[Vegan-commits] r2709 - pkg/vegan/tests/Examples
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Fri Nov 15 14:02:44 CET 2013
Author: jarioksa
Date: 2013-11-15 14:02:44 +0100 (Fri, 15 Nov 2013)
New Revision: 2709
Modified:
pkg/vegan/tests/Examples/vegan-Ex.Rout.save
Log:
update examples
Modified: pkg/vegan/tests/Examples/vegan-Ex.Rout.save
===================================================================
--- pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2013-11-15 12:37:55 UTC (rev 2708)
+++ pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2013-11-15 13:02:44 UTC (rev 2709)
@@ -1,5 +1,5 @@
-R Under development (unstable) (2013-07-10 r63264) -- "Unsuffered Consequences"
+R Under development (unstable) (2013-11-15 r64218) -- "Unsuffered Consequences"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: x86_64-unknown-linux-gnu (64-bit)
@@ -22,7 +22,8 @@
> options(warn = 1)
> library('vegan')
Loading required package: permute
-This is vegan 2.1-32
+Loading required package: lattice
+This is vegan 2.1-39
>
> base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
> cleanEx()
@@ -153,14 +154,15 @@
> plot(ef)
> ordisurf(mod ~ pH, varechem, knots = 1, add = TRUE)
Loading required package: mgcv
-This is mgcv 1.7-24. For overview type 'help("mgcv-package")'.
+Loading required package: nlme
+This is mgcv 1.7-26. For overview type 'help("mgcv-package")'.
Family: gaussian
Link function: identity
Formula:
y ~ poly(x1, 1) + poly(x2, 1)
-<environment: 0x3371698>
+<environment: 0x280d1f8>
Total model degrees of freedom 3
REML score: -3.185099
@@ -169,7 +171,7 @@
>
> cleanEx()
-detaching ‘package:mgcv’
+detaching ‘package:mgcv’, ‘package:nlme’
> nameEx("MOStest")
> ### * MOStest
@@ -394,12 +396,12 @@
dune ~ 1
Df AIC F N.Perm Pr(>F)
-+ Moisture 3 86.608 2.2536 199 0.005 **
++ Moisture 3 86.608 2.2536 199 0.010 **
+ Management 3 86.935 2.1307 199 0.005 **
-+ A1 1 87.411 2.1400 199 0.025 *
++ A1 1 87.411 2.1400 199 0.020 *
<none> 87.657
-+ Manure 4 88.832 1.5251 199 0.020 *
-+ Use 2 89.134 1.1431 99 0.260
++ Manure 4 88.832 1.5251 199 0.025 *
++ Use 2 89.134 1.1431 99 0.250
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
@@ -408,11 +410,11 @@
Df AIC F N.Perm Pr(>F)
<none> 86.608
-+ Management 3 86.813 1.4565 199 0.035 *
-+ A1 1 86.992 1.2624 99 0.190
-+ Use 2 87.259 1.2760 99 0.130
-+ Manure 4 87.342 1.3143 199 0.075 .
-- Moisture 3 87.657 2.2536 199 0.010 **
++ Management 3 86.813 1.4565 199 0.020 *
++ A1 1 86.992 1.2624 99 0.150
++ Use 2 87.259 1.2760 199 0.120
++ Manure 4 87.342 1.3143 199 0.090 .
+- Moisture 3 87.657 2.2536 199 0.005 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Call: cca(formula = dune ~ Moisture, data = dune.env)
@@ -446,21 +448,21 @@
> add1(m0, scope=formula(mbig), test="perm")
Df AIC F N.Perm Pr(>F)
<none> 89.620
-A1 1 89.591 1.9217 199 0.030 *
+A1 1 89.591 1.9217 199 0.070 .
Moisture 3 87.707 2.5883 199 0.005 **
Management 3 87.082 2.8400 199 0.005 **
-Use 2 91.032 1.1741 99 0.170
+Use 2 91.032 1.1741 99 0.180
Manure 4 89.232 1.9539 199 0.010 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> m0 <- update(m0, . ~ . + Management)
> add1(m0, scope=formula(mbig), test="perm")
- Df AIC F N.Perm Pr(>F)
-<none> 87.082
-A1 1 87.424 1.2965 99 0.200
-Moisture 3 85.567 1.9764 199 0.015 *
-Use 2 88.284 1.0510 99 0.470
-Manure 3 87.517 1.3902 199 0.135
+ Df AIC F N.Perm Pr(>F)
+<none> 87.082
+A1 1 87.424 1.2965 99 0.240
+Moisture 3 85.567 1.9764 199 0.005 **
+Use 2 88.284 1.0510 99 0.430
+Manure 3 87.517 1.3902 199 0.130
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> m0 <- update(m0, . ~ . + Moisture)
@@ -468,16 +470,16 @@
> drop1(m0, test="perm")
Df AIC F N.Perm Pr(>F)
<none> 85.567
-Management 3 87.707 2.1769 199 0.005 **
-Moisture 3 87.082 1.9764 199 0.010 **
+Management 3 87.707 2.1769 199 0.010 **
+Moisture 3 87.082 1.9764 199 0.015 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> add1(m0, scope=formula(mbig), test="perm")
Df AIC F N.Perm Pr(>F)
<none> 85.567
-A1 1 86.220 0.8359 99 0.62
-Use 2 86.842 0.8027 99 0.72
-Manure 3 85.762 1.1225 99 0.27
+A1 1 86.220 0.8359 99 0.72
+Use 2 86.842 0.8027 99 0.77
+Manure 3 85.762 1.1225 99 0.26
>
>
>
@@ -664,7 +666,6 @@
> Agropyron <- with(dat, as.numeric(field) + as.numeric(NO3)+2) +rnorm(12)/2
> Schizachyrium <- with(dat, as.numeric(field) - as.numeric(NO3)+2) +rnorm(12)/2
> total <- Agropyron + Schizachyrium
-> library(lattice)
> dotplot(total ~ NO3, dat, jitter.x=TRUE, groups=field,
+ type=c('p','a'), xlab="NO3", auto.key=list(columns=3, lines=TRUE) )
>
@@ -716,9 +717,6 @@
>
>
> cleanEx()
-
-detaching ‘package:lattice’
-
> nameEx("anosim")
> ### * anosim
>
@@ -847,6 +845,36 @@
>
>
> cleanEx()
+> nameEx("anovacca")
+> ### * anovacca
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: anovacca
+> ### Title: Permutation Test for Constrained Correspondence Analysis,
+> ### Redundancy Analysis and Constrained Analysis of Principal Coordinates
+> ### Aliases: anovacca
+> ### Keywords: multivariate htest
+>
+> ### ** Examples
+>
+> data(varespec)
+> data(varechem)
+> vare.cca <- cca(varespec ~ Al + P + K, varechem)
+> ## overall test
+> anovacca(vare.cca)
+Permutation test for cca under reduced model
+
+Model: cca(formula = varespec ~ Al + P + K, data = varechem)
+ Df Chisq F N.Perm Pr(>F)
+Model 3 0.6441 2.9840 999 0.001 ***
+Residual 20 1.4391
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+>
+>
+>
+> cleanEx()
> nameEx("as.mlm")
> ### * as.mlm
>
@@ -1116,7 +1144,7 @@
No. of Positive Eigenvalues: 15
No. of Negative Eigenvalues: 8
-Average distance to medoid:
+Average distance to median:
grazed ungrazed
0.3926 0.2706
@@ -1211,7 +1239,7 @@
27 -0.32914546 -0.17019348 0.231623720 0.019110623
23 -0.19259443 -0.01459250 -0.005679372 -0.209718312
19 -0.06794575 -0.14501690 -0.085645653 0.002431355
-> # group centroids/medoids
+> # group centroids/medians
> scores(mod, 1:4, display = "centroids")
PCoA1 PCoA2 PCoA3 PCoA4
grazed -0.1455200 0.07584572 -0.01366220 -0.0178990
@@ -1238,7 +1266,7 @@
No. of Positive Eigenvalues: 15
No. of Negative Eigenvalues: 8
-Average distance to medoid:
+Average distance to median:
grazed ungrazed
0.4055 0.2893
@@ -1257,7 +1285,7 @@
No. of Positive Eigenvalues: 15
No. of Negative Eigenvalues: 8
-Average distance to medoid:
+Average distance to median:
grazed
0.4255
@@ -1299,7 +1327,7 @@
No. of Positive Eigenvalues: 14
No. of Negative Eigenvalues: 5
-Average distance to medoid:
+Average distance to median:
grazed ungrazed
0.3984 0.3008
@@ -1427,8 +1455,10 @@
3 "c" = (b+c)/2
4 "wb" = b+c
5 "r" = 2*b*c/((a+b+c)^2-2*b*c)
-6 "I" = log(2*a+b+c)-2*a*log(2)/(2*a+b+c)-((a+b)*log(a+b)+(a+c)*log(a+c))/(2*a+b+c)
-7 "e" = exp(log(2*a+b+c)-2*a*log(2)/(2*a+b+c)-((a+b)*log(a+b)+(a+c)*log(a+c))/(2*a+b+c))-1
+6 "I" = log(2*a+b+c) - 2*a*log(2)/(2*a+b+c) - ((a+b)*log(a+b) +
+(a+c)*log(a+c)) / (2*a+b+c)
+7 "e" = exp(log(2*a+b+c) - 2*a*log(2)/(2*a+b+c) - ((a+b)*log(a+b) +
+(a+c)*log(a+c)) / (2*a+b+c))-1
8 "t" = (b+c)/(2*a+b+c)
9 "me" = (b+c)/(2*a+b+c)
10 "j" = a/(a+b+c)
@@ -1526,7 +1556,7 @@
> ### Name: bioenv
> ### Title: Best Subset of Environmental Variables with Maximum (Rank)
> ### Correlation with Community Dissimilarities
-> ### Aliases: bioenv bioenv.default bioenv.formula summary.bioenv
+> ### Aliases: bioenv bioenv.default bioenv.formula summary.bioenv bioenvdist
> ### Keywords: multivariate
>
> ### ** Examples
@@ -1608,10 +1638,10 @@
varechem, distance = "bray")
Inertia Proportion Rank
-Total 4.5444
-Real Total 4.8034 1.0000
-Conditional 0.9772 0.2034 1
-Constrained 0.9972 0.2076 3
+Total 4.5440
+Real Total 4.8030 1.0000
+Conditional 0.9770 0.2034 1
+Constrained 0.9970 0.2076 3
Unconstrained 2.8290 0.5890 15
Imaginary -0.2590 8
Inertia is squared Bray distance
@@ -1643,10 +1673,10 @@
varechem, distance = "bray", add = TRUE)
Inertia Proportion Rank
-Total 8.9643 1.0000
-Conditional 1.5053 0.1679 1
-Constrained 1.7171 0.1915 3
-Unconstrained 5.7419 0.6405 19
+Total 8.9640 1.0000
+Conditional 1.5050 0.1679 1
+Constrained 1.7170 0.1915 3
+Unconstrained 5.7420 0.6405 19
Inertia is squared Bray distance (euclidified)
Eigenvalues for constrained axes:
@@ -1668,9 +1698,9 @@
Inertia Proportion Rank
Total 6.9500 1.0000
-Conditional 0.9535 0.1372 1
-Constrained 1.2267 0.1765 3
-Unconstrained 4.7698 0.6863 19
+Conditional 0.9530 0.1372 1
+Constrained 1.2270 0.1765 3
+Unconstrained 4.7700 0.6863 19
Inertia is Bray distance
Eigenvalues for constrained axes:
@@ -1689,11 +1719,11 @@
Call: capscale(formula = varespec ~ 1, distance = "bray", metaMDSdist =
TRUE)
- Inertia Rank
-Total 2.54753
-Real Total 2.59500
-Unconstrained 2.59500 19
-Imaginary -0.04747 4
+ Inertia Rank
+Total 2.5475
+Real Total 2.5950
+Unconstrained 2.5950 19
+Imaginary -0.0475 4
Inertia is squared Bray distance
Eigenvalues for unconstrained axes:
@@ -1804,9 +1834,9 @@
Call: cca(formula = varespec ~ Ca, data = varechem)
Inertia Proportion Rank
-Total 2.08320 1.00000
-Constrained 0.15722 0.07547 1
-Unconstrained 1.92598 0.92453 22
+Total 2.0832 1.0000
+Constrained 0.1572 0.0755 1
+Unconstrained 1.9260 0.9245 22
Inertia is mean squared contingency coefficient
Eigenvalues for constrained axes:
@@ -2294,16 +2324,12 @@
> data(dune.env)
> mod <- adonis(dune ~ Management, data = dune.env)
> plot(density(mod))
-> library(lattice)
> mod <- adonis(dune ~ Management * Moisture, dune.env)
> densityplot(mod)
>
>
>
> cleanEx()
-
-detaching ‘package:lattice’
-
> nameEx("designdist")
> ### * designdist
>
@@ -2668,6 +2694,8 @@
> plot(S, Srare, xlab = "Observed No. of Species", ylab = "Rarefied No. of Species")
> abline(0, 1)
> rarecurve(BCI, step = 20, sample = raremax, col = "blue", cex = 0.6)
+Warning in rep(border, length = nrow(x)) :
+ 'x' is NULL so the result will be NULL
>
>
>
@@ -2801,21 +2829,21 @@
***VECTORS
- NMDS1 NMDS2 r2 Pr(>r)
-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 .
+ NMDS1 NMDS2 r2 Pr(>r)
+N -0.05719 -0.99836 0.2537 0.046 *
+P 0.61959 0.78492 0.1938 0.103
+K 0.76629 0.64249 0.1809 0.143
+Ca 0.68506 0.72849 0.4119 0.008 **
+Mg 0.63240 0.77464 0.4271 0.004 **
+S 0.19123 0.98155 0.1752 0.140
+Al -0.87169 0.49006 0.5269 0.001 ***
+Fe -0.93613 0.35164 0.4451 0.002 **
+Mn 0.79873 -0.60169 0.5230 0.001 ***
+Zn 0.61750 0.78657 0.1879 0.125
+Mo -0.90304 0.42955 0.0609 0.537
+Baresoil 0.92503 -0.37988 0.2508 0.039 *
+Humdepth 0.93291 -0.36011 0.5200 0.002 **
+pH -0.64809 0.76156 0.2308 0.060 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
P values based on 999 permutations.
@@ -2859,6 +2887,10 @@
> ordispider(ord, Moisture, col="skyblue")
> points(ord, display = "sites", col = as.numeric(Moisture), pch=16)
> plot(fit, cex=1.2, axis=TRUE, bg = rgb(1, 1, 1, 0.5))
+Warning in rep(border, length = nrow(x)) :
+ 'x' is NULL so the result will be NULL
+Warning in rep(border, length = nrow(x)) :
+ 'x' is NULL so the result will be NULL
> ## Use shorter labels for factor centroids
> labels(fit)
$vectors
@@ -2870,6 +2902,10 @@
> plot(ord)
> plot(fit, labels=list(factors = paste("M", c(1,2,4,5), sep = "")),
+ bg = rgb(1,1,0,0.5))
+Warning in rep(border, length = nrow(x)) :
+ 'x' is NULL so the result will be NULL
+Warning in rep(border, length = nrow(x)) :
+ 'x' is NULL so the result will be NULL
>
>
>
@@ -2927,8 +2963,7 @@
> ### Name: fisherfit
> ### Title: Fit Fisher's Logseries and Preston's Lognormal Model to
> ### Abundance Data
-> ### Aliases: fisherfit as.fisher plot.fisherfit profile.fisherfit
-> ### confint.fisherfit plot.profile.fisherfit prestonfit prestondistr
+> ### Aliases: fisherfit as.fisher plot.fisherfit prestonfit prestondistr
> ### as.preston plot.prestonfit lines.prestonfit plot.preston
> ### lines.preston plot.fisher veiledspec
> ### Keywords: univar distribution
@@ -2941,15 +2976,8 @@
Fisher log series model
No. of species: 101
+Fisher alpha: 37.96423
- Estimate Std. Error
-alpha 37.964 4.6847
-
-> plot(profile(mod))
-> confint(mod)
-Loading required package: MASS
- 2.5 % 97.5 %
-29.65932 48.12558
> # prestonfit seems to need large samples
> mod.oct <- prestonfit(colSums(BCI))
> mod.ll <- prestondistr(colSums(BCI))
@@ -3003,9 +3031,6 @@
>
>
> cleanEx()
-
-detaching ‘package:MASS’
-
> nameEx("goodness.cca")
> ### * goodness.cca
>
@@ -3344,6 +3369,8 @@
> fun <- function(x, i) indpower(x)[i,-i]
> ## 'c0' randomizes species occurrences
> os <- oecosimu(dune, fun, "c0", i=i, nsimul=99)
+Warning in oecosimu(dune, fun, "c0", i = i, nsimul = 99) :
+ nullmodel transformed 'comm' to binary data
> ## get z values from oecosimu output
> z <- os$oecosimu$z
> ## p-value
@@ -4492,20 +4519,18 @@
Axis lengths 2.9197 2.5442 2.7546 1.78074
- statistic z mean 5% 50% 100% Pr(sim.)
-DCA1 0.382249 1.8658 0.32404 0.27338 0.32373 0.3918 0.02 *
-DCA2 0.261208 1.5772 0.21939 0.17891 0.21617 0.2899 0.09 .
-DCA3 0.166788 0.5209 0.15594 0.12380 0.15572 0.2361 0.30
-DCA4 0.087226 -1.9822 0.13015 0.10335 0.12649 0.2011 0.99
+ statistic z mean 0% 50% 95% Pr(sim.)
+DCA1 0.382249 1.8658 0.324042 0.230869 0.323729 0.3726 0.02 *
+DCA2 0.261208 1.5772 0.219388 0.149703 0.216169 0.2637 0.09 .
+DCA3 0.166788 0.5209 0.155941 0.105269 0.155716 0.1859 0.30
+DCA4 0.087226 -1.9822 0.130151 0.066742 0.126492 0.1649 0.99
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> ## Inspect the swap sequence as a time series object
> plot(as.ts(out))
> lag.plot(as.ts(out))
> acf(as.ts(out))
-> ## Density plot: needs lattice
-> require(lattice)
-Loading required package: lattice
+> ## Density plot
> densityplot(out, as.table = TRUE)
> ## Use quantitative null models to compare
> ## mean Bray-Curtis dissimilarities
@@ -4553,9 +4578,6 @@
>
>
> cleanEx()
-
-detaching ‘package:lattice’
-
> nameEx("ordiarrows")
> ### * ordiarrows
>
@@ -4581,12 +4603,18 @@
> plot(mod, type = "n")
> ## Annual succession by ditches
> ordiarrows(mod, ditch, label = TRUE)
+Warning in rep(border, length = nrow(x)) :
+ 'x' is NULL so the result will be NULL
> ## Show only control and highest Pyrifos treatment
> plot(mod, type = "n")
> ordiarrows(mod, ditch, label = TRUE,
+ show.groups = c("2", "3", "5", "11"))
+Warning in rep(border, length = nrow(x)) :
+ 'x' is NULL so the result will be NULL
> ordiarrows(mod, ditch, label = TRUE, show = c("6", "9"),
+ col = 2)
+Warning in rep(border, length = nrow(x)) :
+ 'x' is NULL so the result will be NULL
> legend("topright", c("Control", "Pyrifos 44"), lty = 1, col = c(1,2))
>
>
@@ -4624,6 +4652,8 @@
> plot(mod, type = "n")
> pl <- ordihull(mod, Management, scaling = 3)
> ordispider(pl, col="red", lty=3, label = TRUE )
+Warning in rep(border, length = nrow(x)) :
+ 'x' is NULL so the result will be NULL
> ## ordispider to connect WA and LC scores
> plot(mod, dis=c("wa","lc"), type="p")
> ordispider(mod)
@@ -4662,8 +4692,12 @@
> ord <- cca(dune)
> plot(ord, type = "n")
> ordilabel(ord, dis="sites", cex=1.2, font=3, fill="hotpink", col="blue")
+Warning in rep(border, length = nrow(x)) :
+ 'x' is NULL so the result will be NULL
> ## You may prefer separate plots, but here species as well
> ordilabel(ord, dis="sp", font=2, priority=colSums(dune))
+Warning in rep(border, length = nrow(x)) :
+ 'x' is NULL so the result will be NULL
>
>
>
@@ -4797,14 +4831,10 @@
> data(varechem)
> mod <- cca(varespec ~ Al + P + K, varechem)
> ordiresids(mod)
-Loading required package: lattice
>
>
>
> cleanEx()
-
-detaching ‘package:lattice’
-
> nameEx("ordistep")
> ### * ordistep
>
@@ -4870,9 +4900,9 @@
Call: rda(formula = dune ~ Management + Moisture, data = dune.env)
Inertia Proportion Rank
-Total 84.1237 1.0000
-Constrained 46.4249 0.5519 6
-Unconstrained 37.6988 0.4481 13
+Total 84.1200 1.0000
+Constrained 46.4200 0.5519 6
+Unconstrained 37.7000 0.4481 13
Inertia is variance
Eigenvalues for constrained axes:
@@ -4924,9 +4954,9 @@
Call: rda(formula = dune ~ Moisture + Manure, data = dune.env)
Inertia Proportion Rank
-Total 84.1237 1.0000
-Constrained 49.1609 0.5844 7
-Unconstrained 34.9628 0.4156 12
+Total 84.1200 1.0000
+Constrained 49.1600 0.5844 7
+Unconstrained 34.9600 0.4156 12
Inertia is variance
Eigenvalues for constrained axes:
@@ -5088,14 +5118,15 @@
> vare.mds <- monoMDS(vare.dist)
> with(varechem, ordisurf(vare.mds, Baresoil, bubble = 5))
Loading required package: mgcv
-This is mgcv 1.7-24. For overview type 'help("mgcv-package")'.
+Loading required package: nlme
+This is mgcv 1.7-26. For overview type 'help("mgcv-package")'.
Family: gaussian
Link function: identity
Formula:
y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
-<environment: 0xa7eec90>
+<environment: 0x9d2c888>
Estimated degrees of freedom:
5.63 total = 6.63
@@ -5112,7 +5143,7 @@
Formula:
y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
-<environment: 0x8ac38d0>
+<environment: 0x857e428>
Estimated degrees of freedom:
6.45 total = 7.45
@@ -5143,7 +5174,7 @@
Formula:
y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
-<environment: 0xa9f8930>
+<environment: 0x9f03880>
Estimated degrees of freedom:
5.63 total = 6.63
@@ -5158,7 +5189,7 @@
Formula:
y ~ s(x1, x2, k = 10, bs = "ts", fx = FALSE)
-<environment: 0x9948930>
+<environment: 0x8ec7f98>
Estimated degrees of freedom:
4.43 total = 5.43
@@ -5184,7 +5215,7 @@
Formula:
y ~ s(x1, x2, k = 10, bs = "ds", fx = FALSE)
-<environment: 0xb22f4c0>
+<environment: 0xa262658>
Estimated degrees of freedom:
5.63 total = 6.63
@@ -5200,7 +5231,7 @@
Formula:
y ~ s(x1, x2, k = 4, bs = "tp", fx = TRUE)
-<environment: 0xaa2a810>
+<environment: 0x9e03b88>
Estimated degrees of freedom:
3 total = 4
@@ -5217,7 +5248,7 @@
Formula:
y ~ te(x1, x2, k = c(4, 4), bs = c("cr", "cr"), fx = c(FALSE,
FALSE))
-<environment: 0xae83dd0>
+<environment: 0x9a49fc0>
Estimated degrees of freedom:
2.99 total = 3.99
@@ -5236,7 +5267,7 @@
Formula:
y ~ te(x1, x2, k = c(3, 4), bs = c("cs", "cs"), fx = c(TRUE,
TRUE))
-<environment: 0xa30a9e0>
+<environment: 0x9d149d8>
Estimated degrees of freedom:
11 total = 12
@@ -5248,7 +5279,7 @@
>
> cleanEx()
-detaching ‘package:mgcv’
+detaching ‘package:mgcv’, ‘package:nlme’
> nameEx("orditkplot")
> ### * orditkplot
@@ -5334,7 +5365,6 @@
> ord <- cca(dune)
> ## Pairs plots
> ordisplom(ord)
-Loading required package: lattice
> ordisplom(ord, data=dune.env, choices=1:2)
> ordisplom(ord, data=dune.env, form = ~ . | Management, groups=Manure)
> ## Scatter plot
@@ -5354,9 +5384,6 @@
>
>
> cleanEx()
-
-detaching ‘package:lattice’
-
> nameEx("pcnm")
> ### * pcnm
>
@@ -5376,14 +5403,15 @@
> ## 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 1.7-24. For overview type 'help("mgcv-package")'.
+Loading required package: nlme
+This is mgcv 1.7-26. For overview type 'help("mgcv-package")'.
Family: gaussian
Link function: identity
Formula:
y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
-<environment: 0x92f3b20>
+<environment: 0x97f7ee0>
Estimated degrees of freedom:
8.71 total = 9.71
@@ -5396,7 +5424,7 @@
Formula:
y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
-<environment: 0x765c640>
+<environment: 0x856d9e8>
Estimated degrees of freedom:
7.18 total = 8.18
@@ -5409,7 +5437,7 @@
Formula:
y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
-<environment: 0xa083c70>
+<environment: 0x9205860>
Estimated degrees of freedom:
8.32 total = 9.32
@@ -5418,7 +5446,6 @@
> par(op)
> ## Plot first PCNMs against each other
> ordisplom(pcnm1, choices=1:4)
-Loading required package: lattice
> ## Weighted PCNM for CCA
> data(mite)
> rs <- rowSums(mite)/sum(mite)
@@ -5433,7 +5460,7 @@
> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
> cleanEx()
-detaching ‘package:lattice’, ‘package:mgcv’
+detaching ‘package:mgcv’, ‘package:nlme’
> nameEx("permatfull")
> ### * permatfull
@@ -5682,7 +5709,7 @@
No. of Positive Eigenvalues: 15
No. of Negative Eigenvalues: 8
-Average distance to medoid:
+Average distance to median:
grazed ungrazed
0.3926 0.2706
@@ -5905,10 +5932,10 @@
Call: prc(response = pyrifos, treatment = dose, time = week)
Inertia Proportion Rank
-Total 288.9920 1.0000
-Conditional 63.3493 0.2192 10
-Constrained 96.6837 0.3346 44
-Unconstrained 128.9589 0.4462 77
+Total 288.9900 1.0000
+Conditional 63.3500 0.2192 10
+Constrained 96.6800 0.3346 44
+Unconstrained 128.9600 0.4462 77
Inertia is variance
Eigenvalues for constrained axes:
@@ -6254,7 +6281,6 @@
> plot(mod, log = "xy")
> ## Lattice graphics separately for each model
> radlattice(mod)
-Loading required package: lattice
> # Take a subset of BCI to save time and nerves
> mod <- radfit(BCI[3:5,])
> mod
@@ -6272,9 +6298,6 @@
>
>
> cleanEx()
-
-detaching ‘package:lattice’
-
> nameEx("rankindex")
> ### * rankindex
>
@@ -6380,7 +6403,6 @@
> i <- sample(nrow(BCI), 12)
> mod <- renyi(BCI[i,])
> plot(mod)
-Loading required package: lattice
> mod <- renyiaccum(BCI[i,])
> plot(mod, as.table=TRUE, col = c(1, 2, 2))
> persp(mod)
@@ -6388,9 +6410,6 @@
>
>
> cleanEx()
-
-detaching ‘package:lattice’
-
> nameEx("scores")
> ### * scores
>
@@ -6747,9 +6766,9 @@
dune.env)
Inertia Proportion Rank
-Total 87.7829 1.0000
-Constrained 62.2577 0.7092 6
-Unconstrained 25.5252 0.2908 13
+Total 87.7800 1.0000
+Constrained 62.2600 0.7092 6
+Unconstrained 25.5300 0.2908 13
Inertia is variance
Eigenvalues for constrained axes:
@@ -6813,6 +6832,8 @@
> ## Add tree to a metric scaling
> plot(tr, cmdscale(dis), type = "t")
Loading required package: MASS
+Warning in rep(border, length = nrow(x)) :
+ 'x' is NULL so the result will be NULL
> ## Find a configuration to display the tree neatly
> plot(tr, type = "t")
Initial stress : 0.03111
@@ -6820,6 +6841,8 @@
stress after 20 iters: 0.01139, magic = 0.500
stress after 30 iters: 0.01118, magic = 0.500
stress after 40 iters: 0.01114, magic = 0.500
+Warning in rep(border, length = nrow(x)) :
+ 'x' is NULL so the result will be NULL
> ## Depths of nodes
> depths <- spandepth(tr)
> plot(tr, type = "t", label = depths)
@@ -6828,6 +6851,8 @@
stress after 20 iters: 0.01139, magic = 0.500
stress after 30 iters: 0.01118, magic = 0.500
stress after 40 iters: 0.01114, magic = 0.500
+Warning in rep(border, length = nrow(x)) :
+ 'x' is NULL so the result will be NULL
>
>
>
@@ -7105,7 +7130,6 @@
attr(,"class")
[1] "summary.poolaccum"
> plot(pool)
-Loading required package: lattice
> ## Quantitative model
> estimateR(BCI[1:5,])
1 2 3 4 5
@@ -7120,7 +7144,7 @@
> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
> cleanEx()
-detaching ‘package:lattice’, ‘dune.env’
+detaching ‘dune.env’
> nameEx("stepacross")
> ### * stepacross
@@ -7365,6 +7389,8 @@
> ## Significance test using Null model communities.
> ## The current choice fixes only site totals.
> oecosimu(dune, treedive, "r0", tree = cl)
+Warning in oecosimu(dune, treedive, "r0", tree = cl) :
+ nullmodel transformed 'comm' to binary data
oecosimu object
Call: oecosimu(comm = dune, nestfun = treedive, method = "r0", tree =
@@ -7450,7 +7476,6 @@
14 19 28 43 10 41 42 29 27 3 9 7
TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
> plot(x1)
-Loading required package: lattice
> x2 <- tsallis(BCI[i,],norm=TRUE)
> x2
0 0.2 0.4 0.6 0.8 1 1.2 1.4
@@ -7489,9 +7514,6 @@
>
>
> cleanEx()
-
-detaching ‘package:lattice’
-
> nameEx("varechem")
> ### * varechem
>
@@ -7805,21 +7827,21 @@
***VECTORS
- NMDS1 NMDS2 r2 Pr(>r)
-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 .
+ NMDS1 NMDS2 r2 Pr(>r)
+N -0.05719 -0.99836 0.2537 0.046 *
+P 0.61959 0.78492 0.1938 0.103
+K 0.76629 0.64249 0.1809 0.143
+Ca 0.68506 0.72849 0.4119 0.008 **
+Mg 0.63240 0.77464 0.4271 0.004 **
+S 0.19123 0.98155 0.1752 0.140
+Al -0.87169 0.49006 0.5269 0.001 ***
+Fe -0.93613 0.35164 0.4451 0.002 **
+Mn 0.79873 -0.60169 0.5230 0.001 ***
+Zn 0.61750 0.78657 0.1879 0.125
+Mo -0.90304 0.42955 0.0609 0.537
+Baresoil 0.92503 -0.37988 0.2508 0.039 *
+Humdepth 0.93291 -0.36011 0.5200 0.002 **
+pH -0.64809 0.76156 0.2308 0.060 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
P values based on 999 permutations.
@@ -7853,9 +7875,9 @@
data = dune.env)
Inertia Proportion Rank
-Total 84.1237 1.0000
-Constrained 63.2062 0.7513 12
-Unconstrained 20.9175 0.2487 7
+Total 84.1200 1.0000
+Constrained 63.2100 0.7513 12
+Unconstrained 20.9200 0.2487 7
Inertia is variance
Some constraints were aliased because they were collinear (redundant)
@@ -7916,9 +7938,9 @@
Call: rda(formula = dune ~ Management + Moisture, data = dune.env)
Inertia Proportion Rank
-Total 84.1237 1.0000
-Constrained 46.4249 0.5519 6
-Unconstrained 37.6988 0.4481 13
+Total 84.1200 1.0000
+Constrained 46.4200 0.5519 6
+Unconstrained 37.7000 0.4481 13
Inertia is variance
Eigenvalues for constrained axes:
@@ -7963,14 +7985,15 @@
> ## add fitted surface of diversity to the model
> ordisurf(mod, diversity(dune), add = TRUE)
Loading required package: mgcv
-This is mgcv 1.7-24. For overview type 'help("mgcv-package")'.
+Loading required package: nlme
+This is mgcv 1.7-26. For overview type 'help("mgcv-package")'.
Family: gaussian
Link function: identity
Formula:
y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
-<environment: 0xa7a7390>
+<environment: 0xaa07b80>
Estimated degrees of freedom:
1.28 total = 2.28
@@ -8014,7 +8037,7 @@
>
> cleanEx()
-detaching ‘package:mgcv’
+detaching ‘package:mgcv’, ‘package:nlme’
> nameEx("vegandocs")
> ### * vegandocs
@@ -8523,7 +8546,7 @@
> ###
> options(digits = 7L)
> base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
-Time elapsed: 27.384 0.108 27.597 0 0
+Time elapsed: 25.872 0.092 25.999 0 0
> grDevices::dev.off()
null device
1
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