[Vegan-commits] r2337 - pkg/vegan/tests/Examples
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Wed Dec 12 18:47:35 CET 2012
Author: jarioksa
Date: 2012-12-12 18:47:35 +0100 (Wed, 12 Dec 2012)
New Revision: 2337
Modified:
pkg/vegan/tests/Examples/vegan-Ex.Rout.save
Log:
update examples for r2336
Modified: pkg/vegan/tests/Examples/vegan-Ex.Rout.save
===================================================================
--- pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2012-12-12 17:45:44 UTC (rev 2336)
+++ pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2012-12-12 17:47:35 UTC (rev 2337)
@@ -1,8 +1,8 @@
-R Under development (unstable) (2012-10-12 r60919) -- "Unsuffered Consequences"
+R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
-Platform: x86_64-unknown-linux-gnu (64-bit)
+Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
@@ -23,7 +23,7 @@
> options(warn = 1)
> library('vegan')
Loading required package: permute
-This is vegan 2.1-21
+This is vegan 2.1-22
>
> assign(".oldSearch", search(), pos = 'CheckExEnv')
> cleanEx()
@@ -154,17 +154,17 @@
> plot(ef)
> ordisurf(mod ~ pH, varechem, knots = 1, add = TRUE)
Loading required package: mgcv
-This is mgcv 1.7-21. For overview type 'help("mgcv-package")'.
+This is mgcv 1.7-18. For overview type 'help("mgcv-package")'.
Family: gaussian
Link function: identity
Formula:
y ~ poly(x1, 1) + poly(x2, 1)
-<environment: 0x2ab7cc0>
+<environment: 0x102511068>
Total model degrees of freedom 3
-GCV score: 0.0427924
+GCV score: 0.04278782
>
>
>
@@ -668,17 +668,14 @@
> library(lattice)
> dotplot(total ~ NO3, dat, jitter.x=TRUE, groups=field,
+ type=c('p','a'), xlab="NO3", auto.key=list(columns=3, lines=TRUE) )
-Warning in FUN(X[[8L]], ...) : 'x' is NULL so the result will be NULL
-Warning in FUN(X[[9L]], ...) : 'x' is NULL so the result will be NULL
-Warning in FUN(X[[8L]], ...) : 'x' is NULL so the result will be NULL
-Warning in FUN(X[[9L]], ...) : 'x' is NULL so the result will be NULL
>
> Y <- data.frame(Agropyron, Schizachyrium)
> mod <- metaMDS(Y)
-Run 0 stress 0.08556588
-Run 1 stress 0.1560545
-Run 2 stress 0.08556612
-... procrustes: rmse 0.0001630123 max resid 0.0003642025
+Run 0 stress 0.08556586
+Run 1 stress 0.1560544
+Run 2 stress 0.08556586
+... New best solution
+... procrustes: rmse 1.094373e-06 max resid 1.88838e-06
*** Solution reached
> plot(mod)
> ### Hulls show treatment
@@ -2595,48 +2592,48 @@
> ord <- metaMDS(varespec)
Square root transformation
Wisconsin double standardization
-Run 0 stress 0.1843204
-Run 1 stress 0.2455937
-Run 2 stress 0.2169416
-Run 3 stress 0.2313238
-Run 4 stress 0.1974421
-Run 5 stress 0.1858424
-Run 6 stress 0.1948436
-Run 7 stress 0.2265718
-Run 8 stress 0.2225085
-Run 9 stress 0.2023228
+Run 0 stress 0.1843196
+Run 1 stress 0.2455912
+Run 2 stress 0.2169407
+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 11 stress 0.1976154
-Run 12 stress 0.1852405
+Run 11 stress 0.1976151
+Run 12 stress 0.1852397
Run 13 stress 0.2341085
-Run 14 stress 0.1955872
-Run 15 stress 0.2137414
-Run 16 stress 0.2109643
-Run 17 stress 0.1825664
+Run 14 stress 0.1955867
+Run 15 stress 0.2137409
+Run 16 stress 0.2109638
+Run 17 stress 0.1825658
... New best solution
-... procrustes: rmse 0.0421789 max resid 0.1544029
-Run 18 stress 0.1843201
-Run 19 stress 0.2570123
+... procrustes: rmse 0.04169825 max resid 0.1521436
+Run 18 stress 0.1843197
+Run 19 stress 0.2570119
Run 20 stress 0.3760596
> (fit <- envfit(ord, varechem, perm = 999))
***VECTORS
NMDS1 NMDS2 r2 Pr(>r)
-N -0.056188 -0.998420 0.2542 0.046 *
-P 0.618628 0.785684 0.1936 0.103
-K 0.765142 0.643862 0.1809 0.143
-Ca 0.683985 0.729496 0.4120 0.008 **
-Mg 0.631457 0.775411 0.4273 0.004 **
-S 0.190091 0.981767 0.1754 0.140
-Al -0.872443 0.488715 0.5270 0.001 ***
-Fe -0.937013 0.349294 0.4452 0.002 **
-Mn 0.799058 -0.601253 0.5228 0.001 ***
-Zn 0.616932 0.787017 0.1878 0.125
-Mo -0.903112 0.429406 0.0609 0.539
-Baresoil 0.926090 -0.377302 0.2509 0.038 *
-Humdepth 0.933540 -0.358473 0.5198 0.002 **
-pH -0.648970 0.760814 0.2306 0.060 .
+N -0.057194 -0.998363 0.2537 0.046 *
+P 0.619593 0.784923 0.1938 0.103
+K 0.766293 0.642492 0.1809 0.143
+Ca 0.685057 0.728489 0.4119 0.008 **
+Mg 0.632400 0.774642 0.4271 0.004 **
+S 0.191230 0.981545 0.1752 0.140
+Al -0.871691 0.490056 0.5269 0.001 ***
+Fe -0.936135 0.351641 0.4451 0.002 **
+Mn 0.798733 -0.601685 0.5230 0.001 ***
+Zn 0.617495 0.786575 0.1879 0.125
+Mo -0.903045 0.429546 0.0609 0.537
+Baresoil 0.925034 -0.379885 0.2508 0.039 *
+Humdepth 0.932909 -0.360112 0.5200 0.002 **
+pH -0.648094 0.761560 0.2308 0.060 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
P values based on 999 permutations.
@@ -2644,20 +2641,20 @@
> scores(fit, "vectors")
NMDS1 NMDS2
-N -0.02832824 -0.5033758
-P 0.27221584 0.3457258
-K 0.32540799 0.2738285
-Ca 0.43903747 0.4682499
-Mg 0.41279593 0.5069011
-S 0.07961737 0.4112023
-Al -0.63333130 0.3547724
-Fe -0.62521305 0.2330634
-Mn 0.57778128 -0.4347530
-Zn 0.26732049 0.3410194
-Mo -0.22283870 0.1059540
-Baresoil 0.46384359 -0.1889764
-Humdepth 0.67308687 -0.2584611
-pH -0.31165145 0.3653619
+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
> plot(ord)
> plot(fit)
> plot(fit, p.max = 0.05, col = "red")
@@ -3074,36 +3071,36 @@
> mod <- metaMDS(varespec)
Square root transformation
Wisconsin double standardization
-Run 0 stress 0.1843204
-Run 1 stress 0.2455937
-Run 2 stress 0.2169416
-Run 3 stress 0.2313238
-Run 4 stress 0.1974421
-Run 5 stress 0.1858424
-Run 6 stress 0.1948436
-Run 7 stress 0.2265718
-Run 8 stress 0.2225085
-Run 9 stress 0.2023228
+Run 0 stress 0.1843196
+Run 1 stress 0.2455912
+Run 2 stress 0.2169407
+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 11 stress 0.1976154
-Run 12 stress 0.1852405
+Run 11 stress 0.1976151
+Run 12 stress 0.1852397
Run 13 stress 0.2341085
-Run 14 stress 0.1955872
-Run 15 stress 0.2137414
-Run 16 stress 0.2109643
-Run 17 stress 0.1825664
+Run 14 stress 0.1955867
+Run 15 stress 0.2137409
+Run 16 stress 0.2109638
+Run 17 stress 0.1825658
... New best solution
-... procrustes: rmse 0.0421789 max resid 0.1544029
-Run 18 stress 0.1843201
-Run 19 stress 0.2570123
+... procrustes: rmse 0.04169825 max resid 0.1521436
+Run 18 stress 0.1843197
+Run 19 stress 0.2570119
Run 20 stress 0.3760596
> stressplot(mod)
> gof <- goodness(mod)
> gof
- [1] 0.02985151 0.03513602 0.04181211 0.04601046 0.04005199 0.03443943
- [7] 0.03290447 0.03048909 0.03058684 0.02992410 0.03531117 0.02620375
-[13] 0.03833499 0.02979202 0.03376558 0.02225449 0.03559163 0.03504082
-[19] 0.06578666 0.03269039 0.03507122 0.02956140 0.05162094 0.04604686
+ [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
> plot(mod, display = "sites", type = "n")
> points(mod, display = "sites", cex = 2*gof/mean(gof))
>
@@ -3568,13 +3565,13 @@
> data(dune)
> # Global NMDS using monoMDS
> sol <- metaMDS(dune)
-Run 0 stress 0.1192691
-Run 1 stress 0.1808932
-Run 2 stress 0.1808918
-Run 3 stress 0.1808912
-Run 4 stress 0.2035431
-Run 5 stress 0.1192719
-... procrustes: rmse 0.001450932 max resid 0.004117039
+Run 0 stress 0.1192678
+Run 1 stress 0.1808913
+Run 2 stress 0.1808915
+Run 3 stress 0.1808911
+Run 4 stress 0.2035424
+Run 5 stress 0.119268
+... procrustes: rmse 0.0002214386 max resid 0.0006802088
*** Solution reached
> sol
@@ -3587,7 +3584,7 @@
Distance: bray
Dimensions: 2
-Stress: 0.1192691
+Stress: 0.1192678
Stress type 1, weak ties
Two convergent solutions found after 5 tries
Scaling: centring, PC rotation, halfchange scaling
@@ -3597,11 +3594,10 @@
> ## Start from previous best solution
> sol <- metaMDS(dune, previous.best = sol)
Starting from 2-dimensional configuration
-Run 0 stress 0.1192691
-Run 1 stress 0.1939205
-Run 2 stress 0.1192685
-... New best solution
-... procrustes: rmse 0.000737655 max resid 0.001867068
+Run 0 stress 0.1192678
+Run 1 stress 0.1939203
+Run 2 stress 0.119268
+... procrustes: rmse 0.0002104822 max resid 0.0006474109
*** Solution reached
> ## Local NMDS and stress 2 of monoMDS
> sol2 <- metaMDS(dune, model = "local", stress=2)
@@ -3628,9 +3624,10 @@
> ## Use Arrhenius exponent 'z' as a binary dissimilarity measure
> sol <- metaMDS(dune, distfun = betadiver, distance = "z")
-Run 0 stress 0.106717
-Run 1 stress 0.1067187
-... procrustes: rmse 0.0004025738 max resid 0.001408067
+Run 0 stress 0.1067169
+Run 1 stress 0.1067169
+... New best solution
+... procrustes: rmse 1.234853e-05 max resid 2.993581e-05
*** Solution reached
> sol
@@ -3643,7 +3640,7 @@
Distance: beta.z
Dimensions: 2
-Stress: 0.106717
+Stress: 0.1067169
Stress type 1, weak ties
Two convergent solutions found after 1 tries
Scaling: centring, PC rotation, halfchange scaling
@@ -3832,10 +3829,9 @@
> layout(matrix(1:2,nr=1))
>
> plot(dune.ord <- metaMDS(dune), type="text", display="sites" )
-Run 0 stress 0.1192691
-Run 1 stress 0.1192689
-... New best solution
-... procrustes: rmse 0.0006819572 max resid 0.001730883
+Run 0 stress 0.1192678
+Run 1 stress 0.119268
+... procrustes: rmse 8.18569e-05 max resid 0.0001982896
*** Solution reached
> ordihull(dune.ord, dune.env$Management)
>
@@ -4110,7 +4106,7 @@
> ### Name: nestedtemp
> ### Title: Nestedness Indices for Communities of Islands or Patches
> ### Aliases: nestedtemp nestedchecker nestedn0 nesteddisc nestednodf
-> ### nestedbetasor nestedbetajac plot.nestedtemp
+> ### nestedbetasor nestedbetajac plot.nestedtemp plot.nestednodf
> ### Keywords: univar
>
> ### ** Examples
@@ -4903,19 +4899,19 @@
> vare.mds <- monoMDS(vare.dist)
> with(varechem, ordisurf(vare.mds, Baresoil, bubble = 5))
Loading required package: mgcv
-This is mgcv 1.7-21. For overview type 'help("mgcv-package")'.
+This is mgcv 1.7-18. For overview type 'help("mgcv-package")'.
Family: gaussian
Link function: identity
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0xa05c300>
+<environment: 0x109ecd740>
Estimated degrees of freedom:
-6.44 total = 7.44
+6.4512 total = 7.451249
-GCV score: 144.1236
+GCV score: 144.0039
>
> ## as above but with extra penalties on smooth terms:
> with(varechem, ordisurf(vare.mds, Baresoil, bubble = 5, col = "blue",
@@ -4926,25 +4922,25 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x9df8f50>
+<environment: 0x10aa363f0>
Estimated degrees of freedom:
-6.1 total = 7.1
+6.1234 total = 7.123433
-GCV score: 140.0949
+GCV score: 139.9445
>
> ## Cover of Cladina arbuscula
> fit <- with(varespec, ordisurf(vare.mds, Cla.arb, family=quasipoisson))
> ## Get fitted values
> calibrate(fit)
1 2 3 4 5 6 7
-22.0644615 6.0132250 3.6350484 4.1019743 9.0030990 5.9202602 8.6399182
+22.0596535 6.0185659 3.6298559 4.1000950 8.9833600 5.9067472 8.6617389
8 9 10 11 12 13 14
-11.0719302 0.6561783 35.2282116 10.4346331 7.2900019 5.5710617 24.6503109
+11.0812152 0.6432691 35.2567124 10.4452454 7.2748478 5.5780162 24.6561685
15 16 17 18 19 20 21
-18.8754520 29.7286540 5.6158934 9.5869715 3.2876268 2.7111723 10.7832857
+18.8879906 29.7642964 5.6095920 9.5945524 3.2753633 2.6966143 10.7869351
22 23 24
- 3.0020415 9.8128952 7.3656934
+ 2.9902832 9.8082237 7.3406581
>
> ## Plot method
> plot(fit, what = "contour")
@@ -5049,10 +5045,6 @@
> ordicloud(ord, form = CA2 ~ CA3*CA1, groups = Manure, data = dune.env)
> ordicloud(ord, form = CA2 ~ CA3*CA1 | Management, groups = Manure,
+ data = dune.env, auto.key = TRUE, type = c("p","h"))
-Warning in FUN(X[[8L]], ...) : 'x' is NULL so the result will be NULL
-Warning in FUN(X[[9L]], ...) : 'x' is NULL so the result will be NULL
-Warning in FUN(X[[8L]], ...) : 'x' is NULL so the result will be NULL
-Warning in FUN(X[[9L]], ...) : 'x' is NULL so the result will be NULL
>
>
>
@@ -5079,17 +5071,17 @@
> ## Map of PCNMs in the sample plot
> ordisurf(mite.xy, scores(pcnm1, choi=1), bubble = 4, main = "PCNM 1")
Loading required package: mgcv
-This is mgcv 1.7-21. For overview type 'help("mgcv-package")'.
+This is mgcv 1.7-18. For overview type 'help("mgcv-package")'.
Family: gaussian
Link function: identity
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x702bcf8>
+<environment: 0x10b52c508>
Estimated degrees of freedom:
-8.93 total = 9.93
+8.9275 total = 9.927492
GCV score: 0.001054656
> ordisurf(mite.xy, scores(pcnm1, choi=2), bubble = 4, main = "PCNM 2")
@@ -5099,10 +5091,10 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x8de5c00>
+<environment: 0x10b144508>
Estimated degrees of freedom:
-7.75 total = 8.75
+7.7529 total = 8.75294
GCV score: 0.002284958
> ordisurf(mite.xy, scores(pcnm1, choi=3), bubble = 4, main = "PCNM 3")
@@ -5112,10 +5104,10 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x80c4518>
+<environment: 0x10aa31460>
Estimated degrees of freedom:
-8.9 total = 9.9
+8.8962 total = 9.89616
GCV score: 0.002508871
> par(op)
@@ -5854,7 +5846,7 @@
procrustes(X = mds.alt, Y = mds.null)
Procrustes sum of squares:
-13.11
+13.14
> summary(vare.proc)
@@ -5864,36 +5856,36 @@
Number of objects: 24 Number of dimensions: 2
Procrustes sum of squares:
- 13.10751
+ 13.1368
Procrustes root mean squared error:
- 0.739017
+ 0.7398422
Quantiles of Procrustes errors:
Min 1Q Median 3Q Max
-0.02322413 0.27259237 0.34146895 0.64121655 2.23350654
+0.02033237 0.27686842 0.34124239 0.64358542 2.23460568
Rotation matrix:
[,1] [,2]
-[1,] 0.9931369 -0.1169579
-[2,] 0.1169579 0.9931369
+[1,] 0.9933117 -0.1154641
+[2,] 0.1154641 0.9933117
Translation of averages:
[,1] [,2]
-[1,] 1.674651e-17 2.106557e-17
+[1,] 2.827507e-19 5.561347e-18
Scaling of target:
-[1] 0.6736868
+[1] 0.6727804
> plot(vare.proc)
> plot(vare.proc, kind=2)
> residuals(vare.proc)
18 15 24 27 23 19 22
-0.27803992 0.12847298 1.72993477 0.63690082 0.37244204 0.11829836 0.38555390
+0.28003467 0.13180094 1.72990690 0.63953155 0.37177564 0.11807007 0.38558576
16 28 13 14 20 25 7
-0.27735743 1.22675827 0.65416375 0.25829720 0.02322413 0.35904126 0.28489676
+0.28008712 1.22839229 0.65574702 0.26736966 0.02033237 0.35725398 0.28654874
5 6 3 4 2 9 12
-0.69505200 0.19983445 0.29397151 0.50089415 2.23350654 0.31836035 0.21622926
+0.69324047 0.20264726 0.29744269 0.50070811 2.23460568 0.31901411 0.21727135
10 11 21
-0.32389663 0.56349530 0.77939208
+0.32523080 0.56506437 0.78179902
>
>
>
@@ -5971,9 +5963,6 @@
Zipf 50.1262 47.9108 30.936
Mandelbrot 5.7342 5.5665 10.573
> plot(mod, pch=".")
-Warning in FUN(X[[8L]], ...) : 'x' is NULL so the result will be NULL
-Warning in FUN(X[[9L]], ...) : 'x' is NULL so the result will be NULL
-Warning in FUN(X[[7L]], ...) : 'x' is NULL so the result will be NULL
>
>
>
@@ -7469,28 +7458,28 @@
> ord <- metaMDS(varespec)
Square root transformation
Wisconsin double standardization
-Run 0 stress 0.1843204
-Run 1 stress 0.2455937
-Run 2 stress 0.2169416
-Run 3 stress 0.2313238
-Run 4 stress 0.1974421
-Run 5 stress 0.1858424
-Run 6 stress 0.1948436
-Run 7 stress 0.2265718
-Run 8 stress 0.2225085
-Run 9 stress 0.2023228
+Run 0 stress 0.1843196
+Run 1 stress 0.2455912
+Run 2 stress 0.2169407
+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 11 stress 0.1976154
-Run 12 stress 0.1852405
+Run 11 stress 0.1976151
+Run 12 stress 0.1852397
Run 13 stress 0.2341085
-Run 14 stress 0.1955872
-Run 15 stress 0.2137414
-Run 16 stress 0.2109643
-Run 17 stress 0.1825664
+Run 14 stress 0.1955867
+Run 15 stress 0.2137409
+Run 16 stress 0.2109638
+Run 17 stress 0.1825658
... New best solution
-... procrustes: rmse 0.0421789 max resid 0.1544029
-Run 18 stress 0.1843201
-Run 19 stress 0.2570123
+... procrustes: rmse 0.04169825 max resid 0.1521436
+Run 18 stress 0.1843197
+Run 19 stress 0.2570119
Run 20 stress 0.3760596
> plot(ord, type = "t")
> ## Fit environmental variables
@@ -7500,20 +7489,20 @@
***VECTORS
NMDS1 NMDS2 r2 Pr(>r)
-N -0.056188 -0.998420 0.2542 0.046 *
-P 0.618628 0.785684 0.1936 0.103
-K 0.765142 0.643862 0.1809 0.143
-Ca 0.683985 0.729496 0.4120 0.008 **
-Mg 0.631457 0.775411 0.4273 0.004 **
-S 0.190091 0.981767 0.1754 0.140
-Al -0.872443 0.488715 0.5270 0.001 ***
-Fe -0.937013 0.349294 0.4452 0.002 **
-Mn 0.799058 -0.601253 0.5228 0.001 ***
-Zn 0.616932 0.787017 0.1878 0.125
-Mo -0.903112 0.429406 0.0609 0.539
-Baresoil 0.926090 -0.377302 0.2509 0.038 *
-Humdepth 0.933540 -0.358473 0.5198 0.002 **
-pH -0.648970 0.760814 0.2306 0.060 .
+N -0.057194 -0.998363 0.2537 0.046 *
+P 0.619593 0.784923 0.1938 0.103
+K 0.766293 0.642492 0.1809 0.143
+Ca 0.685057 0.728489 0.4119 0.008 **
+Mg 0.632400 0.774642 0.4271 0.004 **
+S 0.191230 0.981545 0.1752 0.140
+Al -0.871691 0.490056 0.5269 0.001 ***
+Fe -0.936135 0.351641 0.4451 0.002 **
+Mn 0.798733 -0.601685 0.5230 0.001 ***
+Zn 0.617495 0.786575 0.1879 0.125
+Mo -0.903045 0.429546 0.0609 0.537
+Baresoil 0.925034 -0.379885 0.2508 0.039 *
+Humdepth 0.932909 -0.360112 0.5200 0.002 **
+pH -0.648094 0.761560 0.2308 0.060 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
P values based on 999 permutations.
@@ -7659,14 +7648,14 @@
> ## add fitted surface of diversity to the model
> ordisurf(mod, diversity(dune), add = TRUE)
Loading required package: mgcv
-This is mgcv 1.7-21. For overview type 'help("mgcv-package")'.
+This is mgcv 1.7-18. For overview type 'help("mgcv-package")'.
Family: gaussian
Link function: identity
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0xac90180>
+<environment: 0x10a933c98>
Estimated degrees of freedom:
2 total = 3
@@ -8113,13 +8102,13 @@
> ## Eigevalues are numerically similar
> ca$CA$eig - ord$eig
CA1 CA2 CA3 CA4 CA5
--9.992007e-16 -1.665335e-16 6.661338e-16 -3.330669e-16 -1.110223e-16
+-7.771561e-16 -2.220446e-16 6.106227e-16 -3.608225e-16 -1.110223e-16
CA6 CA7 CA8 CA9 CA10
- 2.775558e-17 1.249001e-16 1.387779e-17 1.387779e-17 1.249001e-16
+ 1.387779e-17 9.714451e-17 1.387779e-17 2.775558e-17 1.318390e-16
CA11 CA12 CA13 CA14 CA15
- 8.326673e-17 6.938894e-18 -1.387779e-17 2.775558e-17 0.000000e+00
+ 9.714451e-17 6.938894e-18 -6.938894e-18 3.122502e-17 0.000000e+00
CA16 CA17 CA18 CA19
--3.295975e-17 2.428613e-17 2.949030e-17 5.637851e-18
+-3.295975e-17 2.428613e-17 2.862294e-17 5.637851e-18
> ## Configurations are similar when site scores are scaled by
> ## eigenvalues in CA
> procrustes(ord, ca, choices=1:19, scaling = 1)
@@ -8128,7 +8117,7 @@
procrustes(X = ord, Y = ca, choices = 1:19, scaling = 1)
Procrustes sum of squares:
--4.263e-14
+ 0
> plot(procrustes(ord, ca, choices=1:2, scaling=1))
> ## Reconstruction of non-Euclidean distances with negative eigenvalues
@@ -8146,7 +8135,7 @@
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", pos = 'CheckExEnv'),"\n")
-Time elapsed: 25.917 0.18 26.257 0 0
+Time elapsed: 81.69 1.562 86.604 0 0
> grDevices::dev.off()
null device
1
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