] [,2] [,3] [,4] [,5] +#> [1,] 1.00000000 0.08956080 0.35454684 -0.11295047 -0.00366931 +#> [2,] 0.08956080 1.00000000 -0.03499248 0.02751043 -0.11022267 +#> [3,] 0.35454684 -0.03499248 1.00000000 -0.06411345 0.03575739 +#> [4,] -0.11295047 0.02751043 -0.06411345 1.00000000 -0.06091456 +#> [5,] -0.00366931 -0.11022267 0.03575739 -0.06091456 1.00000000 #> #> [[9]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.0000000 0.10523057 0.12117802 0.11768437 0.15868367 -#> [2,] 0.1052306 1.00000000 0.04010823 -0.26752332 0.11007336 -#> [3,] 0.1211780 0.04010823 1.00000000 -0.04003409 0.19725909 -#> [4,] 0.1176844 -0.26752332 -0.04003409 1.00000000 0.09007414 -#> [5,] 0.1586837 0.11007336 0.19725909 0.09007414 1.00000000 +#> [,1] [,2] [,3] [,4] [,5] +#> [1,] 1.00000000 0.08956080 0.35454684 -0.11295047 -0.00366931 +#> [2,] 0.08956080 1.00000000 -0.03499248 0.02751043 -0.11022267 +#> [3,] 0.35454684 -0.03499248 1.00000000 -0.06411345 0.03575739 +#> [4,] -0.11295047 0.02751043 -0.06411345 1.00000000 -0.06091456 +#> [5,] -0.00366931 -0.11022267 0.03575739 -0.06091456 1.00000000 #> apply(x1, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic) -#> [1] 0.04531878 -0.07609907 -0.02773368 0.07468736 0.12705310 +#> [1] -0.036393570 0.003795524 -0.125770324 -0.108078457 0.011237403 apply(x1.5[[1]], 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic) -#> [1] 0.04165888 -0.07380134 -0.02154848 0.06854768 0.13557780 +#> [1] -0.030109453 0.002228235 -0.116894243 -0.105638622 0.015747495 ## singleton preserving correlations for multivariate data x1.6 <- msr(x1, lw1, nrepet = 9, method = "singleton") cor(x1) -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.0000000 0.10523057 0.12117802 0.11768437 0.15868367 -#> [2,] 0.1052306 1.00000000 0.04010823 -0.26752332 0.11007336 -#> [3,] 0.1211780 0.04010823 1.00000000 -0.04003409 0.19725909 -#> [4,] 0.1176844 -0.26752332 -0.04003409 1.00000000 0.09007414 -#> [5,] 0.1586837 0.11007336 0.19725909 0.09007414 1.00000000 +#> [,1] [,2] [,3] [,4] [,5] +#> [1,] 1.00000000 0.08956080 0.35454684 -0.11295047 -0.00366931 +#> [2,] 0.08956080 1.00000000 -0.03499248 0.02751043 -0.11022267 +#> [3,] 0.35454684 -0.03499248 1.00000000 -0.06411345 0.03575739 +#> [4,] -0.11295047 0.02751043 -0.06411345 1.00000000 -0.06091456 +#> [5,] -0.00366931 -0.11022267 0.03575739 -0.06091456 1.00000000 lapply(x1.6, cor) #> [[1]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.0000000 0.10523057 0.12117802 0.11768437 0.15868367 -#> [2,] 0.1052306 1.00000000 0.04010823 -0.26752332 0.11007336 -#> [3,] 0.1211780 0.04010823 1.00000000 -0.04003409 0.19725909 -#> [4,] 0.1176844 -0.26752332 -0.04003409 1.00000000 0.09007414 -#> [5,] 0.1586837 0.11007336 0.19725909 0.09007414 1.00000000 +#> [,1] [,2] [,3] [,4] [,5] +#> [1,] 1.00000000 0.08956080 0.35454684 -0.11295047 -0.00366931 +#> [2,] 0.08956080 1.00000000 -0.03499248 0.02751043 -0.11022267 +#> [3,] 0.35454684 -0.03499248 1.00000000 -0.06411345 0.03575739 +#> [4,] -0.11295047 0.02751043 -0.06411345 1.00000000 -0.06091456 +#> [5,] -0.00366931 -0.11022267 0.03575739 -0.06091456 1.00000000 #> #> [[2]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.0000000 0.10523057 0.12117802 0.11768437 0.15868367 -#> [2,] 0.1052306 1.00000000 0.04010823 -0.26752332 0.11007336 -#> [3,] 0.1211780 0.04010823 1.00000000 -0.04003409 0.19725909 -#> [4,] 0.1176844 -0.26752332 -0.04003409 1.00000000 0.09007414 -#> [5,] 0.1586837 0.11007336 0.19725909 0.09007414 1.00000000 +#> [,1] [,2] [,3] [,4] [,5] +#> [1,] 1.00000000 0.08956080 0.35454684 -0.11295047 -0.00366931 +#> [2,] 0.08956080 1.00000000 -0.03499248 0.02751043 -0.11022267 +#> [3,] 0.35454684 -0.03499248 1.00000000 -0.06411345 0.03575739 +#> [4,] -0.11295047 0.02751043 -0.06411345 1.00000000 -0.06091456 +#> [5,] -0.00366931 -0.11022267 0.03575739 -0.06091456 1.00000000 #> #> [[3]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.0000000 0.10523057 0.12117802 0.11768437 0.15868367 -#> [2,] 0.1052306 1.00000000 0.04010823 -0.26752332 0.11007336 -#> [3,] 0.1211780 0.04010823 1.00000000 -0.04003409 0.19725909 -#> [4,] 0.1176844 -0.26752332 -0.04003409 1.00000000 0.09007414 -#> [5,] 0.1586837 0.11007336 0.19725909 0.09007414 1.00000000 +#> [,1] [,2] [,3] [,4] [,5] +#> [1,] 1.00000000 0.08956080 0.35454684 -0.11295047 -0.00366931 +#> [2,] 0.08956080 1.00000000 -0.03499248 0.02751043 -0.11022267 +#> [3,] 0.35454684 -0.03499248 1.00000000 -0.06411345 0.03575739 +#> [4,] -0.11295047 0.02751043 -0.06411345 1.00000000 -0.06091456 +#> [5,] -0.00366931 -0.11022267 0.03575739 -0.06091456 1.00000000 #> #> [[4]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.0000000 0.10523057 0.12117802 0.11768437 0.15868367 -#> [2,] 0.1052306 1.00000000 0.04010823 -0.26752332 0.11007336 -#> [3,] 0.1211780 0.04010823 1.00000000 -0.04003409 0.19725909 -#> [4,] 0.1176844 -0.26752332 -0.04003409 1.00000000 0.09007414 -#> [5,] 0.1586837 0.11007336 0.19725909 0.09007414 1.00000000 +#> [,1] [,2] [,3] [,4] [,5] +#> [1,] 1.00000000 0.08956080 0.35454684 -0.11295047 -0.00366931 +#> [2,] 0.08956080 1.00000000 -0.03499248 0.02751043 -0.11022267 +#> [3,] 0.35454684 -0.03499248 1.00000000 -0.06411345 0.03575739 +#> [4,] -0.11295047 0.02751043 -0.06411345 1.00000000 -0.06091456 +#> [5,] -0.00366931 -0.11022267 0.03575739 -0.06091456 1.00000000 #> #> [[5]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.0000000 0.10523057 0.12117802 0.11768437 0.15868367 -#> [2,] 0.1052306 1.00000000 0.04010823 -0.26752332 0.11007336 -#> [3,] 0.1211780 0.04010823 1.00000000 -0.04003409 0.19725909 -#> [4,] 0.1176844 -0.26752332 -0.04003409 1.00000000 0.09007414 -#> [5,] 0.1586837 0.11007336 0.19725909 0.09007414 1.00000000 +#> [,1] [,2] [,3] [,4] [,5] +#> [1,] 1.00000000 0.08956080 0.35454684 -0.11295047 -0.00366931 +#> [2,] 0.08956080 1.00000000 -0.03499248 0.02751043 -0.11022267 +#> [3,] 0.35454684 -0.03499248 1.00000000 -0.06411345 0.03575739 +#> [4,] -0.11295047 0.02751043 -0.06411345 1.00000000 -0.06091456 +#> [5,] -0.00366931 -0.11022267 0.03575739 -0.06091456 1.00000000 #> #> [[6]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.0000000 0.10523057 0.12117802 0.11768437 0.15868367 -#> [2,] 0.1052306 1.00000000 0.04010823 -0.26752332 0.11007336 -#> [3,] 0.1211780 0.04010823 1.00000000 -0.04003409 0.19725909 -#> [4,] 0.1176844 -0.26752332 -0.04003409 1.00000000 0.09007414 -#> [5,] 0.1586837 0.11007336 0.19725909 0.09007414 1.00000000 +#> [,1] [,2] [,3] [,4] [,5] +#> [1,] 1.00000000 0.08956080 0.35454684 -0.11295047 -0.00366931 +#> [2,] 0.08956080 1.00000000 -0.03499248 0.02751043 -0.11022267 +#> [3,] 0.35454684 -0.03499248 1.00000000 -0.06411345 0.03575739 +#> [4,] -0.11295047 0.02751043 -0.06411345 1.00000000 -0.06091456 +#> [5,] -0.00366931 -0.11022267 0.03575739 -0.06091456 1.00000000 #> #> [[7]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.0000000 0.10523057 0.12117802 0.11768437 0.15868367 -#> [2,] 0.1052306 1.00000000 0.04010823 -0.26752332 0.11007336 -#> [3,] 0.1211780 0.04010823 1.00000000 -0.04003409 0.19725909 -#> [4,] 0.1176844 -0.26752332 -0.04003409 1.00000000 0.09007414 -#> [5,] 0.1586837 0.11007336 0.19725909 0.09007414 1.00000000 +#> [,1] [,2] [,3] [,4] [,5] +#> [1,] 1.00000000 0.08956080 0.35454684 -0.11295047 -0.00366931 +#> [2,] 0.08956080 1.00000000 -0.03499248 0.02751043 -0.11022267 +#> [3,] 0.35454684 -0.03499248 1.00000000 -0.06411345 0.03575739 +#> [4,] -0.11295047 0.02751043 -0.06411345 1.00000000 -0.06091456 +#> [5,] -0.00366931 -0.11022267 0.03575739 -0.06091456 1.00000000 #> #> [[8]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.0000000 0.10523057 0.12117802 0.11768437 0.15868367 -#> [2,] 0.1052306 1.00000000 0.04010823 -0.26752332 0.11007336 -#> [3,] 0.1211780 0.04010823 1.00000000 -0.04003409 0.19725909 -#> [4,] 0.1176844 -0.26752332 -0.04003409 1.00000000 0.09007414 -#> [5,] 0.1586837 0.11007336 0.19725909 0.09007414 1.00000000 +#> [,1] [,2] [,3] [,4] [,5] +#> [1,] 1.00000000 0.08956080 0.35454684 -0.11295047 -0.00366931 +#> [2,] 0.08956080 1.00000000 -0.03499248 0.02751043 -0.11022267 +#> [3,] 0.35454684 -0.03499248 1.00000000 -0.06411345 0.03575739 +#> [4,] -0.11295047 0.02751043 -0.06411345 1.00000000 -0.06091456 +#> [5,] -0.00366931 -0.11022267 0.03575739 -0.06091456 1.00000000 #> #> [[9]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.0000000 0.10523057 0.12117802 0.11768437 0.15868367 -#> [2,] 0.1052306 1.00000000 0.04010823 -0.26752332 0.11007336 -#> [3,] 0.1211780 0.04010823 1.00000000 -0.04003409 0.19725909 -#> [4,] 0.1176844 -0.26752332 -0.04003409 1.00000000 0.09007414 -#> [5,] 0.1586837 0.11007336 0.19725909 0.09007414 1.00000000 +#> [,1] [,2] [,3] [,4] [,5] +#> [1,] 1.00000000 0.08956080 0.35454684 -0.11295047 -0.00366931 +#> [2,] 0.08956080 1.00000000 -0.03499248 0.02751043 -0.11022267 +#> [3,] 0.35454684 -0.03499248 1.00000000 -0.06411345 0.03575739 +#> [4,] -0.11295047 0.02751043 -0.06411345 1.00000000 -0.06091456 +#> [5,] -0.00366931 -0.11022267 0.03575739 -0.06091456 1.00000000 #> apply(x1, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic) -#> [1] 0.04531878 -0.07609907 -0.02773368 0.07468736 0.12705310 +#> [1] -0.036393570 0.003795524 -0.125770324 -0.108078457 0.011237403 apply(x1.6[[1]], 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic) -#> [1] 0.04531878 -0.07609907 -0.02773368 0.07468736 0.12705310 +#> [1] -0.036393570 0.003795524 -0.125770324 -0.108078457 0.011237403 diff --git a/reference/msr.mantelrtest.html b/reference/msr.mantelrtest.html index e90ebcf..3749d57 100644 --- a/reference/msr.mantelrtest.html +++ b/reference/msr.mantelrtest.html @@ -153,11 +153,11 @@

Examples

#> Observation: 0.1197583 #> #> Based on 999 replicates -#> Simulated p-value: 0.193 +#> Simulated p-value: 0.207 #> Alternative hypothesis: greater #> #> Std.Obs Expectation Variance -#> 0.869789570 0.042607964 0.007867696 +#> 0.746684848 0.050906679 0.008502644 diff --git a/reference/msr.varipart.html b/reference/msr.varipart.html index f30a073..ff31014 100644 --- a/reference/msr.varipart.html +++ b/reference/msr.varipart.html @@ -162,11 +162,11 @@

Examples

#> Observation: 0.2366554 #> #> Based on 99 replicates -#> Simulated p-value: 0.01 +#> Simulated p-value: 0.02 #> Alternative hypothesis: greater #> #> Std.Obs Expectation Variance -#> 2.6097801213 0.1729625437 0.0005956257 +#> 2.6146724878 0.1746737065 0.0005619428 #> #> Individual fractions: #> a b c d @@ -174,7 +174,7 @@

Examples

#> #> Adjusted fractions: #> a b c d -#> -0.01230070 0.08931395 0.25672378 0.66626297 +#> -0.01181311 0.08691272 0.25912501 0.66577538 diff --git a/reference/mst.nb-1.png b/reference/mst.nb-1.png index 0050220..b7b7ebf 100644 Binary files a/reference/mst.nb-1.png and b/reference/mst.nb-1.png differ diff --git a/reference/ortho.AIC.html b/reference/ortho.AIC.html index 53c7c57..1fddc6d 100644 --- a/reference/ortho.AIC.html +++ b/reference/ortho.AIC.html @@ -129,24 +129,24 @@

Examples

nvar <- length(1:minAIC)+1 # number of orthogonal vectors + 1 for intercept lm1 <- lm(y~x[,res$ord[1:minAIC]]) summary(lm1)$r.squared # R2 -#> [1] 0.9099463 +#> [1] 0.8310555 res$R2[minAIC] # the same -#> [1] 0.9099463 +#> [1] 0.8310555 min(res$AICc) # corrected AIC -#> [1] -101.7769 +#> [1] -78.7284 extractAIC(lm1) # classical AIC -#> [1] 4.0000 -102.6658 +#> [1] 5.00000 -80.09203 min(res$AICc)-2*(nvar*(nvar+1))/(nrow(x)-nvar-1) # the same -#> [1] -102.6658 +#> [1] -80.09203 lm2 <- lm(y~1) res$AICc0 # corrected AIC for the null model -#> [1] 11.78498 +#> [1] 0.9005399 extractAIC(lm2) # classical AIC -#> [1] 1.00000 11.70165 +#> [1] 1.0000000 0.8172066 res$AICc0-2*(1*(1+1))/(nrow(x)-1-1) # the same -#> [1] 11.70165 +#> [1] 0.8172066 diff --git a/reference/rotation-1.png b/reference/rotation-1.png index 6830b37..d669b94 100644 Binary files a/reference/rotation-1.png and b/reference/rotation-1.png differ diff --git a/reference/rotation-10.png b/reference/rotation-10.png index c7f5311..8fa36d1 100644 Binary files a/reference/rotation-10.png and b/reference/rotation-10.png differ diff --git a/reference/rotation-11.png b/reference/rotation-11.png index 1766f3a..aa939aa 100644 Binary files a/reference/rotation-11.png and b/reference/rotation-11.png differ diff --git a/reference/rotation-12.png b/reference/rotation-12.png index 75cb232..4f2f110 100644 Binary files a/reference/rotation-12.png and b/reference/rotation-12.png differ diff --git a/reference/rotation-13.png b/reference/rotation-13.png index 88c15e0..bf1f27e 100644 Binary files a/reference/rotation-13.png and b/reference/rotation-13.png differ diff --git a/reference/rotation-14.png b/reference/rotation-14.png index b672fd8..1e7d5b5 100644 Binary files a/reference/rotation-14.png and b/reference/rotation-14.png differ diff --git a/reference/rotation-15.png b/reference/rotation-15.png index 1f94066..8a871ef 100644 Binary files a/reference/rotation-15.png and b/reference/rotation-15.png differ diff --git a/reference/rotation-16.png b/reference/rotation-16.png index e53e584..5d71330 100644 Binary files a/reference/rotation-16.png and b/reference/rotation-16.png differ diff --git a/reference/rotation-17.png b/reference/rotation-17.png index d59dc9e..53d156f 100644 Binary files a/reference/rotation-17.png and b/reference/rotation-17.png differ diff --git a/reference/rotation-18.png b/reference/rotation-18.png index 1bcc37f..4f3d94a 100644 Binary files a/reference/rotation-18.png and b/reference/rotation-18.png differ diff --git a/reference/rotation-19.png b/reference/rotation-19.png index e4adfdb..62f3012 100644 Binary files a/reference/rotation-19.png and b/reference/rotation-19.png differ diff --git a/reference/rotation-2.png b/reference/rotation-2.png index 4cf5b62..eb27ce6 100644 Binary files a/reference/rotation-2.png and b/reference/rotation-2.png differ diff --git a/reference/rotation-20.png b/reference/rotation-20.png index 09f3097..9e2668b 100644 Binary files a/reference/rotation-20.png and b/reference/rotation-20.png differ diff --git a/reference/rotation-21.png b/reference/rotation-21.png index a95d907..07992a0 100644 Binary files a/reference/rotation-21.png and b/reference/rotation-21.png differ diff --git a/reference/rotation-3.png b/reference/rotation-3.png index 68f02c7..68b2f5e 100644 Binary files a/reference/rotation-3.png and b/reference/rotation-3.png differ diff --git a/reference/rotation-4.png b/reference/rotation-4.png index 019ea12..ce35ef4 100644 Binary files a/reference/rotation-4.png and b/reference/rotation-4.png differ diff --git a/reference/rotation-5.png b/reference/rotation-5.png index f3f0b46..e2203a3 100644 Binary files a/reference/rotation-5.png and b/reference/rotation-5.png differ diff --git a/reference/rotation-6.png b/reference/rotation-6.png index 0e37e58..c85a100 100644 Binary files a/reference/rotation-6.png and b/reference/rotation-6.png differ diff --git a/reference/rotation-7.png b/reference/rotation-7.png index 1a28d0b..1a16081 100644 Binary files a/reference/rotation-7.png and b/reference/rotation-7.png differ diff --git a/reference/rotation-8.png b/reference/rotation-8.png index da2440e..60c6252 100644 Binary files a/reference/rotation-8.png and b/reference/rotation-8.png differ diff --git a/reference/rotation-9.png b/reference/rotation-9.png index 570fbfe..3011d6b 100644 Binary files a/reference/rotation-9.png and b/reference/rotation-9.png differ diff --git a/reference/rotation.html b/reference/rotation.html index 14007c4..16c387b 100644 --- a/reference/rotation.html +++ b/reference/rotation.html @@ -125,26 +125,26 @@

Examples

coords.90<-rotation(coords,90*pi/180) coords.90 #> [,1] [,2] -#> [1,] -0.49436660 0.12146946 -#> [2,] -0.66756874 0.87881145 -#> [3,] -0.47219908 0.04498921 -#> [4,] -0.92011965 0.26161847 -#> [5,] -0.92036831 0.06570816 -#> [6,] -0.05435769 0.10695880 -#> [7,] -0.91876742 0.87386207 -#> [8,] -0.40758212 0.62272459 -#> [9,] -0.69247183 0.21117708 -#> [10,] -0.22037140 0.47192742 -#> [11,] -0.86430913 0.61567096 -#> [12,] -0.12851487 0.46261189 -#> [13,] -0.60337047 0.98628901 -#> [14,] -0.05932105 0.98570459 -#> [15,] -0.74161721 0.41266974 -#> [16,] -0.82125185 0.16802699 -#> [17,] -0.21323951 0.11368613 -#> [18,] -0.60717224 0.47117757 -#> [19,] -0.61174605 0.15719777 -#> [20,] -0.23961096 0.07063365 +#> [1,] -0.28374728 0.98923920 +#> [2,] -0.04633513 0.39377011 +#> [3,] -0.01377965 0.05113566 +#> [4,] -0.51962734 0.46812314 +#> [5,] -0.84724666 0.06252493 +#> [6,] -0.22623976 0.89712233 +#> [7,] -0.05130279 0.08959023 +#> [8,] -0.82374271 0.27166667 +#> [9,] -0.15504513 0.70649633 +#> [10,] -0.32467674 0.26759735 +#> [11,] -0.42556442 0.17219251 +#> [12,] -0.21220240 0.03810026 +#> [13,] -0.42789429 0.61548212 +#> [14,] -0.49993821 0.79052511 +#> [15,] -0.51375854 0.52703084 +#> [16,] -0.99336227 0.74967838 +#> [17,] -0.12121028 0.65250327 +#> [18,] -0.07505957 0.58096035 +#> [19,] -0.37882504 0.06843743 +#> [20,] -0.65982190 0.08314040 plot(coords,xlim=range(rbind(coords.90,coords)[,1]),ylim=range(rbind(coords.90,coords)[,2]),asp=1) points(coords.90,pch=19) diff --git a/reference/stimodels.html b/reference/stimodels.html index 3875576..3d55355 100644 --- a/reference/stimodels.html +++ b/reference/stimodels.html @@ -351,7 +351,7 @@

Examples

#> Time test: R2 = 0.3076 F = 22.0858 P( 99 perm) = 0.01 #> #> ------------------------------------------------------- -#> Time for computation = 0.793000 sec +#> Time for computation = 1.159000 sec #> ======================================================= #> @@ -419,7 +419,7 @@

Examples

#> Time test: R2 = 0.5265 F = 3.4186 P( 999 perm) = 0.001 #> #> --------------------------------------------------------- -#> Time for computation = 3.632000 sec +#> Time for computation = 4.264000 sec #> ========================================================= #> @@ -473,7 +473,7 @@

Examples

#> Time test: R2 = 0.045 F = 7.045 P( 999 perm) = 0.001 #> #> --------------------------------------------------------- -#> Time for computation = 0.559000 sec +#> Time for computation = 0.724000 sec #> ========================================================= #> @@ -513,7 +513,7 @@

Examples

#> Number of interaction variables = 10 #> Number of residual degrees of freedom = 11 #> -#> Interaction test: R2 = 0.1476 F = 1.5921 P( 999 perm) = 0.022 +#> Interaction test: R2 = 0.1476 F = 1.5921 P( 999 perm) = 0.014 #> ---------------------------------------------------- #> Testing for separate spatial structures (model 6a) #> ---------------------------------------------------- @@ -534,7 +534,7 @@

Examples

#> Model 6b requires that 'tt' be larger than 2. When tt=2, full coding of the times by a binary variable or Helmert contrast does not leave any degree of freedom for the residuals in the test of the Time factor. #> #> --------------------------------------------------------- -#> Time for computation = 0.640000 sec +#> Time for computation = 0.837000 sec #> ========================================================= #> @@ -584,7 +584,7 @@

Examples

#> Time test: R2 = 0.3076 F = 22.0858 P( 999 perm) = 0.001 #> #> ------------------------------------------------------- -#> Time for computation = 2.053000 sec +#> Time for computation = 2.474000 sec #> ======================================================= #> @@ -631,7 +631,7 @@

Examples

#> Time test: R2 = 0.3076 F = 18.8093 P( 999 perm) = 0.001 #> #> ------------------------------------------------------- -#> Time for computation = 2.451000 sec +#> Time for computation = 2.978000 sec #> ======================================================= #> @@ -674,7 +674,7 @@

Examples

#> Time test: R2 = 0.3076 F = 18.8093 P( 999 perm) = 0.001 #> #> ------------------------------------------------------- -#> Time for computation = 2.054000 sec +#> Time for computation = 2.420000 sec #> ======================================================= #> diff --git a/reference/tpaired.randtest.html b/reference/tpaired.randtest.html index 2a4928d..ae45e38 100644 --- a/reference/tpaired.randtest.html +++ b/reference/tpaired.randtest.html @@ -144,7 +144,7 @@

Examples

#> Degrees of freedom: 9 #> Alternative hypothesis: two.sided #> Prob (parametric): 0.007703223 -#> Prob ( 99 permutations): 0.01000 +#> Prob ( 99 permutations): 0.03000 ## Compare the results to: res2 = t.test(deer[,1], deer[,2], paired=TRUE)