[Vegan-commits] r2884 - in pkg/vegan: man tests/Examples
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Wed Sep 17 13:29:20 CEST 2014
Author: jarioksa
Date: 2014-09-17 13:29:19 +0200 (Wed, 17 Sep 2014)
New Revision: 2884
Modified:
pkg/vegan/man/goodness.cca.Rd
pkg/vegan/tests/Examples/vegan-Ex.Rout.save
Log:
Merge branch 'master' into r-forge-svn-local
Modified: pkg/vegan/man/goodness.cca.Rd
===================================================================
--- pkg/vegan/man/goodness.cca.Rd 2014-09-16 14:13:14 UTC (rev 2883)
+++ pkg/vegan/man/goodness.cca.Rd 2014-09-17 11:29:19 UTC (rev 2884)
@@ -32,7 +32,7 @@
\arguments{
\item{object}{A result object from \code{\link{cca}},
- \code{\link{rda}}, \code{\link{capscale}} or \code{\link{decorana}}. }
+ \code{\link{rda}} or \code{\link{capscale}}. }
\item{display}{Display \code{"species"} or \code{"sites"}. }
\item{choices}{Axes shown. Default is to show all axes of the \code{"model"}. }
\item{model}{Show constrained (\code{"CCA"}) or unconstrained
@@ -52,7 +52,7 @@
Function \code{goodness} gives the diagnostic statistics for species
or sites. The alternative statistics are the cumulative proportion of
inertia accounted for by the axes, and the residual distance left
- unaccounted for. The conditional (``partialled out'') constraints are
+ unaccounted for. The conditional (\dQuote{partialled out}) constraints are
always regarded as explained and included in the statistics.
Function \code{inertcomp} decomposes the inertia into partial,
@@ -124,7 +124,7 @@
}
\seealso{\code{\link{cca}}, \code{\link{rda}}, \code{\link{capscale}},
- \code{\link{decorana}}, \code{\link[car]{vif}}. }
+ \code{\link[car]{vif}}. }
\examples{
data(dune)
data(dune.env)
Modified: pkg/vegan/tests/Examples/vegan-Ex.Rout.save
===================================================================
--- pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2014-09-16 14:13:14 UTC (rev 2883)
+++ pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2014-09-17 11:29:19 UTC (rev 2884)
@@ -1,5 +1,5 @@
-R Under development (unstable) (2014-09-03 r66516) -- "Unsuffered Consequences"
+R Under development (unstable) (2014-09-15 r66611) -- "Unsuffered Consequences"
Copyright (C) 2014 The R Foundation for Statistical Computing
Platform: x86_64-unknown-linux-gnu (64-bit)
@@ -23,7 +23,7 @@
> library('vegan')
Loading required package: permute
Loading required package: lattice
-This is vegan 2.1-41
+This is vegan 2.1-43
>
> base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
> cleanEx()
@@ -83,8 +83,7 @@
Pillai's trace: 4.573009
Significance of Pillai's trace:
-from F-distribution: 0.0032737
-
+from F-distribution: 0.0032737
CanAxis1 CanAxis2 CanAxis3 CanAxis4 CanAxis5 CanAxis6
Canonical Correlations 0.92810 0.82431 0.81209 0.74981 0.70795 0.65950
CanAxis7 CanAxis8 CanAxis9 CanAxis10 CanAxis11
@@ -104,13 +103,13 @@
> # Example using random numbers. No significant relationship is expected
> mat1 <- matrix(rnorm(60),20,3)
> mat2 <- matrix(rnorm(100),20,5)
-> out2 = CCorA(mat1, mat2, nperm=99)
+> out2 = CCorA(mat1, mat2, permutations=99)
> out2
Canonical Correlation Analysis
Call:
-CCorA(Y = mat1, X = mat2, nperm = 99)
+CCorA(Y = mat1, X = mat2, permutations = 99)
Y X
Matrix Ranks 3 5
@@ -118,9 +117,11 @@
Pillai's trace: 0.6455578
Significance of Pillai's trace:
-based on 99 permutations: 0.69
-from F-distribution: 0.70352
-
+from F-distribution: 0.70352
+based on permutations: 0.69
+Permutation: free
+Number of permutations: 99
+
CanAxis1 CanAxis2 CanAxis3
Canonical Correlations 0.69691 0.38140 0.12
@@ -159,7 +160,7 @@
Formula:
y ~ poly(x1, 1) + poly(x2, 1)
-<environment: 0x5c946e8>
+<environment: 0x4d09c38>
Total model degrees of freedom 3
REML score: -3.185099
@@ -624,6 +625,9 @@
Call:
adonis(formula = dune ~ Management * A1, data = dune.env, permutations = 99)
+Permutation: free
+Number of permutations: 99
+
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
@@ -680,6 +684,10 @@
Call:
adonis(formula = Y ~ NO3, data = dat, permutations = 999, strata = dat$field)
+Blocks: strata
+Permutation: free
+Number of permutations: 999
+
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
@@ -695,6 +703,9 @@
Call:
adonis(formula = Y ~ NO3, data = dat, permutations = 999)
+Permutation: free
+Number of permutations: 999
+
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
@@ -733,7 +744,8 @@
ANOSIM statistic R: 0.2579
Significance: 0.017
-Based on 999 permutations
+Permutation: free
+Number of permutations: 999
Upper quantiles of permutations (null model):
90% 95% 97.5% 99%
@@ -1081,23 +1093,9 @@
> permutest(mod, pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
+Permutation: free
+Number of permutations: 999
-Permutation Design:
-
-Blocks:
- Defined by: none
-
-Plots:
- Defined by: none
-
-Within Plots:
- Permutation type: free
-
-Permutation details:
- Number of permutations requested: 999
- Max. number of permutations allowed: 9999
- Evaluate all permutations?: No. Activation limit: 99
-
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.07931 0.079306 4.6156 1000 0.05 *
@@ -1252,23 +1250,9 @@
> permutest(mod2, control = how(nperm = 100))
Permutation test for homogeneity of multivariate dispersions
+Permutation: free
+Number of permutations: 999
-Permutation Design:
-
-Blocks:
- Defined by: none
-
-Plots:
- Defined by: none
-
-Within Plots:
- Permutation type: free
-
-Permutation details:
- Number of permutations requested: 999
- Max. number of permutations allowed: 9999
- Evaluate all permutations?: No. Activation limit: 99
-
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.039979 0.039979 2.4237 1000 0.115
@@ -1309,23 +1293,9 @@
> permutest(mod3, control = how(nperm = 100))
Permutation test for homogeneity of multivariate dispersions
+Permutation: free
+Number of permutations: 999
-Permutation Design:
-
-Blocks:
- Defined by: none
-
-Plots:
- Defined by: none
-
-Within Plots:
- Permutation type: free
-
-Permutation details:
- Number of permutations requested: 999
- Max. number of permutations allowed: 9999
- Evaluate all permutations?: No. Activation limit: 99
-
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.033468 0.033468 3.1749 1000 0.084 .
@@ -2751,23 +2721,24 @@
***VECTORS
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
+N -0.05719 -0.99836 0.2537 0.039 *
+P 0.61959 0.78492 0.1938 0.129
+K 0.76629 0.64249 0.1809 0.138
+Ca 0.68506 0.72849 0.4119 0.005 **
+Mg 0.63240 0.77464 0.4271 0.005 **
+S 0.19123 0.98155 0.1752 0.133
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 .
+Fe -0.93613 0.35164 0.4451 0.001 ***
+Mn 0.79873 -0.60169 0.5230 0.002 **
+Zn 0.61750 0.78657 0.1879 0.122
+Mo -0.90304 0.42955 0.0609 0.524
+Baresoil 0.92503 -0.37988 0.2508 0.043 *
+Humdepth 0.93291 -0.36011 0.5200 0.001 ***
+pH -0.64809 0.76156 0.2308 0.064 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-P values based on 999 permutations.
+Permutation: free
+Number of permutations: 999
> scores(fit, "vectors")
@@ -3316,7 +3287,7 @@
>
> ### Name: isomap
> ### Title: Isometric Feature Mapping Ordination
-> ### Aliases: isomap isomapdist plot.isomap summary.isomap rgl.isomap
+> ### Aliases: isomap isomapdist plot.isomap summary.isomap
> ### Keywords: multivariate
>
> ### ** Examples
@@ -3348,10 +3319,6 @@
> pl <- plot(isomap(dis, epsilon=0.45), main="isomap epsilon=0.45")
> lines(tr, pl, col="red")
> par(op)
-> ## The following command requires user interaction
-> ## Not run:
-> ##D rgl.isomap(ord, size=4, color="hotpink")
-> ## End(Not run)
>
>
>
@@ -3542,9 +3509,9 @@
Upper quantiles of permutations (null model):
90% 95% 97.5% 99%
0.107 0.145 0.169 0.200
+Permutation: free
+Number of permutations: 999
-Based on 999 permutations
-
> mantel(veg.dist, env.dist, method="spear")
Mantel statistic based on Spearman's rank correlation rho
@@ -3558,9 +3525,9 @@
Upper quantiles of permutations (null model):
90% 95% 97.5% 99%
0.108 0.141 0.169 0.196
+Permutation: free
+Number of permutations: 999
-Based on 999 permutations
-
>
>
>
@@ -3950,7 +3917,8 @@
Based on observed delta 11.15 and expected delta 12.74
Significance of delta: 0.002
-Based on 999 permutations
+Permutation: free
+Number of permutations: 999
>
> # Save and change plotting parameters
@@ -4029,6 +3997,7 @@
> ## Canonical correspondence analysis (cca):
> Example.cca <- cca(X, Y)
> Example.cca <- mso(Example.cca, tmat)
+Set of permutations < 'minperm'. Generating entire set.
> msoplot(Example.cca)
> Example.cca$vario
H Dist n All Sum CA CCA se
@@ -4037,6 +4006,7 @@
>
> ## Correspondence analysis (ca):
> Example.ca <- mso(cca(X), tmat)
+Set of permutations < 'minperm'. Generating entire set.
> msoplot(Example.ca)
>
> ## Unconstrained ordination with test for autocorrelation
@@ -4046,11 +4016,11 @@
> data(mite.xy)
>
> mite.cca <- cca(log(mite + 1))
-> mite.cca <- mso(mite.cca, mite.xy, grain = 1, permutations = 100)
+> mite.cca <- mso(mite.cca, mite.xy, grain = 1, permutations = 99)
> msoplot(mite.cca)
> mite.cca
Call: mso(object.cca = mite.cca, object.xy = mite.xy, grain = 1,
-permutations = 100)
+permutations = 99)
Inertia Rank
Total 1.164
@@ -4065,23 +4035,27 @@
mso variogram:
H Dist n All CA CA.signif
-0 0 0.3555 63 0.6250 0.6250 0.00
-1 1 1.0659 393 0.7556 0.7556 0.00
-2 2 2.0089 534 0.8931 0.8931 0.00
-3 3 2.9786 417 1.0988 1.0988 0.02
-4 4 3.9817 322 1.3321 1.3321 0.00
-5 5 5.0204 245 1.5109 1.5109 0.00
-10 10 6.8069 441 1.7466 1.7466 0.00
+0 0 0.3555 63 0.6250 0.6250 0.01
+1 1 1.0659 393 0.7556 0.7556 0.01
+2 2 2.0089 534 0.8931 0.8931 0.01
+3 3 2.9786 417 1.0988 1.0988 0.03
+4 4 3.9817 322 1.3321 1.3321 0.01
+5 5 5.0204 245 1.5109 1.5109 0.01
+10 10 6.8069 441 1.7466 1.7466 0.01
+
+Permutation: free
+Number of permutations: 99
+
>
> ## Constrained ordination with test for residual autocorrelation
> ## and scale-invariance of species-environment relationships
> mite.cca <- cca(log(mite + 1) ~ SubsDens + WatrCont + Substrate + Shrub + Topo, mite.env)
-> mite.cca <- mso(mite.cca, mite.xy, permutations = 100)
+> mite.cca <- mso(mite.cca, mite.xy, permutations = 99)
> msoplot(mite.cca)
Error variance of regression model underestimated by 0.4 percent
> mite.cca
Call: mso(object.cca = mite.cca, object.xy = mite.xy, permutations =
-100)
+99)
Inertia Proportion Rank
Total 1.1638 1.0000
@@ -4103,13 +4077,17 @@
mso variogram:
H Dist n All Sum CA CCA se CA.signif
-0 0 0.3555 63 0.6250 0.7479 0.5512 0.1967 0.03506 0.00
-1 1 1.0659 393 0.7556 0.8820 0.6339 0.2482 0.01573 0.16
+0 0 0.3555 63 0.6250 0.7479 0.5512 0.1967 0.03506 0.01
+1 1 1.0659 393 0.7556 0.8820 0.6339 0.2482 0.01573 0.17
2 2 2.0089 534 0.8931 0.9573 0.6473 0.3100 0.01487 0.71
-3 3 2.9786 417 1.0988 1.1010 0.6403 0.4607 0.01858 0.44
+3 3 2.9786 417 1.0988 1.1010 0.6403 0.4607 0.01858 0.46
4 4 3.9817 322 1.3321 1.2548 0.6521 0.6027 0.02439 0.99
5 5 5.0204 245 1.5109 1.4564 0.6636 0.7928 0.02801 0.42
-10 10 6.8069 441 1.7466 1.6266 0.6914 0.9351 0.02052 0.20
+10 10 6.8069 441 1.7466 1.6266 0.6914 0.9351 0.02052 0.21
+
+Permutation: free
+Number of permutations: 99
+
>
>
>
@@ -4643,58 +4621,6 @@
>
>
> cleanEx()
-> nameEx("ordiplot3d")
-> ### * ordiplot3d
->
-> flush(stderr()); flush(stdout())
->
-> ### Name: ordiplot3d
-> ### Title: Three-Dimensional and Dynamic Ordination Graphics
-> ### Aliases: ordiplot3d ordirgl orglpoints orgltext orglsegments orglspider
-> ### Keywords: hplot dynamic
->
-> ### ** Examples
->
-> ## Examples are not run, because they need non-standard packages
-> ## 'scatterplot3d' and 'rgl' (and the latter needs user interaction).
-> #####
-> ### Default 'ordiplot3d'
-> ## Not run:
-> ##D data(dune)
-> ##D data(dune.env)
-> ##D ord <- cca(dune ~ A1 + Moisture, dune.env)
-> ##D ordiplot3d(ord)
-> ##D ### A boxed 'pin' version
-> ##D ordiplot3d(ord, type = "h")
-> ##D ### More user control
-> ##D pl <- ordiplot3d(ord, scaling = 3, angle=15, type="n")
-> ##D points(pl, "points", pch=16, col="red", cex = 0.7)
-> ##D ### identify(pl, "arrows", col="blue") would put labels in better positions
-> ##D text(pl, "arrows", col="blue", pos=3)
-> ##D text(pl, "centroids", col="blue", pos=1, cex = 1)
-> ##D ### Add species using xyz.convert function returned by ordiplot3d
-> ##D sp <- scores(ord, choices=1:3, display="species", scaling=3)
-> ##D text(pl$xyz.convert(sp), rownames(sp), cex=0.7, xpd=TRUE)
-> ##D ### Two ways of adding fitted variables to ordination plots
-> ##D ord <- cca(dune)
-> ##D ef <- envfit(ord ~ Moisture + A1, dune.env, choices = 1:3)
-> ##D ### 1. use argument 'envfit'
-> ##D ordiplot3d(ord, envfit = ef)
-> ##D ### 2. use returned envfit.convert function for better user control
-> ##D pl3 <- ordiplot3d(ord)
-> ##D plot(pl3$envfit.convert(ef), at = pl3$origin)
-> ##D ### envfit.convert() also handles different 'choices' of axes
-> ##D pl3 <- ordiplot3d(ord, choices = c(1,3,2))
-> ##D plot(pl3$envfit.convert(ef), at = pl3$origin)
-> ##D ### ordirgl
-> ##D ordirgl(ord, size=2)
-> ##D ordirgl(ord, display = "species", type = "t")
-> ##D rgl.quit()
-> ## End(Not run)
->
->
->
-> cleanEx()
> nameEx("ordipointlabel")
> ### * ordipointlabel
>
@@ -5033,7 +4959,7 @@
Formula:
y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
-<environment: 0x77c4478>
+<environment: 0xb279580>
Estimated degrees of freedom:
5.63 total = 6.63
@@ -5050,7 +4976,7 @@
Formula:
y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
-<environment: 0x8afa6c0>
+<environment: 0xa7c6dc0>
Estimated degrees of freedom:
6.45 total = 7.45
@@ -5081,7 +5007,7 @@
Formula:
y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
-<environment: 0x97119c8>
+<environment: 0xab2f7e8>
Estimated degrees of freedom:
5.63 total = 6.63
@@ -5096,7 +5022,7 @@
Formula:
y ~ s(x1, x2, k = 10, bs = "ts", fx = FALSE)
-<environment: 0x7a3cfd0>
+<environment: 0xa4e68e0>
Estimated degrees of freedom:
4.43 total = 5.43
@@ -5125,7 +5051,7 @@
Formula:
y ~ s(x1, x2, k = 10, bs = "ds", fx = FALSE)
-<environment: 0x95af318>
+<environment: 0xaa71098>
Estimated degrees of freedom:
5.63 total = 6.63
@@ -5141,7 +5067,7 @@
Formula:
y ~ s(x1, x2, k = 4, bs = "tp", fx = TRUE)
-<environment: 0x7bccf50>
+<environment: 0xb2b2870>
Estimated degrees of freedom:
3 total = 4
@@ -5158,7 +5084,7 @@
Formula:
y ~ te(x1, x2, k = c(4, 4), bs = c("cr", "cr"), fx = c(FALSE,
FALSE))
-<environment: 0x8a73740>
+<environment: 0xb11bd50>
Estimated degrees of freedom:
2.99 total = 3.99
@@ -5177,7 +5103,7 @@
Formula:
y ~ te(x1, x2, k = c(3, 4), bs = c("cs", "cs"), fx = c(TRUE,
TRUE))
-<environment: 0x86bea20>
+<environment: 0xaf954b0>
Estimated degrees of freedom:
11 total = 12
@@ -5318,7 +5244,7 @@
Formula:
y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
-<environment: 0x9eca308>
+<environment: 0xa652028>
Estimated degrees of freedom:
8.71 total = 9.71
@@ -5331,7 +5257,7 @@
Formula:
y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
-<environment: 0x8adb928>
+<environment: 0x9701348>
Estimated degrees of freedom:
7.18 total = 8.18
@@ -5344,7 +5270,7 @@
Formula:
y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
-<environment: 0x86d11e0>
+<environment: 0x9b23ce8>
Estimated degrees of freedom:
8.32 total = 9.32
@@ -5636,23 +5562,9 @@
> permutest(mod, permutations = 99, pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
+Permutation: free
+Number of permutations: 99
-Permutation Design:
-
-Blocks:
- Defined by: none
-
-Plots:
- Defined by: none
-
-Within Plots:
- Permutation type: free
-
-Permutation details:
- Number of permutations requested: 99
- Max. number of permutations allowed: 9999
- Evaluate all permutations?: No. Activation limit: 99
-
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.07931 0.079306 4.6156 100 0.04 *
@@ -6308,7 +6220,6 @@
> ### Name: renyi
> ### Title: Renyi and Hill Diversities and Corresponding Accumulation Curves
> ### Aliases: renyi plot.renyi renyiaccum plot.renyiaccum persp.renyiaccum
-> ### rgl.renyiaccum
> ### Keywords: univar
>
> ### ** Examples
@@ -6702,6 +6613,8 @@
Vicilath 0.002399 0.005461 0.4393 0.0 0.1667 1.00000
Chenalbu 0.000000 0.000000 NaN 0.0 0.0000 1.00000
Cirsarve 0.000000 0.000000 NaN 0.0 0.0000 1.00000
+Permutation: free
+Number of permutations: 0
>
>
>
@@ -7803,23 +7716,24 @@
***VECTORS
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
+N -0.05719 -0.99836 0.2537 0.039 *
+P 0.61959 0.78492 0.1938 0.129
+K 0.76629 0.64249 0.1809 0.138
+Ca 0.68506 0.72849 0.4119 0.005 **
+Mg 0.63240 0.77464 0.4271 0.005 **
+S 0.19123 0.98155 0.1752 0.133
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 .
+Fe -0.93613 0.35164 0.4451 0.001 ***
+Mn 0.79873 -0.60169 0.5230 0.002 **
+Zn 0.61750 0.78657 0.1879 0.122
+Mo -0.90304 0.42955 0.0609 0.524
+Baresoil 0.92503 -0.37988 0.2508 0.043 *
+Humdepth 0.93291 -0.36011 0.5200 0.001 ***
+pH -0.64809 0.76156 0.2308 0.064 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-P values based on 999 permutations.
+Permutation: free
+Number of permutations: 999
> plot(ef, p.max = 0.05)
@@ -7875,9 +7789,9 @@
Df AIC F Pr(>F)
+ Management 3 87.082 2.8400 0.005 **
+ Moisture 3 87.707 2.5883 0.005 **
-+ Manure 4 89.232 1.9539 0.005 **
-+ A1 1 89.591 1.9217 0.050 *
-+ Use 2 91.032 1.1741 0.265
++ Manure 4 89.232 1.9539 0.015 *
++ A1 1 89.591 1.9217 0.045 *
++ Use 2 91.032 1.1741 0.295
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
@@ -7889,25 +7803,25 @@
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Df AIC F Pr(>F)
-+ Moisture 3 85.567 1.9764 0.005 **
-+ Manure 3 87.517 1.3902 0.120
-+ A1 1 87.424 1.2965 0.210
-+ Use 2 88.284 1.0510 0.390
++ Moisture 3 85.567 1.9764 0.010 **
++ Manure 3 87.517 1.3902 0.045 *
++ A1 1 87.424 1.2965 0.245
++ Use 2 88.284 1.0510 0.400
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Step: dune ~ Management + Moisture
Df AIC F Pr(>F)
-- Moisture 3 87.082 1.9764 0.010 **
+- Moisture 3 87.082 1.9764 0.015 *
- Management 3 87.707 2.1769 0.005 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Df AIC F Pr(>F)
-+ Manure 3 85.762 1.1225 0.345
-+ A1 1 86.220 0.8359 0.640
-+ Use 2 86.842 0.8027 0.700
++ Manure 3 85.762 1.1225 0.255
++ A1 1 86.220 0.8359 0.625
++ Use 2 86.842 0.8027 0.780
> mod
Call: rda(formula = dune ~ Management + Moisture, data = dune.env)
@@ -7949,8 +7863,8 @@
Model: rda(formula = dune ~ Management + Moisture, data = dune.env)
Df Variance F Pr(>F)
-Management 3 18.938 2.1769 0.005 **
-Moisture 3 17.194 1.9764 0.006 **
+Management 3 18.938 2.1769 0.003 **
+Moisture 3 17.194 1.9764 0.005 **
Residual 13 37.699
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
@@ -7969,7 +7883,7 @@
Formula:
y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
-<environment: 0x974c070>
+<environment: 0x9990b88>
Estimated degrees of freedom:
1.28 total = 2.28
@@ -7982,14 +7896,17 @@
Call:
adonis(formula = dune ~ ., data = dune.env)
+Permutation: free
+Number of permutations: 999
+
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
A1 1 0.7230 0.72295 5.2038 0.16817 0.001 ***
-Moisture 3 1.1871 0.39569 2.8482 0.27613 0.004 **
-Management 3 0.9036 0.30121 2.1681 0.21019 0.026 *
-Use 2 0.0921 0.04606 0.3315 0.02143 0.983
-Manure 3 0.4208 0.14026 1.0096 0.09787 0.441
+Moisture 3 1.1871 0.39569 2.8482 0.27613 0.006 **
+Management 3 0.9036 0.30121 2.1681 0.21019 0.023 *
+Use 2 0.0921 0.04606 0.3315 0.02143 0.982
+Manure 3 0.4208 0.14026 1.0096 0.09787 0.481
Residuals 7 0.9725 0.13893 0.22621
Total 19 4.2990 1.00000
---
@@ -7999,11 +7916,14 @@
Call:
adonis(formula = dune ~ Management + Moisture, data = dune.env)
+Permutation: free
+Number of permutations: 999
+
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
-Management 3 1.4686 0.48953 3.7907 0.34161 0.002 **
-Moisture 3 1.1516 0.38387 2.9726 0.26788 0.001 ***
+Management 3 1.4686 0.48953 3.7907 0.34161 0.001 ***
+Moisture 3 1.1516 0.38387 2.9726 0.26788 0.002 **
Residuals 13 1.6788 0.12914 0.39051
Total 19 4.2990 1.00000
---
@@ -8519,7 +8439,7 @@
> ###
> options(digits = 7L)
> base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
-Time elapsed: 35.834 7.734 41.19 0 0.001
+Time elapsed: 35.902 8.181 41.58 0 0
> grDevices::dev.off()
null device
1
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