diff --git a/R/monoMDS.R b/R/monoMDS.R index 5d2384b2a..9445f5a89 100644 --- a/R/monoMDS.R +++ b/R/monoMDS.R @@ -3,7 +3,7 @@ model = c("global", "local", "linear", "hybrid"), threshold = 0.8, maxit = 200, weakties = TRUE, stress = 1, scaling = TRUE, pc = TRUE, smin = 1e-4, sfgrmin = 1e-7, - sratmax=0.99999, ...) + sratmax=0.999999, ...) { ## Check that 'dist' are distances or a symmetric square matrix if (!(inherits(dist, "dist") || diff --git a/man/monoMDS.Rd b/man/monoMDS.Rd index 6e7989a49..e3a59545b 100644 --- a/man/monoMDS.Rd +++ b/man/monoMDS.Rd @@ -19,7 +19,7 @@ monoMDS(dist, y, k = 2, model = c("global", "local", "linear", "hybrid"), threshold = 0.8, maxit = 200, weakties = TRUE, stress = 1, scaling = TRUE, pc = TRUE, smin = 1e-4, sfgrmin = 1e-7, - sratmax=0.99999, ...) + sratmax=0.999999, ...) \method{scores}{monoMDS}(x, choices = NA, ...) \method{plot}{monoMDS}(x, choices = c(1,2), type = "t", ...) \method{points}{monoMDS}(x, choices = c(1,2), select, ...) diff --git a/tests/Examples/vegan-Ex.Rout.save b/tests/Examples/vegan-Ex.Rout.save index da33af175..875887fdb 100644 --- a/tests/Examples/vegan-Ex.Rout.save +++ b/tests/Examples/vegan-Ex.Rout.save @@ -194,7 +194,7 @@ Formula: y ~ poly(x1, 1) + poly(x2, 1) Total model degrees of freedom 3 -REML score: -3.185099 +REML score: -3.187414 > > > @@ -2843,28 +2843,28 @@ Cumulative Proportion 0.998816 0.9997514 1.0000000 Square root transformation Wisconsin double standardization Run 0 stress 0.1843196 -Run 1 stress 0.2455912 -Run 2 stress 0.2169407 +Run 1 stress 0.2455911 +Run 2 stress 0.2169404 Run 3 stress 0.2313231 Run 4 stress 0.1974406 -Run 5 stress 0.1858402 -Run 6 stress 0.1948414 -Run 7 stress 0.2265717 -Run 8 stress 0.222507 -Run 9 stress 0.2023215 -Run 10 stress 0.2673177 +Run 5 stress 0.1858401 +Run 6 stress 0.1948413 +Run 7 stress 0.2265716 +Run 8 stress 0.2104573 +Run 9 stress 0.1962451 +Run 10 stress 0.2673136 Run 11 stress 0.1976151 Run 12 stress 0.1852397 -Run 13 stress 0.2341085 -Run 14 stress 0.1955867 -Run 15 stress 0.2137409 -Run 16 stress 0.2109638 +Run 13 stress 0.2341081 +Run 14 stress 0.1955837 +Run 15 stress 0.2137396 +Run 16 stress 0.2109613 Run 17 stress 0.1825658 ... New best solution -... Procrustes: rmse 0.04169825 max resid 0.1521436 -Run 18 stress 0.1843197 -Run 19 stress 0.2570119 -Run 20 stress 0.3760596 +... Procrustes: rmse 0.04163108 max resid 0.1518283 +Run 18 stress 0.1843196 +Run 19 stress 0.2570114 +Run 20 stress 0.3760489 *** No convergence -- monoMDS stopping criteria: 20: stress ratio > sratmax > (fit <- envfit(ord, varechem, perm = 999)) @@ -2872,20 +2872,20 @@ Run 20 stress 0.3760596 ***VECTORS NMDS1 NMDS2 r2 Pr(>r) -N -0.05719 -0.99836 0.2537 0.044 * -P 0.61959 0.78492 0.1938 0.096 . -K 0.76629 0.64249 0.1809 0.120 -Ca 0.68506 0.72849 0.4119 0.004 ** -Mg 0.63240 0.77464 0.4271 0.003 ** -S 0.19123 0.98155 0.1752 0.144 -Al -0.87169 0.49006 0.5269 0.002 ** -Fe -0.93613 0.35164 0.4451 0.004 ** -Mn 0.79873 -0.60169 0.5230 0.002 ** -Zn 0.61750 0.78657 0.1879 0.120 -Mo -0.90304 0.42955 0.0609 0.490 -Baresoil 0.92503 -0.37988 0.2508 0.053 . -Humdepth 0.93291 -0.36011 0.5200 0.001 *** -pH -0.64809 0.76156 0.2308 0.051 . +N -0.05727 -0.99836 0.2537 0.044 * +P 0.61968 0.78485 0.1938 0.096 . +K 0.76641 0.64235 0.1809 0.120 +Ca 0.68515 0.72840 0.4119 0.004 ** +Mg 0.63249 0.77457 0.4270 0.003 ** +S 0.19134 0.98152 0.1752 0.144 +Al -0.87163 0.49017 0.5269 0.002 ** +Fe -0.93605 0.35187 0.4451 0.004 ** +Mn 0.79872 -0.60171 0.5231 0.002 ** +Zn 0.61754 0.78654 0.1879 0.120 +Mo -0.90307 0.42949 0.0609 0.490 +Baresoil 0.92492 -0.38015 0.2508 0.053 . +Humdepth 0.93285 -0.36027 0.5200 0.001 *** +pH -0.64801 0.76163 0.2308 0.051 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Permutation: free @@ -2894,20 +2894,20 @@ Number of permutations: 999 > scores(fit, "vectors") NMDS1 NMDS2 -N -0.02880854 -0.5028710 -P 0.27276629 0.3455503 -K 0.32594190 0.2732833 -Ca 0.43965251 0.4675260 -Mg 0.41327822 0.5062343 -S 0.08004855 0.4108725 -Al -0.63277161 0.3557376 -Fe -0.62453266 0.2345938 -Mn 0.57765765 -0.4351493 -Zn 0.26766914 0.3409611 -Mo -0.22292390 0.1060370 -Baresoil 0.46323932 -0.1902392 -Humdepth 0.67274437 -0.2596855 -pH -0.31133604 0.3658438 +N -0.02884498 -0.5028120 +P 0.27282092 0.3455381 +K 0.32599478 0.2732275 +Ca 0.43970784 0.4674593 +Mg 0.41331681 0.5061682 +S 0.08008661 0.4108311 +Al -0.63271699 0.3558174 +Fe -0.62445907 0.2347377 +Mn 0.57765925 -0.4351757 +Zn 0.26769911 0.3409636 +Mo -0.22294149 0.1060277 +Baresoil 0.46317948 -0.1903704 +Humdepth 0.67271356 -0.2598028 +pH -0.31130902 0.3658919 > plot(ord) > plot(fit) > plot(fit, p.max = 0.05, col = "red") @@ -3342,37 +3342,37 @@ Manure^4 0.41603287 -0.01661279 Square root transformation Wisconsin double standardization Run 0 stress 0.1843196 -Run 1 stress 0.2455912 -Run 2 stress 0.2169407 +Run 1 stress 0.2455911 +Run 2 stress 0.2169404 Run 3 stress 0.2313231 Run 4 stress 0.1974406 -Run 5 stress 0.1858402 -Run 6 stress 0.1948414 -Run 7 stress 0.2265717 -Run 8 stress 0.222507 -Run 9 stress 0.2023215 -Run 10 stress 0.2673177 +Run 5 stress 0.1858401 +Run 6 stress 0.1948413 +Run 7 stress 0.2265716 +Run 8 stress 0.2104573 +Run 9 stress 0.1962451 +Run 10 stress 0.2673136 Run 11 stress 0.1976151 Run 12 stress 0.1852397 -Run 13 stress 0.2341085 -Run 14 stress 0.1955867 -Run 15 stress 0.2137409 -Run 16 stress 0.2109638 +Run 13 stress 0.2341081 +Run 14 stress 0.1955837 +Run 15 stress 0.2137396 +Run 16 stress 0.2109613 Run 17 stress 0.1825658 ... New best solution -... Procrustes: rmse 0.04169825 max resid 0.1521436 -Run 18 stress 0.1843197 -Run 19 stress 0.2570119 -Run 20 stress 0.3760596 +... Procrustes: rmse 0.04163108 max resid 0.1518283 +Run 18 stress 0.1843196 +Run 19 stress 0.2570114 +Run 20 stress 0.3760489 *** No convergence -- monoMDS stopping criteria: 20: stress ratio > sratmax > stressplot(mod) > gof <- goodness(mod) > gof - [1] 0.02984480 0.03513719 0.04188277 0.04598591 0.04003418 0.03441705 - [7] 0.03294386 0.03050002 0.03060565 0.02993891 0.03526891 0.02621276 -[13] 0.03831284 0.02980754 0.03370603 0.02225802 0.03561193 0.03505161 -[19] 0.06577544 0.03268484 0.03503582 0.02956571 0.05167231 0.04602312 + [1] 0.02984509 0.03513713 0.04189022 0.04598306 0.04003163 0.03441492 + [7] 0.03294805 0.03050081 0.03060723 0.02994055 0.03526429 0.02621404 +[13] 0.03831063 0.02980886 0.03369757 0.02225859 0.03561516 0.03505229 +[19] 0.06577486 0.03268415 0.03503187 0.02956635 0.05167876 0.04602055 > plot(mod, display = "sites", type = "n") > points(mod, display = "sites", cex = 2*gof/mean(gof)) > @@ -3597,7 +3597,7 @@ TIP.i.Achimill significance heterogeneity minIP varIP > lmod1 <- lm(y1 ~ Al + P + K, varechem, weights = rowSums(varespec)) > ## numerically identical within 2e-15 > range(cooks.distance(lmod1) - cooks.distance(mod)[, "Cladstel"]) -[1] -2.775558e-16 2.498002e-16 +[1] -2.775558e-16 4.440892e-16 > > ## t-values of regression coefficients based on type = "canoco" > ## residuals @@ -4016,42 +4016,42 @@ Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ Run 0 stress 0.1192678 Run 1 stress 0.1886532 Run 2 stress 0.1192678 -... New best solution -... Procrustes: rmse 4.497004e-05 max resid 0.0001297349 +... Procrustes: rmse 5.822837e-06 max resid 1.845818e-05 ... Similar to previous best Run 3 stress 0.1192678 -... New best solution -... Procrustes: rmse 1.801542e-05 max resid 5.078152e-05 +... Procrustes: rmse 6.697234e-06 max resid 2.061976e-05 ... Similar to previous best Run 4 stress 0.1183186 ... New best solution -... Procrustes: rmse 0.02025988 max resid 0.06488321 -Run 5 stress 0.1192685 +... Procrustes: rmse 0.02027078 max resid 0.06496407 +Run 5 stress 0.1192679 Run 6 stress 0.1939202 -Run 7 stress 0.1808919 +Run 7 stress 0.1808911 Run 8 stress 0.1183186 -... Procrustes: rmse 1.538285e-05 max resid 4.789705e-05 +... New best solution +... Procrustes: rmse 1.651372e-06 max resid 5.655239e-06 ... Similar to previous best -Run 9 stress 0.1192679 +Run 9 stress 0.1192678 Run 10 stress 0.1183186 -... New best solution -... Procrustes: rmse 1.244871e-05 max resid 3.745684e-05 +... Procrustes: rmse 1.505921e-06 max resid 4.480433e-06 ... Similar to previous best -Run 11 stress 0.1192686 -Run 12 stress 0.1192683 +Run 11 stress 0.1192679 +Run 12 stress 0.1192678 Run 13 stress 0.1886532 Run 14 stress 0.1183186 -... Procrustes: rmse 2.260745e-06 max resid 5.293932e-06 +... New best solution +... Procrustes: rmse 1.088269e-06 max resid 3.328672e-06 ... Similar to previous best Run 15 stress 0.1192678 Run 16 stress 0.1183186 -... Procrustes: rmse 3.225938e-06 max resid 7.314283e-06 +... New best solution +... Procrustes: rmse 1.797818e-06 max resid 3.862713e-06 ... Similar to previous best Run 17 stress 0.2075713 Run 18 stress 0.1192678 -Run 19 stress 0.1192685 +Run 19 stress 0.1192679 Run 20 stress 0.1183186 -... Procrustes: rmse 2.295609e-05 max resid 6.384852e-05 +... Procrustes: rmse 6.241245e-06 max resid 1.34356e-05 ... Similar to previous best *** Solution reached > sol @@ -4077,113 +4077,113 @@ Species: expanded scores based on ‘dune’ Starting from 2-dimensional configuration Run 0 stress 0.1183186 Run 1 stress 0.1183186 -... Procrustes: rmse 1.396951e-05 max resid 4.609836e-05 +... Procrustes: rmse 4.788018e-06 max resid 1.519832e-05 ... Similar to previous best -Run 2 stress 0.181294 +Run 2 stress 0.1812935 Run 3 stress 0.1183186 -... Procrustes: rmse 1.222929e-05 max resid 3.671397e-05 +... Procrustes: rmse 2.196256e-06 max resid 6.801087e-06 ... Similar to previous best Run 4 stress 0.1192678 -Run 5 stress 0.1192679 +Run 5 stress 0.1192678 Run 6 stress 0.1183186 -... Procrustes: rmse 3.046112e-05 max resid 9.621436e-05 +... Procrustes: rmse 7.03453e-06 max resid 2.236837e-05 ... Similar to previous best Run 7 stress 0.1183186 ... New best solution -... Procrustes: rmse 8.248765e-06 max resid 2.709666e-05 +... Procrustes: rmse 2.728661e-06 max resid 8.49573e-06 ... Similar to previous best -Run 8 stress 0.1192679 +Run 8 stress 0.1192678 Run 9 stress 0.1183186 -... Procrustes: rmse 4.987329e-05 max resid 0.0001617922 +... Procrustes: rmse 8.200604e-06 max resid 2.1511e-05 ... Similar to previous best Run 10 stress 0.1183186 -... Procrustes: rmse 6.258948e-05 max resid 0.0001841984 +... Procrustes: rmse 3.222731e-05 max resid 9.836386e-05 ... Similar to previous best Run 11 stress 0.1183186 -... Procrustes: rmse 2.298969e-05 max resid 7.777073e-05 +... Procrustes: rmse 9.840546e-06 max resid 3.276067e-05 ... Similar to previous best Run 12 stress 0.1183186 -... Procrustes: rmse 9.953516e-05 max resid 0.0002971112 +... Procrustes: rmse 2.658646e-05 max resid 8.215503e-05 ... Similar to previous best -Run 13 stress 0.1192679 +Run 13 stress 0.1192678 Run 14 stress 0.1183186 -... Procrustes: rmse 0.0001064944 max resid 0.0003485089 +... Procrustes: rmse 2.801593e-05 max resid 9.120261e-05 ... Similar to previous best Run 15 stress 0.1192678 -Run 16 stress 0.1183187 -... Procrustes: rmse 6.269441e-05 max resid 0.0001989035 +Run 16 stress 0.1183186 +... Procrustes: rmse 1.230384e-05 max resid 4.080807e-05 ... Similar to previous best -Run 17 stress 0.1192685 -Run 18 stress 0.1192679 -Run 19 stress 0.1192683 -Run 20 stress 0.1809581 +Run 17 stress 0.1192679 +Run 18 stress 0.1192678 +Run 19 stress 0.1192679 +Run 20 stress 0.1809577 *** Solution reached > ## Local NMDS and stress 2 of monoMDS > sol2 <- metaMDS(dune, model = "local", stress=2) -Run 0 stress 0.1928489 -Run 1 stress 0.1928478 +Run 0 stress 0.1928478 +Run 1 stress 0.1928475 ... New best solution -... Procrustes: rmse 0.000227748 max resid 0.0006739712 +... Procrustes: rmse 0.0001176864 max resid 0.0003465431 ... Similar to previous best -Run 2 stress 0.1928479 -... Procrustes: rmse 0.0005683554 max resid 0.001654113 +Run 2 stress 0.1928476 +... Procrustes: rmse 0.0003230671 max resid 0.0009395776 ... Similar to previous best -Run 3 stress 0.1928529 -... Procrustes: rmse 0.001497169 max resid 0.004237256 +Run 3 stress 0.192849 +... Procrustes: rmse 0.000653679 max resid 0.001926441 ... Similar to previous best -Run 4 stress 0.1928479 -... Procrustes: rmse 0.0002205786 max resid 0.000527516 +Run 4 stress 0.1928475 +... New best solution +... Procrustes: rmse 7.245208e-05 max resid 0.0002138775 ... Similar to previous best -Run 5 stress 0.1928542 -... Procrustes: rmse 0.001703784 max resid 0.00489439 +Run 5 stress 0.1928478 +... Procrustes: rmse 0.000352899 max resid 0.0009949857 ... Similar to previous best -Run 6 stress 0.192857 -... Procrustes: rmse 0.002035406 max resid 0.005757312 +Run 6 stress 0.1928478 +... Procrustes: rmse 0.0003262518 max resid 0.0009223792 ... Similar to previous best -Run 7 stress 0.1928513 -... Procrustes: rmse 0.0003555387 max resid 0.0009295202 +Run 7 stress 0.1928476 +... Procrustes: rmse 0.0001088032 max resid 0.0003261061 ... Similar to previous best -Run 8 stress 0.1928514 -... Procrustes: rmse 0.00131401 max resid 0.003754444 +Run 8 stress 0.1928475 +... New best solution +... Procrustes: rmse 0.0001255331 max resid 0.0003130878 ... Similar to previous best -Run 9 stress 0.1928527 -... Procrustes: rmse 0.001508577 max resid 0.004304102 +Run 9 stress 0.1928479 +... Procrustes: rmse 0.0002656517 max resid 0.0007825987 ... Similar to previous best -Run 10 stress 0.1928516 -... Procrustes: rmse 0.001200753 max resid 0.003381483 +Run 10 stress 0.1928478 +... Procrustes: rmse 0.0002328614 max resid 0.0006821753 ... Similar to previous best -Run 11 stress 0.1928477 +Run 11 stress 0.1928475 ... New best solution -... Procrustes: rmse 0.0002147781 max resid 0.0005379322 +... Procrustes: rmse 6.504978e-05 max resid 0.0001794633 ... Similar to previous best -Run 12 stress 0.1928476 -... New best solution -... Procrustes: rmse 8.797899e-05 max resid 0.0002368433 +Run 12 stress 0.1928475 +... Procrustes: rmse 4.196296e-05 max resid 0.0001142352 ... Similar to previous best -Run 13 stress 0.1928518 -... Procrustes: rmse 0.001194318 max resid 0.003382898 +Run 13 stress 0.1928475 +... Procrustes: rmse 0.0001178629 max resid 0.0003459239 ... Similar to previous best -Run 14 stress 0.1928479 -... Procrustes: rmse 0.0001251706 max resid 0.0003371929 +Run 14 stress 0.1928475 +... Procrustes: rmse 0.0001018608 max resid 0.0002984929 ... Similar to previous best -Run 15 stress 0.1928498 -... Procrustes: rmse 0.000713671 max resid 0.002074812 +Run 15 stress 0.1928479 +... Procrustes: rmse 0.000321311 max resid 0.000929565 ... Similar to previous best -Run 16 stress 0.1928581 -... Procrustes: rmse 0.001956972 max resid 0.005484317 +Run 16 stress 0.1928476 +... Procrustes: rmse 0.0001588211 max resid 0.0004590285 ... Similar to previous best -Run 17 stress 0.1928499 -... Procrustes: rmse 0.0008529383 max resid 0.002406656 +Run 17 stress 0.1928479 +... Procrustes: rmse 0.000329319 max resid 0.0009609323 ... Similar to previous best -Run 18 stress 0.1928478 -... Procrustes: rmse 0.0003445826 max resid 0.0009688855 +Run 18 stress 0.1928476 +... Procrustes: rmse 0.000169362 max resid 0.0004958518 ... Similar to previous best -Run 19 stress 0.1928493 -... Procrustes: rmse 0.0005050439 max resid 0.001350483 +Run 19 stress 0.1928476 +... Procrustes: rmse 0.0001803285 max resid 0.0005047989 ... Similar to previous best Run 20 stress 0.1928475 -... New best solution -... Procrustes: rmse 4.656629e-05 max resid 0.0001065303 +... Procrustes: rmse 1.07477e-05 max resid 3.58325e-05 ... Similar to previous best *** Solution reached > sol2 @@ -4210,39 +4210,38 @@ Run 1 stress 0.1073148 Run 2 stress 0.1073148 Run 3 stress 0.1067169 ... New best solution -... Procrustes: rmse 9.860777e-06 max resid 3.045935e-05 +... Procrustes: rmse 5.139264e-06 max resid 1.72906e-05 ... Similar to previous best Run 4 stress 0.1067169 -... Procrustes: rmse 2.720552e-05 max resid 7.599563e-05 +... Procrustes: rmse 4.49858e-06 max resid 1.408353e-05 ... Similar to previous best Run 5 stress 0.1073148 -Run 6 stress 0.1653581 +Run 6 stress 0.1653484 Run 7 stress 0.1073148 Run 8 stress 0.1067169 -... Procrustes: rmse 1.047284e-05 max resid 2.581037e-05 +... Procrustes: rmse 5.848173e-06 max resid 1.923175e-05 ... Similar to previous best Run 9 stress 0.1073148 Run 10 stress 0.1073148 Run 11 stress 0.1067169 -... Procrustes: rmse 4.569546e-05 max resid 9.658124e-05 +... Procrustes: rmse 4.371453e-06 max resid 1.067082e-05 ... Similar to previous best Run 12 stress 0.107471 Run 13 stress 0.107471 Run 14 stress 0.1073148 Run 15 stress 0.107471 Run 16 stress 0.1073148 -Run 17 stress 0.1069788 -... Procrustes: rmse 0.006831076 max resid 0.0241299 +Run 17 stress 0.1069787 +... Procrustes: rmse 0.006797389 max resid 0.02397179 Run 18 stress 0.1067169 ... New best solution -... Procrustes: rmse 4.325792e-06 max resid 1.459835e-05 +... Procrustes: rmse 3.536972e-06 max resid 1.241593e-05 ... Similar to previous best Run 19 stress 0.1067169 -... Procrustes: rmse 1.880583e-05 max resid 4.343547e-05 +... Procrustes: rmse 4.131723e-06 max resid 9.650096e-06 ... Similar to previous best Run 20 stress 0.1067169 -... New best solution -... Procrustes: rmse 1.608418e-06 max resid 3.595822e-06 +... Procrustes: rmse 8.175633e-07 max resid 1.839172e-06 ... Similar to previous best *** Solution reached > sol @@ -4310,10 +4309,10 @@ Local non-metric Multidimensional Scaling 20 points, dissimilarity ‘bray’, call ‘vegdist(x = dune)’ Dimensions: 2 -Stress: 0.1238005 +Stress: 0.1238001 Stress type 1, weak ties Scores scaled to unit root mean square, rotated to principal components -Stopped after 73 iterations: Stress nearly unchanged (ratio > sratmax) +Stopped after 80 iterations: Stress nearly unchanged (ratio > sratmax) > plot(m) > > @@ -4364,43 +4363,42 @@ Number of permutations: 999 > plot(dune.ord <- metaMDS(dune), type="text", display="sites" ) Run 0 stress 0.1192678 Run 1 stress 0.1192678 -... New best solution -... Procrustes: rmse 5.689345e-05 max resid 0.0001718508 +... Procrustes: rmse 3.027648e-05 max resid 9.262627e-05 ... Similar to previous best -Run 2 stress 0.1192685 -... Procrustes: rmse 0.0003550222 max resid 0.00106452 +Run 2 stress 0.119268 +... Procrustes: rmse 0.0002057255 max resid 0.0006282266 ... Similar to previous best Run 3 stress 0.1183186 ... New best solution -... Procrustes: rmse 0.02029276 max resid 0.06511836 +... Procrustes: rmse 0.02026982 max resid 0.06495764 Run 4 stress 0.1183186 -... Procrustes: rmse 1.689038e-05 max resid 5.289041e-05 +... New best solution +... Procrustes: rmse 3.290177e-06 max resid 7.737989e-06 ... Similar to previous best Run 5 stress 0.1192678 Run 6 stress 0.1183186 -... Procrustes: rmse 1.102487e-05 max resid 3.361892e-05 +... Procrustes: rmse 7.097048e-06 max resid 2.303315e-05 ... Similar to previous best Run 7 stress 0.1192678 Run 8 stress 0.1183186 -... New best solution -... Procrustes: rmse 9.669693e-06 max resid 2.893244e-05 +... Procrustes: rmse 6.340026e-06 max resid 2.058872e-05 ... Similar to previous best Run 9 stress 0.1192678 Run 10 stress 0.1183186 -... Procrustes: rmse 0.0001268713 max resid 0.000356103 +... Procrustes: rmse 3.001704e-05 max resid 8.927845e-05 ... Similar to previous best -Run 11 stress 0.1808912 +Run 11 stress 0.1808911 Run 12 stress 0.1808911 Run 13 stress 0.1192678 -Run 14 stress 0.1809578 +Run 14 stress 0.1809577 Run 15 stress 0.1183186 -... Procrustes: rmse 7.279939e-05 max resid 0.0002152383 +... Procrustes: rmse 1.356528e-05 max resid 4.110144e-05 ... Similar to previous best -Run 16 stress 0.1808915 -Run 17 stress 0.1192679 -Run 18 stress 0.1192683 -Run 19 stress 0.1192679 -Run 20 stress 0.1812947 +Run 16 stress 0.1808911 +Run 17 stress 0.1192678 +Run 18 stress 0.1192678 +Run 19 stress 0.1192678 +Run 20 stress 0.1812936 *** Solution reached > with(dune.env, ordihull(dune.ord, Management)) > @@ -5521,9 +5519,9 @@ Formula: y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE) Estimated degrees of freedom: -5.63 total = 6.63 +5.64 total = 6.64 -REML score: 92.96761 +REML score: 92.95905 > > ## as above but without the extra penalties on smooth terms, > ## and using GCV smoothness selection (old behaviour of `ordisurf()`): @@ -5537,20 +5535,20 @@ Formula: y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE) Estimated degrees of freedom: -6.45 total = 7.45 +6.49 total = 7.49 -GCV score: 144.0039 +GCV score: 143.7761 > > ## Cover of Cladina arbuscula > fit <- ordisurf(vare.mds ~ Cladarbu, varespec, family=quasipoisson) > ## Get fitted values > calibrate(fit) 18 15 24 27 23 19 22 16 -21.254026 5.675259 3.679616 3.898510 9.643821 7.698310 7.729203 9.758046 +21.260675 5.673306 3.677152 3.889980 9.618595 7.665832 7.763240 9.773191 28 13 14 20 25 7 5 6 - 2.743212 29.700556 11.971747 8.310960 5.390564 24.905994 11.007652 25.330656 + 2.699161 29.765356 12.007288 8.286221 5.417316 24.914271 11.045769 25.390480 3 4 2 9 12 10 11 21 - 6.520708 9.410616 4.519672 4.090948 11.557930 4.314850 11.693692 14.243453 + 6.510626 9.424402 4.492915 4.051451 11.563182 4.286884 11.683416 14.189292 > > ## Variable selection via additional shrinkage penalties > ## This allows non-significant smooths to be selected out @@ -5566,9 +5564,9 @@ Formula: y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE) Estimated degrees of freedom: -5.63 total = 6.63 +5.64 total = 6.64 -REML score: 92.96761 +REML score: 92.95905 > ## - option 2: use a basis with shrinkage > ordisurf(vare.mds ~ Baresoil, varechem, method = "REML", bs = "ts") @@ -5579,9 +5577,9 @@ Formula: y ~ s(x1, x2, k = 10, bs = "ts", fx = FALSE) Estimated degrees of freedom: -4.43 total = 5.43 +4.41 total = 5.41 -REML score: 96.2345 +REML score: 96.22088 > ## or bs = "cs" with `isotropic = FALSE` > > ## Plot method @@ -5592,7 +5590,7 @@ REML score: 96.2345 > ## or via plot.gam directly > library(mgcv) Loading required package: nlme -This is mgcv 1.8-28. For overview type 'help("mgcv-package")'. +This is mgcv 1.8-31. For overview type 'help("mgcv-package")'. > plot.gam(fit, cex = 2, pch = 1, col = "blue") > ## 'col' effects all objects drawn... > @@ -5607,9 +5605,9 @@ Formula: y ~ s(x1, x2, k = 10, bs = "ds", fx = FALSE) Estimated degrees of freedom: -5.63 total = 6.63 +5.65 total = 6.65 -REML score: 93.17149 +REML score: 93.16148 > > ## A fixed degrees of freedom smooth, must use 'select = FALSE' > ordisurf(vare.mds ~ Baresoil, varechem, knots = 4, @@ -5624,7 +5622,7 @@ y ~ s(x1, x2, k = 4, bs = "tp", fx = TRUE) Estimated degrees of freedom: 3 total = 4 -REML score: 81.86011 +REML score: 81.86252 > > ## An anisotropic smoother with cubic regression spline bases > ordisurf(vare.mds ~ Baresoil, varechem, isotropic = FALSE, @@ -5638,9 +5636,9 @@ y ~ te(x1, x2, k = c(4, 4), bs = c("cr", "cr"), fx = c(FALSE, FALSE)) Estimated degrees of freedom: -2.99 total = 3.99 +3 total = 4 -REML score: 90.86099 +REML score: 90.83453 > > ## An anisotropic smoother with cubic regression spline with > ## shrinkage bases & different degrees of freedom in each dimension @@ -5658,7 +5656,7 @@ y ~ te(x1, x2, k = c(3, 4), bs = c("cs", "cs"), fx = c(TRUE, Estimated degrees of freedom: 11 total = 12 -REML score: 39.58245 +REML score: 39.52198 > > > @@ -6622,36 +6620,34 @@ procrustes(X = mds.alt, Y = mds.null) Number of objects: 24 Number of dimensions: 2 Procrustes sum of squares: - 13.1368 + 13.14344 Procrustes root mean squared error: - 0.7398422 + 0.7400292 Quantiles of Procrustes errors: - Min 1Q Median 3Q Max -0.02033237 0.27686842 0.34124239 0.64358542 2.23460567 + Min 1Q Median 3Q Max +0.0206655 0.2765963 0.3415901 0.6420556 2.2360422 Rotation matrix: [,1] [,2] -[1,] 0.9933117 -0.1154641 -[2,] 0.1154641 0.9933117 +[1,] 0.9932434 -0.1160497 +[2,] 0.1160497 0.9932434 Translation of averages: - [,1] [,2] -[1,] -7.774462e-18 4.29519e-20 + [,1] [,2] +[1,] 1.275858e-17 -1.158176e-17 Scaling of target: -[1] 0.6727804 +[1] 0.6725748 > plot(vare.proc) > plot(vare.proc, kind=2) > residuals(vare.proc) - 18 15 24 27 23 19 22 -0.28003467 0.13180094 1.72990690 0.63953155 0.37177564 0.11807007 0.38558576 - 16 28 13 14 20 25 7 -0.28008711 1.22839229 0.65574702 0.26736966 0.02033237 0.35725398 0.28654874 - 5 6 3 4 2 9 12 -0.69324047 0.20264726 0.29744269 0.50070811 2.23460567 0.31901411 0.21727135 - 10 11 21 -0.32523080 0.56506437 0.78179902 + 18 15 24 27 23 19 22 16 +0.2797688 0.1314075 1.7292429 0.6375208 0.3726501 0.1187909 0.3859464 0.2801511 + 28 13 14 20 25 7 5 6 +1.2289732 0.6556602 0.2670786 0.0206655 0.3578691 0.2864106 0.6962618 0.2021195 + 3 4 2 9 12 10 11 21 +0.2970201 0.5014819 2.2360422 0.3189630 0.2176967 0.3253111 0.5647893 0.7806887 > > > @@ -8359,28 +8355,28 @@ Use function ‘rda’ to test significance of fractions of interest Square root transformation Wisconsin double standardization Run 0 stress 0.1843196 -Run 1 stress 0.2455912 -Run 2 stress 0.2169407 +Run 1 stress 0.2455911 +Run 2 stress 0.2169404 Run 3 stress 0.2313231 Run 4 stress 0.1974406 -Run 5 stress 0.1858402 -Run 6 stress 0.1948414 -Run 7 stress 0.2265717 -Run 8 stress 0.222507 -Run 9 stress 0.2023215 -Run 10 stress 0.2673177 +Run 5 stress 0.1858401 +Run 6 stress 0.1948413 +Run 7 stress 0.2265716 +Run 8 stress 0.2104573 +Run 9 stress 0.1962451 +Run 10 stress 0.2673136 Run 11 stress 0.1976151 Run 12 stress 0.1852397 -Run 13 stress 0.2341085 -Run 14 stress 0.1955867 -Run 15 stress 0.2137409 -Run 16 stress 0.2109638 +Run 13 stress 0.2341081 +Run 14 stress 0.1955837 +Run 15 stress 0.2137396 +Run 16 stress 0.2109613 Run 17 stress 0.1825658 ... New best solution -... Procrustes: rmse 0.04169825 max resid 0.1521436 -Run 18 stress 0.1843197 -Run 19 stress 0.2570119 -Run 20 stress 0.3760596 +... Procrustes: rmse 0.04163108 max resid 0.1518283 +Run 18 stress 0.1843196 +Run 19 stress 0.2570114 +Run 20 stress 0.3760489 *** No convergence -- monoMDS stopping criteria: 20: stress ratio > sratmax > plot(ord, type = "t") @@ -8391,20 +8387,20 @@ Run 20 stress 0.3760596 ***VECTORS NMDS1 NMDS2 r2 Pr(>r) -N -0.05719 -0.99836 0.2537 0.044 * -P 0.61959 0.78492 0.1938 0.096 . -K 0.76629 0.64249 0.1809 0.120 -Ca 0.68506 0.72849 0.4119 0.004 ** -Mg 0.63240 0.77464 0.4271 0.003 ** -S 0.19123 0.98155 0.1752 0.144 -Al -0.87169 0.49006 0.5269 0.002 ** -Fe -0.93613 0.35164 0.4451 0.004 ** -Mn 0.79873 -0.60169 0.5230 0.002 ** -Zn 0.61750 0.78657 0.1879 0.120 -Mo -0.90304 0.42955 0.0609 0.490 -Baresoil 0.92503 -0.37988 0.2508 0.053 . -Humdepth 0.93291 -0.36011 0.5200 0.001 *** -pH -0.64809 0.76156 0.2308 0.051 . +N -0.05727 -0.99836 0.2537 0.044 * +P 0.61968 0.78485 0.1938 0.096 . +K 0.76641 0.64235 0.1809 0.120 +Ca 0.68515 0.72840 0.4119 0.004 ** +Mg 0.63249 0.77457 0.4270 0.003 ** +S 0.19134 0.98152 0.1752 0.144 +Al -0.87163 0.49017 0.5269 0.002 ** +Fe -0.93605 0.35187 0.4451 0.004 ** +Mn 0.79872 -0.60171 0.5231 0.002 ** +Zn 0.61754 0.78654 0.1879 0.120 +Mo -0.90307 0.42949 0.0609 0.490 +Baresoil 0.92492 -0.38015 0.2508 0.053 . +Humdepth 0.93285 -0.36027 0.5200 0.001 *** +pH -0.64801 0.76163 0.2308 0.051 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Permutation: free @@ -9036,13 +9032,13 @@ Cladphyl 50.475000 35.28125 8.568750 0.2812500 7.728125 1.575000 3.231250 > ## Eigevalues are numerically similar > ca$CA$eig - ord$eig CA1 CA2 CA3 CA4 CA5 - 2.220446e-16 -1.387779e-15 -1.054712e-15 2.498002e-16 1.110223e-16 + 0.000000e+00 -5.551115e-16 -8.881784e-16 3.330669e-16 3.053113e-16 CA6 CA7 CA8 CA9 CA10 - 1.665335e-16 -1.387779e-17 -1.110223e-16 2.220446e-16 4.163336e-17 + 1.942890e-16 -2.359224e-16 -2.359224e-16 -1.387779e-17 7.632783e-17 CA11 CA12 CA13 CA14 CA15 - 1.179612e-16 -5.551115e-17 -1.387779e-17 3.469447e-18 -6.938894e-18 + 9.714451e-17 2.775558e-17 5.551115e-17 -1.040834e-17 -4.163336e-17 CA16 CA17 CA18 CA19 --5.204170e-18 1.734723e-18 -4.683753e-17 3.989864e-17 + 1.908196e-17 -2.081668e-17 -3.209238e-17 4.857226e-17 > ## Configurations are similar when site scores are scaled by > ## eigenvalues in CA > procrustes(ord, ca, choices=1:19, scaling = "sites") @@ -9051,7 +9047,7 @@ Call: procrustes(X = ord, Y = ca, choices = 1:19, scaling = "sites") Procrustes sum of squares: --5.684e-14 +-4.263e-14 > plot(procrustes(ord, ca, choices=1:2, scaling="sites")) > ## Reconstruction of non-Euclidean distances with negative eigenvalues @@ -9071,7 +9067,7 @@ Procrustes sum of squares: > cleanEx() > options(digits = 7L) > base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n") -Time elapsed: 29.921 1.74 32.805 0 0 +Time elapsed: 23.655 1.156 25.361 0 0 > grDevices::dev.off() null device 1