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Examples

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ll(vHCv;U8*IXrJS)Q$qR#i0xZY6t?+xn+2>L=*n(e*qDM*Qo#i diff --git a/reference/create.dbMEM.model.html b/reference/create.dbMEM.model.html index bc19e13..1cb049a 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 -0.9525116 -0.42940974 0.0000000 0.00000000 0.0000000 0.00000000 -#> S2 0.8770175 0.09898209 0.0000000 0.00000000 0.0000000 0.00000000 -#> S3 -1.3958677 -0.52698846 0.0000000 0.00000000 0.0000000 0.00000000 -#> S4 1.2596367 -1.54188114 0.0000000 0.00000000 0.0000000 0.00000000 -#> S5 -0.5130143 0.83060806 0.0000000 0.00000000 0.0000000 0.00000000 -#> S6 0.7247394 1.56868919 0.0000000 0.00000000 0.0000000 0.00000000 -#> S7 0.0000000 0.00000000 1.0161387 -0.60522835 0.0000000 0.00000000 -#> S8 0.0000000 0.00000000 -1.6127650 0.02670681 0.0000000 0.00000000 -#> S9 0.0000000 0.00000000 0.6702304 -1.17794235 0.0000000 0.00000000 -#> S10 0.0000000 0.00000000 0.3907945 -0.77144272 0.0000000 0.00000000 -#> S11 0.0000000 0.00000000 0.1854734 1.82774379 0.0000000 0.00000000 -#> S12 0.0000000 0.00000000 0.7973284 1.07965389 0.0000000 0.00000000 -#> S13 0.0000000 0.00000000 -1.4472004 -0.37949106 0.0000000 0.00000000 -#> S14 0.0000000 0.00000000 0.0000000 0.00000000 -0.3096167 0.00000000 -#> S15 0.0000000 0.00000000 0.0000000 0.00000000 -0.8281028 0.00000000 -#> S16 0.0000000 0.00000000 0.0000000 0.00000000 -0.8252123 0.00000000 -#> S17 0.0000000 0.00000000 0.0000000 0.00000000 1.3290649 0.00000000 -#> S18 0.0000000 0.00000000 0.0000000 0.00000000 1.4505340 0.00000000 -#> S19 0.0000000 0.00000000 0.0000000 0.00000000 -0.8166672 0.00000000 -#> S20 0.0000000 0.00000000 0.0000000 0.00000000 0.0000000 -1.11801930 -#> S21 0.0000000 0.00000000 0.0000000 0.00000000 0.0000000 -1.11925706 -#> S22 0.0000000 0.00000000 0.0000000 0.00000000 0.0000000 1.11804863 -#> S23 0.0000000 0.00000000 0.0000000 0.00000000 0.0000000 0.00242073 -#> S24 0.0000000 0.00000000 0.0000000 0.00000000 0.0000000 1.11680700 -#> S25 0.0000000 0.00000000 0.0000000 0.00000000 0.0000000 0.00000000 -#> S26 0.0000000 0.00000000 0.0000000 0.00000000 0.0000000 0.00000000 -#> S27 0.0000000 0.00000000 0.0000000 0.00000000 0.0000000 0.00000000 -#> S28 0.0000000 0.00000000 0.0000000 0.00000000 0.0000000 0.00000000 -#> S29 0.0000000 0.00000000 0.0000000 0.00000000 0.0000000 0.00000000 -#> S30 0.0000000 0.00000000 0.0000000 0.00000000 0.0000000 0.00000000 -#> S31 0.0000000 0.00000000 0.0000000 0.00000000 0.0000000 0.00000000 -#> S32 0.0000000 0.00000000 0.0000000 0.00000000 0.0000000 0.00000000 -#> S33 0.0000000 0.00000000 0.0000000 0.00000000 0.0000000 0.00000000 -#> S34 0.0000000 0.00000000 0.0000000 0.00000000 0.0000000 0.00000000 -#> S35 0.0000000 0.00000000 0.0000000 0.00000000 0.0000000 0.00000000 -#> dbMEM.7 dbMEM.8 dbMEM.9 dbMEM.10 dbMEM.11 -#> S1 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S2 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S3 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S4 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S5 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S6 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S7 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S8 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S9 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S10 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S11 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S12 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S13 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S14 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S15 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S16 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S17 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S18 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S19 0.0000000 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S20 -0.3186046 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S21 1.0914138 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S22 -0.3169125 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S23 -1.5513819 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S24 1.0954852 0.0000000 0.0000000000 0.0000000 0.0000000 -#> S25 0.0000000 1.4256949 -0.0005359717 0.0000000 0.0000000 -#> S26 0.0000000 -0.8080852 1.6266560069 0.0000000 0.0000000 -#> S27 0.0000000 -0.8661121 -1.5328536215 0.0000000 0.0000000 -#> S28 0.0000000 -0.7513530 -0.0461798870 0.0000000 0.0000000 -#> S29 0.0000000 0.9998554 -0.0470865267 0.0000000 0.0000000 -#> S30 0.0000000 0.0000000 0.0000000000 1.0376746 1.0749071 -#> S31 0.0000000 0.0000000 0.0000000000 0.5463637 -1.3331463 -#> S32 0.0000000 0.0000000 0.0000000000 -1.0223537 1.0926111 -#> S33 0.0000000 0.0000000 0.0000000000 1.2791452 0.2358774 -#> S34 0.0000000 0.0000000 0.0000000000 -1.2732404 0.2539069 -#> S35 0.0000000 0.0000000 0.0000000000 -0.5675894 -1.3241562 +#> 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 diff --git a/reference/dbmem.html b/reference/dbmem.html index 2c286e2..f3810c3 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 = 0.024000 sec +#> Time to compute dbMEMs = 0.018000 sec diff --git a/reference/envspace.test.html b/reference/envspace.test.html index 860813a..214ed8d 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 0.079000 (> 0.050000) -#> Procedure stopped (alpha criteria): pvalue for variable 14 is 0.078000 (> 0.050000) -#> Procedure stopped (alpha criteria): pvalue for variable 13 is 0.059000 (> 0.050000) -#> Procedure stopped (alpha criteria): pvalue for variable 12 is 0.052000 (> 0.050000) +#> 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) #> 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 -#> -2.892464273 0.408747194 0.004465964 +#> -2.929908523 0.414013769 0.004592953 # } diff --git a/reference/forward.sel.html b/reference/forward.sel.html index 787514f..54a1dde 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 0.620000 (> 0.500000) -#> variables order R2 R2Cum AdjR2Cum F pvalue -#> 1 V2 2 0.1027206 0.1027206 -0.009439354 0.9158402 0.47 -#> 2 V1 1 0.1247527 0.2274733 0.006751390 1.1304064 0.42 +#> 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 diff --git a/reference/forward.sel.par.html b/reference/forward.sel.par.html index 80986e9..4e9a3ec 100644 --- a/reference/forward.sel.par.html +++ b/reference/forward.sel.par.html @@ -182,10 +182,9 @@

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 0.7723384 -#> variable order R2 R2cum AdjR2Cum F pval -#> 1 V3 3 0.35404270 0.3540427 0.2732980 4.384720 0.002819594 -#> 2 V1 1 0.07643803 0.4304807 0.2677609 0.939505 0.467690877 +#> 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 diff --git a/reference/listw.candidates-1.png b/reference/listw.candidates-1.png index 55505484645375517cc18ad6a746372ec58738b4..262e0fdf39e03cd82f10daa1aba1a0887bfb6fe4 100644 GIT binary patch literal 70144 zcmeFY^;?tw_Xkc$Old?(k(Lf6BqfB67AZlxySrtgNH>xqQ%W48g^>z~l+wKc(lL-2 zFvh+UU+?etANc0a|vX8%!+G=U2;qUnCOtm!%e*<^}pZO6Gu)X^GN0_pD;6OldmjI-uY#3ay zvk=nE>hQMaK#uR#YZ_HGds^DkubiHD>APOv%?tHSsf`-N6i?)mK@0*)PYtM!ab zq)(peDTo5oxk3N;`9BT(p9cO<1OKOi|KBtKImj~zAo%;Z2gq!a#T)LFaOT|W@&wO` 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zqZL<}_uwIQ62*a)11xEO=ut4v@?tZy#_45A#m4>-} diff --git a/reference/listw.candidates.html b/reference/listw.candidates.html index 9fec68d..41e6001 100644 --- a/reference/listw.candidates.html +++ b/reference/listw.candidates.html @@ -214,11 +214,9 @@

Examples

### (binary weighting), or weighted by the linearly decreasing function: candidates <- listw.candidates(coord = xy, nb = c("gab", "mst", "dnear"), weights = c("binary", "flin")) -#> Warning: zero sum general weights -#> Warning: zero sum general weights names(candidates) #> [1] "Gabriel_Binary" "Gabriel_Linear" "MST_Binary" -#> [4] "MST_Linear" "Dnear18.11_Binary" "Dnear18.11_Linear" +#> [4] "MST_Linear" "Dnear17.46_Binary" "Dnear17.46_Linear" plot(candidates[[1]], xy) plot(candidates[[3]], xy) @@ -229,11 +227,6 @@

Examples

### varying between 2 and 5, and a concave-up function with a y parametre of 0.2. candidates2 <- listw.candidates(coord = xy, nb = "dnear", weights = c("fdown", "fup"), y_fdown = 1:5, y_fup = 0.2) -#> Warning: zero sum general weights -#> Warning: zero sum general weights -#> Warning: zero sum general weights -#> Warning: zero sum general weights -#> Warning: zero sum general weights ### Number of spatial weighting matrices generated: length(candidates2) #> [1] 6 diff --git a/reference/listw.select.html b/reference/listw.select.html index d3b8173..0fd2f57 100644 --- a/reference/listw.select.html +++ b/reference/listw.select.html @@ -300,12 +300,12 @@

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 = 0.909749 with 8 variables (> 0.907009) -#> Procedure stopped (adjR2thresh criteria) adjR2cum = 0.924424 with 13 variables (> 0.923426) -#> Procedure stopped (alpha criteria): pvalue for variable 12 is 0.060000 (> 0.050000) -#> Procedure stopped (alpha criteria): pvalue for variable 15 is 0.066667 (> 0.050000) +#> 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) #> Warning: no non-missing arguments to max; returning -Inf -#> [1] 0.9068204 0.9216808 0.8430524 0.8211807 +#> [1] 0.9095298 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a/reference/msr.mantelrtest.html b/reference/msr.mantelrtest.html index 2c7c2ba..e90ebcf 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: 0.199 +#> Simulated p-value: 0.193 #> Alternative hypothesis: greater #> #> Std.Obs Expectation Variance -#> 0.880893593 0.043578208 0.007478877 +#> 0.869789570 0.042607964 0.007867696 diff --git a/reference/msr.varipart.html b/reference/msr.varipart.html index ef41223..f30a073 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: 0.02 +#> Simulated p-value: 0.01 #> Alternative hypothesis: greater #> #> Std.Obs Expectation Variance -#> 2.3368828423 0.1759501466 0.0006748053 +#> 2.6097801213 0.1729625437 0.0005956257 #> #> Individual fractions: #> a b c d @@ -174,7 +174,7 @@

Examples

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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] 0.8805261 +#> [1] 0.9099463 res$R2[minAIC] # the same -#> [1] 0.8805261 +#> [1] 0.9099463 min(res$AICc) # corrected AIC -#> [1] -91.64267 +#> [1] -101.7769 extractAIC(lm1) # classical AIC -#> [1] 5.0000 -93.0063 +#> [1] 4.0000 -102.6658 min(res$AICc)-2*(nvar*(nvar+1))/(nrow(x)-nvar-1) # the same -#> [1] -93.0063 +#> [1] -102.6658 lm2 <- lm(y~1) res$AICc0 # corrected AIC for the null model -#> [1] 5.309911 +#> [1] 11.78498 extractAIC(lm2) # classical AIC -#> [1] 1.000000 5.226578 +#> [1] 1.00000 11.70165 res$AICc0-2*(1*(1+1))/(nrow(x)-1-1) # the same -#> [1] 5.226578 +#> [1] 11.70165 diff --git a/reference/rotation-1.png b/reference/rotation-1.png index 70757d7a95b6df8341a2b693e8e9a66734f66d71..6830b37e3193361acf456beafa59d7456218458f 100644 GIT binary patch literal 25869 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zva`P;#+qld=!KDMP&RLiD|n~hn>m}*62sih7!gw%=+OR>TpNd&KhoiN53I!SUqsFflp}N>-d>+qqv=fj zB6`C9<+|QX-0f`-#EZBo$%^pGc`z5n|gLgbT6%M__ubN%sZnF8h;1u4djQg sM&N?;2ihw<(7Op`ySMp&b!Zl08H57zyJUM diff --git a/reference/rotation.html b/reference/rotation.html index 2a0fa2d..14007c4 100644 --- a/reference/rotation.html +++ b/reference/rotation.html @@ -124,27 +124,27 @@

Examples

### Rotate the coordinates by an angle of 90 degrees coords.90<-rotation(coords,90*pi/180) coords.90 -#> [,1] [,2] -#> [1,] -0.84908189 0.360220782 -#> [2,] -0.37909595 0.512086354 -#> [3,] -0.40189431 0.420590492 -#> [4,] -0.20801798 0.683014809 -#> [5,] -0.84558032 0.500707625 -#> [6,] -0.88923832 0.719514775 -#> [7,] -0.66271731 0.137711900 -#> [8,] -0.05577212 0.007749267 -#> [9,] -0.59868136 0.590474524 -#> [10,] -0.46028044 0.925249007 -#> [11,] -0.21547356 0.532804769 -#> [12,] -0.41950327 0.096689476 -#> [13,] -0.76850049 0.245828244 -#> [14,] -0.66740155 0.644044165 -#> [15,] -0.81925519 0.485382771 -#> [16,] -0.81130742 0.812532515 -#> [17,] -0.37518138 0.022988966 -#> [18,] -0.77492710 0.463044783 -#> [19,] -0.50322514 0.611293529 -#> [20,] -0.42802284 0.680320285 +#> [,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
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 142becb..3875576 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 = 1.031000 sec +#> Time for computation = 0.793000 sec #> ======================================================= #> @@ -419,7 +419,7 @@

Examples

#> Time test: R2 = 0.5265 F = 3.4186 P( 999 perm) = 0.001 #> #> --------------------------------------------------------- -#> Time for computation = 4.494000 sec +#> Time for computation = 3.632000 sec #> ========================================================= #> @@ -464,7 +464,7 @@

Examples

#> Number of interaction variables = 10 #> Number of residual degrees of freedom = 11 #> -#> Interaction test: R2 = 0.0755 F = 1.1814 P( 999 perm) = 0.238 +#> Interaction test: R2 = 0.0755 F = 1.1814 P( 999 perm) = 0.243 #> --------------------------------------------------------------------- #> Testing for common spatial and common temporal structures (model 5) #> --------------------------------------------------------------------- @@ -473,7 +473,7 @@

Examples

#> Time test: R2 = 0.045 F = 7.045 P( 999 perm) = 0.001 #> #> --------------------------------------------------------- -#> Time for computation = 0.685000 sec +#> Time for computation = 0.559000 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) = 0.02 +#> Interaction test: R2 = 0.1476 F = 1.5921 P( 999 perm) = 0.022 #> ---------------------------------------------------- #> 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 = 0.775000 sec +#> Time for computation = 0.640000 sec #> ========================================================= #> @@ -584,7 +584,7 @@

Examples

#> Time test: R2 = 0.3076 F = 22.0858 P( 999 perm) = 0.001 #> #> ------------------------------------------------------- -#> Time for computation = 2.505000 sec +#> Time for computation = 2.053000 sec #> ======================================================= #> @@ -631,7 +631,7 @@

Examples

#> Time test: R2 = 0.3076 F = 18.8093 P( 999 perm) = 0.001 #> #> ------------------------------------------------------- -#> Time for computation = 2.918000 sec +#> Time for computation = 2.451000 sec #> ======================================================= #> @@ -674,7 +674,7 @@

Examples

#> Time test: R2 = 0.3076 F = 18.8093 P( 999 perm) = 0.001 #> #> ------------------------------------------------------- -#> Time for computation = 2.592000 sec +#> Time for computation = 2.054000 sec #> ======================================================= #> diff --git a/reference/tpaired.randtest.html b/reference/tpaired.randtest.html index 62fadd5..2a4928d 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): 0.05000 +#> Prob ( 99 permutations): 0.01000 ## Compare the results to: res2 = t.test(deer[,1], deer[,2], paired=TRUE)

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    Examples

    #> --- #> #> Test Stat Obs Std.Obs Alter Pvalue -#> 1 Clay / Lfp r 0.014241856 0.50336063 two-sided 0.64 -#> 2 Silt / Lfp r -0.076256133 -1.39414914 two-sided 0.16 -#> 3 Sand / Lfp r 0.063614861 0.90011363 two-sided 0.34 -#> 4 K2O / Lfp r 0.031979355 0.69890029 two-sided 0.5 -#> 5 Mg++ / Lfp r 0.014982765 0.44590343 two-sided 0.78 -#> 6 Na+/100g / Lfp r 0.093499930 1.56781661 two-sided 0.12 -#> 7 K+ / Lfp r 0.149968927 0.98083483 two-sided 0.32 -#> 8 Conductivity / Lfp r 0.080497038 1.25720481 two-sided 0.26 -#> 9 Retention / Lfp r -0.067943687 -0.99214511 two-sided 0.38 -#> 10 Na+/l / Lfp r 0.063272819 1.02967752 two-sided 0.32 -#> 11 Elevation / Lfp r -0.129537428 -1.77149896 two-sided 0.08 -#> 12 Clay / Min height r 0.061180871 1.72403167 two-sided 0.08 -#> 13 Silt / Min height r -0.052382325 -1.67169285 two-sided 0.08 -#> 14 Sand / Min height r -0.027907397 -0.47284220 two-sided 0.78 -#> 15 K2O / Min height r 0.047550545 0.98241947 two-sided 0.26 -#> 16 Mg++ / Min height r -0.023023055 -0.36532754 two-sided 0.82 -#> 17 Na+/100g / Min height r 0.027191299 0.43327283 two-sided 0.76 -#> 18 K+ / Min height r 0.095029793 0.58043911 two-sided 0.78 -#> 19 Conductivity / Min height r -0.018899856 -0.40255941 two-sided 0.8 -#> 20 Retention / Min height r 0.004165428 -0.01962477 two-sided 0.98 -#> 21 Na+/l / Min height r -0.046119268 -0.93224875 two-sided 0.46 -#> 22 Elevation / Min height r 0.061211127 0.78806485 two-sided 0.54 -#> 23 Clay / Max height r 0.010762151 0.45752694 two-sided 0.66 -#> 24 Silt / Max height r -0.030835126 -0.77698921 two-sided 0.42 -#> 25 Sand / Max height r 0.027229507 0.52462804 two-sided 0.64 -#> 26 K2O / Max height r 0.063923177 1.42591043 two-sided 0.14 -#> 27 Mg++ / Max height r 0.010886210 0.36228252 two-sided 0.78 -#> 28 Na+/100g / Max height r 0.026525395 0.61831265 two-sided 0.5 -#> 29 K+ / Max height r 0.109401849 0.64642308 two-sided 0.68 -#> 30 Conductivity / Max height r 0.002446542 0.13708809 two-sided 0.96 -#> 31 Retention / Max height r -0.005456736 -0.20318173 two-sided 0.86 -#> 32 Na+/l / Max height r -0.018577793 -0.32224569 two-sided 0.76 -#> 33 Elevation / Max height r 0.033495574 0.44172706 two-sided 0.62 +#> 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 #> Pvalue.adj #> 1 1 #> 2 1 diff --git a/reference/msr.html b/reference/msr.html index 1516af5..bd89702 100644 --- a/reference/msr.html +++ b/reference/msr.html @@ -178,31 +178,31 @@

    Examples

    moran.mc(x1[,1], lw1, 2)$statistic #> statistic -#> 0.04155483 +#> 0.04531878 ## singleton x1.1 <- msr(x1[,1], lw1, nrepet = 9, method = "singleton") apply(x1.1, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic) -#> [1] 0.04155483 0.04155483 0.04155483 0.04155483 0.04155483 0.04155483 0.04155483 -#> [8] 0.04155483 0.04155483 +#> [1] 0.04531878 0.04531878 0.04531878 0.04531878 0.04531878 0.04531878 0.04531878 +#> [8] 0.04531878 0.04531878 ## triplet x1.2 <- msr(x1[,1], lw1, nrepet = 9, method = "triplet") apply(x1.2, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic) -#> [1] 0.04155483 0.04155483 0.04155483 0.04155483 0.04155483 0.04155483 0.04155483 -#> [8] 0.04155483 0.04155483 +#> [1] 0.04531878 0.04531878 0.04531878 0.04531878 0.04531878 0.04531878 0.04531878 +#> [8] 0.04531878 0.04531878 ## pair x1.3 <- msr(x1[,1], lw1, nrepet = 9, method = "pair") apply(x1.3, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic) -#> [1] 0.05580854 0.04739626 0.04963102 0.05461207 0.05122191 0.05086100 0.05169779 -#> [8] 0.05874263 0.05574642 +#> [1] 0.03715568 0.04275092 0.03674746 0.04385720 0.04670547 0.03513355 0.04130996 +#> [8] 0.04125028 0.03844362 ## 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] 0.05926776 0.05369884 0.05638992 0.05207227 0.05715406 0.05633329 0.05270968 -#> [8] 0.06037437 0.05383403 +#> [1] 0.03601004 0.04344145 0.04372924 0.03797438 0.04209478 0.04314482 0.03686685 +#> [8] 0.04262874 0.04513779 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,] 1.00000000 0.07367839 0.16593367 -0.21585776 0.07365584 -#> [2,] 0.07367839 1.00000000 0.05568866 -0.08642863 0.09426883 -#> [3,] 0.16593367 0.05568866 1.00000000 -0.17336608 0.18596924 -#> [4,] -0.21585776 -0.08642863 -0.17336608 1.00000000 -0.06669571 -#> [5,] 0.07365584 0.09426883 0.18596924 -0.06669571 1.00000000 +#> [,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 lapply(x1.5, cor) #> [[1]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.00000000 0.07367839 0.16593367 -0.21585776 0.07365584 -#> [2,] 0.07367839 1.00000000 0.05568866 -0.08642863 0.09426883 -#> [3,] 0.16593367 0.05568866 1.00000000 -0.17336608 0.18596924 -#> [4,] -0.21585776 -0.08642863 -0.17336608 1.00000000 -0.06669571 -#> [5,] 0.07365584 0.09426883 0.18596924 -0.06669571 1.00000000 +#> [,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 #> #> [[2]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.00000000 0.07367839 0.16593367 -0.21585776 0.07365584 -#> [2,] 0.07367839 1.00000000 0.05568866 -0.08642863 0.09426883 -#> [3,] 0.16593367 0.05568866 1.00000000 -0.17336608 0.18596924 -#> [4,] -0.21585776 -0.08642863 -0.17336608 1.00000000 -0.06669571 -#> [5,] 0.07365584 0.09426883 0.18596924 -0.06669571 1.00000000 +#> [,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 #> #> [[3]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.00000000 0.07367839 0.16593367 -0.21585776 0.07365584 -#> [2,] 0.07367839 1.00000000 0.05568866 -0.08642863 0.09426883 -#> [3,] 0.16593367 0.05568866 1.00000000 -0.17336608 0.18596924 -#> [4,] -0.21585776 -0.08642863 -0.17336608 1.00000000 -0.06669571 -#> [5,] 0.07365584 0.09426883 0.18596924 -0.06669571 1.00000000 +#> [,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 #> #> [[4]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.00000000 0.07367839 0.16593367 -0.21585776 0.07365584 -#> [2,] 0.07367839 1.00000000 0.05568866 -0.08642863 0.09426883 -#> [3,] 0.16593367 0.05568866 1.00000000 -0.17336608 0.18596924 -#> [4,] -0.21585776 -0.08642863 -0.17336608 1.00000000 -0.06669571 -#> [5,] 0.07365584 0.09426883 0.18596924 -0.06669571 1.00000000 +#> [,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 #> #> [[5]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.00000000 0.07367839 0.16593367 -0.21585776 0.07365584 -#> [2,] 0.07367839 1.00000000 0.05568866 -0.08642863 0.09426883 -#> [3,] 0.16593367 0.05568866 1.00000000 -0.17336608 0.18596924 -#> [4,] -0.21585776 -0.08642863 -0.17336608 1.00000000 -0.06669571 -#> [5,] 0.07365584 0.09426883 0.18596924 -0.06669571 1.00000000 +#> [,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 #> #> [[6]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.00000000 0.07367839 0.16593367 -0.21585776 0.07365584 -#> [2,] 0.07367839 1.00000000 0.05568866 -0.08642863 0.09426883 -#> [3,] 0.16593367 0.05568866 1.00000000 -0.17336608 0.18596924 -#> [4,] -0.21585776 -0.08642863 -0.17336608 1.00000000 -0.06669571 -#> [5,] 0.07365584 0.09426883 0.18596924 -0.06669571 1.00000000 +#> [,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 #> #> [[7]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.00000000 0.07367839 0.16593367 -0.21585776 0.07365584 -#> [2,] 0.07367839 1.00000000 0.05568866 -0.08642863 0.09426883 -#> [3,] 0.16593367 0.05568866 1.00000000 -0.17336608 0.18596924 -#> [4,] -0.21585776 -0.08642863 -0.17336608 1.00000000 -0.06669571 -#> [5,] 0.07365584 0.09426883 0.18596924 -0.06669571 1.00000000 +#> [,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 #> #> [[8]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.00000000 0.07367839 0.16593367 -0.21585776 0.07365584 -#> [2,] 0.07367839 1.00000000 0.05568866 -0.08642863 0.09426883 -#> [3,] 0.16593367 0.05568866 1.00000000 -0.17336608 0.18596924 -#> [4,] -0.21585776 -0.08642863 -0.17336608 1.00000000 -0.06669571 -#> [5,] 0.07365584 0.09426883 0.18596924 -0.06669571 1.00000000 +#> [,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 #> #> [[9]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.00000000 0.07367839 0.16593367 -0.21585776 0.07365584 -#> [2,] 0.07367839 1.00000000 0.05568866 -0.08642863 0.09426883 -#> [3,] 0.16593367 0.05568866 1.00000000 -0.17336608 0.18596924 -#> [4,] -0.21585776 -0.08642863 -0.17336608 1.00000000 -0.06669571 -#> [5,] 0.07365584 0.09426883 0.18596924 -0.06669571 1.00000000 +#> [,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 #> apply(x1, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic) -#> [1] 0.04155483 0.03180460 -0.01188175 -0.02170072 0.05282863 +#> [1] 0.04531878 -0.07609907 -0.02773368 0.07468736 0.12705310 apply(x1.5[[1]], 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic) -#> [1] 0.047187236 0.030897288 -0.008126386 -0.014227234 0.065325753 +#> [1] 0.04165888 -0.07380134 -0.02154848 0.06854768 0.13557780 ## singleton preserving correlations for multivariate data x1.6 <- msr(x1, lw1, nrepet = 9, method = "singleton") cor(x1) -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.00000000 0.07367839 0.16593367 -0.21585776 0.07365584 -#> [2,] 0.07367839 1.00000000 0.05568866 -0.08642863 0.09426883 -#> [3,] 0.16593367 0.05568866 1.00000000 -0.17336608 0.18596924 -#> [4,] -0.21585776 -0.08642863 -0.17336608 1.00000000 -0.06669571 -#> [5,] 0.07365584 0.09426883 0.18596924 -0.06669571 1.00000000 +#> [,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 lapply(x1.6, cor) #> [[1]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.00000000 0.07367839 0.16593367 -0.21585776 0.07365584 -#> [2,] 0.07367839 1.00000000 0.05568866 -0.08642863 0.09426883 -#> [3,] 0.16593367 0.05568866 1.00000000 -0.17336608 0.18596924 -#> [4,] -0.21585776 -0.08642863 -0.17336608 1.00000000 -0.06669571 -#> [5,] 0.07365584 0.09426883 0.18596924 -0.06669571 1.00000000 +#> [,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 #> #> [[2]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.00000000 0.07367839 0.16593367 -0.21585776 0.07365584 -#> [2,] 0.07367839 1.00000000 0.05568866 -0.08642863 0.09426883 -#> [3,] 0.16593367 0.05568866 1.00000000 -0.17336608 0.18596924 -#> [4,] -0.21585776 -0.08642863 -0.17336608 1.00000000 -0.06669571 -#> [5,] 0.07365584 0.09426883 0.18596924 -0.06669571 1.00000000 +#> [,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 #> #> [[3]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.00000000 0.07367839 0.16593367 -0.21585776 0.07365584 -#> [2,] 0.07367839 1.00000000 0.05568866 -0.08642863 0.09426883 -#> [3,] 0.16593367 0.05568866 1.00000000 -0.17336608 0.18596924 -#> [4,] -0.21585776 -0.08642863 -0.17336608 1.00000000 -0.06669571 -#> [5,] 0.07365584 0.09426883 0.18596924 -0.06669571 1.00000000 +#> [,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 #> #> [[4]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.00000000 0.07367839 0.16593367 -0.21585776 0.07365584 -#> [2,] 0.07367839 1.00000000 0.05568866 -0.08642863 0.09426883 -#> [3,] 0.16593367 0.05568866 1.00000000 -0.17336608 0.18596924 -#> [4,] -0.21585776 -0.08642863 -0.17336608 1.00000000 -0.06669571 -#> [5,] 0.07365584 0.09426883 0.18596924 -0.06669571 1.00000000 +#> [,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 #> #> [[5]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.00000000 0.07367839 0.16593367 -0.21585776 0.07365584 -#> [2,] 0.07367839 1.00000000 0.05568866 -0.08642863 0.09426883 -#> [3,] 0.16593367 0.05568866 1.00000000 -0.17336608 0.18596924 -#> [4,] -0.21585776 -0.08642863 -0.17336608 1.00000000 -0.06669571 -#> [5,] 0.07365584 0.09426883 0.18596924 -0.06669571 1.00000000 +#> [,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 #> #> [[6]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.00000000 0.07367839 0.16593367 -0.21585776 0.07365584 -#> [2,] 0.07367839 1.00000000 0.05568866 -0.08642863 0.09426883 -#> [3,] 0.16593367 0.05568866 1.00000000 -0.17336608 0.18596924 -#> [4,] -0.21585776 -0.08642863 -0.17336608 1.00000000 -0.06669571 -#> [5,] 0.07365584 0.09426883 0.18596924 -0.06669571 1.00000000 +#> [,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 #> #> [[7]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.00000000 0.07367839 0.16593367 -0.21585776 0.07365584 -#> [2,] 0.07367839 1.00000000 0.05568866 -0.08642863 0.09426883 -#> [3,] 0.16593367 0.05568866 1.00000000 -0.17336608 0.18596924 -#> [4,] -0.21585776 -0.08642863 -0.17336608 1.00000000 -0.06669571 -#> [5,] 0.07365584 0.09426883 0.18596924 -0.06669571 1.00000000 +#> [,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 #> #> [[8]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.00000000 0.07367839 0.16593367 -0.21585776 0.07365584 -#> [2,] 0.07367839 1.00000000 0.05568866 -0.08642863 0.09426883 -#> [3,] 0.16593367 0.05568866 1.00000000 -0.17336608 0.18596924 -#> [4,] -0.21585776 -0.08642863 -0.17336608 1.00000000 -0.06669571 -#> [5,] 0.07365584 0.09426883 0.18596924 -0.06669571 1.00000000 +#> [,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 #> #> [[9]] -#> [,1] [,2] [,3] [,4] [,5] -#> [1,] 1.00000000 0.07367839 0.16593367 -0.21585776 0.07365584 -#> [2,] 0.07367839 1.00000000 0.05568866 -0.08642863 0.09426883 -#> [3,] 0.16593367 0.05568866 1.00000000 -0.17336608 0.18596924 -#> [4,] -0.21585776 -0.08642863 -0.17336608 1.00000000 -0.06669571 -#> [5,] 0.07365584 0.09426883 0.18596924 -0.06669571 1.00000000 +#> [,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 #> apply(x1, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic) -#> [1] 0.04155483 0.03180460 -0.01188175 -0.02170072 0.05282863 +#> [1] 0.04531878 -0.07609907 -0.02773368 0.07468736 0.12705310 apply(x1.6[[1]], 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic) -#> [1] 0.04155483 0.03180460 -0.01188175 -0.02170072 0.05282863 +#> [1] 0.04531878 -0.07609907 -0.02773368 0.07468736 0.12705310

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in this periodogram (which(res.var2[,2] == max(res.var2[,2])) -1) -#> [1] 118 +#> [1] 114 # "-1" correction at the end of the previous line: the first computed period is T=2, # so period #118 is on line #117 of file res.var2 diff --git a/reference/beta.div.html b/reference/beta.div.html index 8b90de1..f96fad9 100644 --- a/reference/beta.div.html +++ b/reference/beta.div.html @@ -301,7 +301,7 @@