[Vegan-commits] r2352 - pkg/vegan/tests/Examples
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Wed Jan 9 09:50:25 CET 2013
Author: jarioksa
Date: 2013-01-09 09:50:25 +0100 (Wed, 09 Jan 2013)
New Revision: 2352
Modified:
pkg/vegan/tests/Examples/vegan-Ex.Rout.save
Log:
update for r2349
also includes changes due to amount of printed white space
and no. of significant digits in printout in current R-devel
Modified: pkg/vegan/tests/Examples/vegan-Ex.Rout.save
===================================================================
--- pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2013-01-09 07:49:29 UTC (rev 2351)
+++ pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2013-01-09 08:50:25 UTC (rev 2352)
@@ -1,8 +1,8 @@
-R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows"
-Copyright (C) 2012 The R Foundation for Statistical Computing
+R Under development (unstable) (2013-01-08 r61589) -- "Unsuffered Consequences"
+Copyright (C) 2013 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
-Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)
+Platform: x86_64-unknown-linux-gnu (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 2.1-22
+This is vegan 2.1-23
>
> assign(".oldSearch", search(), pos = 'CheckExEnv')
> cleanEx()
@@ -154,14 +154,14 @@
> plot(ef)
> ordisurf(mod ~ pH, varechem, knots = 1, add = TRUE)
Loading required package: mgcv
-This is mgcv 1.7-18. For overview type 'help("mgcv-package")'.
+This is mgcv 1.7-22. For overview type 'help("mgcv-package")'.
Family: gaussian
Link function: identity
Formula:
y ~ poly(x1, 1) + poly(x2, 1)
-<environment: 0x102511068>
+<environment: 0x12bef70>
Total model degrees of freedom 3
GCV score: 0.04278782
@@ -227,7 +227,7 @@
hump at max 7.8160 9.0487 0.01191 *
Combined 0.03338 *
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> plot(mod)
> par(op)
> ## Confidence Limits
@@ -305,14 +305,14 @@
> marr <- nls(S ~ SSarrhenius(sipoo.area, k, z))
> marr
Nonlinear regression model
- model: S ~ SSarrhenius(sipoo.area, k, z)
- data: parent.frame()
+ model: S ~ SSarrhenius(sipoo.area, k, z)
+ data: parent.frame()
k z
3.4062 0.4364
residual sum-of-squares: 78.1
Number of iterations to convergence: 5
-Achieved convergence tolerance: 1.056e-06
+Achieved convergence tolerance: 1.056e-06
> ## confidence limits from profile likelihood
> confint(marr)
Waiting for profiling to be done...
@@ -348,14 +348,14 @@
> mlom <- nls(S ~ SSlomolino(sipoo.area, Smax, A50, Hill))
> mlom
Nonlinear regression model
- model: S ~ SSlomolino(sipoo.area, Smax, A50, Hill)
- data: parent.frame()
+ model: S ~ SSlomolino(sipoo.area, Smax, A50, Hill)
+ data: parent.frame()
Smax A50 Hill
53.493 94.697 2.018
residual sum-of-squares: 55.37
Number of iterations to convergence: 6
-Achieved convergence tolerance: 9.715e-07
+Achieved convergence tolerance: 9.715e-07
> lines(xtmp, predict(mlom, newdata=data.frame(sipoo.area=xtmp)),
+ lwd=2, col = 4)
> ## One canned model of standard R:
@@ -402,7 +402,7 @@
+ 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
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Step: AIC=86.61
dune ~ Moisture
@@ -415,7 +415,7 @@
+ 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
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Call: cca(formula = dune ~ Moisture, data = dune.env)
Inertia Proportion Rank
@@ -453,7 +453,7 @@
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
+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)
@@ -463,7 +463,7 @@
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
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> m0 <- update(m0, . ~ . + Moisture)
> ## -- included variables still significant?
> drop1(m0, test="perm")
@@ -472,7 +472,7 @@
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
+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
@@ -536,7 +536,7 @@
gamma 35.000 0.00 35.000 35.000 35.000 35.000 1.00
beta.1 19.886 38.43 12.656 12.392 12.700 12.968 0.05 *
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> adipart(mite ~ ., levsm, index="richness", nsimul=19)
adipart object
@@ -556,7 +556,7 @@
beta.2 3.250 13.1373 0.18421 0.00000 0.00000 0.6375 0.05 *
beta.3 2.000 0.0000 0.00000 0.00000 0.00000 0.0000 0.05 *
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> ## Hierarchical null model testing
> ## diversity analysis (similar to adipart)
> hiersimu(mite, FUN=diversity, relative=TRUE, nsimul=19)
@@ -572,7 +572,7 @@
level_1 0.76064 -71.195 0.93904 0.93487 0.93856 0.9444 0.05 *
leve_2 1.00000 0.000 1.00000 1.00000 1.00000 1.0000 1.00
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> hiersimu(mite ~., levsm, FUN=diversity, relative=TRUE, nsimul=19)
hiersimu object
@@ -589,7 +589,7 @@
l3 0.92791 -417.338 0.99940 0.99904 0.99943 0.9996 0.05 *
l4 1.00000 0.000 1.00000 1.00000 1.00000 1.0000 1.00
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> ## Hierarchical testing with the Morisita index
> morfun <- function(x) dispindmorisita(x)$imst
> hiersimu(mite ~., levsm, morfun, drop.highest=TRUE, nsimul=19)
@@ -607,7 +607,7 @@
l2 0.60234 14.3854 0.153047 0.096700 0.150434 0.1969 0.05 *
l3 0.67509 20.3162 -0.182473 -0.234793 -0.195937 -0.0988 0.05 *
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
>
>
@@ -642,7 +642,7 @@
Residuals 12 1.8004 0.15003 0.41878
Total 19 4.2990 1.00000
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
>
> ### Example of use with strata, for nested (e.g., block) designs.
@@ -668,6 +668,10 @@
> 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)
@@ -675,7 +679,7 @@
Run 1 stress 0.1560544
Run 2 stress 0.08556586
... New best solution
-... procrustes: rmse 1.094373e-06 max resid 1.88838e-06
+... procrustes: rmse 1.094382e-06 max resid 1.88838e-06
*** Solution reached
> plot(mod)
> ### Hulls show treatment
@@ -697,7 +701,7 @@
Residuals 10 0.138667 0.013867 0.71286
Total 11 0.194524 1.00000
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> ### Incorrect (no strata)
> adonis(Y ~ NO3, data=dat, perm=999)
@@ -712,7 +716,7 @@
Residuals 10 0.138667 0.013867 0.71286
Total 11 0.194524 1.00000
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
>
>
@@ -796,7 +800,7 @@
Model 3 0.6441 2.9840 199 0.005 **
Residual 20 1.4391
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> ## Test for axes
> anova(vare.cca, by="axis", perm.max=500)
Model: cca(formula = varespec ~ Al + P + K, data = varechem)
@@ -806,7 +810,7 @@
CCA3 1 0.1126 1.5651 399 0.100 .
Residual 20 1.4391
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> ## Sequential test for terms
> anova(vare.cca, by="terms", permu=200)
Permutation test for cca under reduced model
@@ -819,7 +823,7 @@
K 1 0.1561 2.1688 199 0.030 *
Residual 20 1.4391
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> ## Marginal or Type III effects
> anova(vare.cca, by="margin")
Permutation test for cca under reduced model
@@ -832,7 +836,7 @@
K 1 0.1561 2.1688 599 0.02833 *
Residual 20 1.4391
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> ## Marginal test knows 'scope'
> anova(vare.cca, by = "m", scope="P")
Permutation test for cca under reduced model
@@ -843,7 +847,7 @@
P 1 0.1681 2.3362 199 0.015 *
Residual 20 1.4391
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
>
>
@@ -1137,7 +1141,7 @@
Groups 1 0.07931 0.079306 4.6156 0.04295 *
Residuals 22 0.37801 0.017182
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> ## Permutation test for F
> permutest(mod, pairwise = TRUE)
@@ -1158,7 +1162,7 @@
Groups 1 0.07931 0.079306 4.6156 999 0.05 *
Residuals 22 0.37801 0.017182
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
@@ -1286,7 +1290,7 @@
Groups 1 0.033468 0.033468 3.1749 100 0.06931 .
Residuals 18 0.189749 0.010542
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> anova(mod3)
Analysis of Variance Table
@@ -1295,7 +1299,7 @@
Groups 1 0.033468 0.033468 3.1749 0.09166 .
Residuals 18 0.189749 0.010542
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> plot(mod3)
> boxplot(mod3)
> plot(TukeyHSD(mod3))
@@ -1559,7 +1563,7 @@
Model 3 0.99717 2.2324 199 0.005 **
Residual 19 2.82904
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> ## Avoid negative eigenvalues with additive constant
> capscale(varespec ~ N + P + K + Condition(Al), varechem,
+ dist="bray", add =TRUE)
@@ -2280,7 +2284,7 @@
Pearson's Chi-squared test
-data: dune
+data: dune
X-squared = 1448.956, df = 551, p-value < 2.2e-16
> deviance(cca(dune))
@@ -2635,7 +2639,7 @@
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
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
P values based on 999 permutations.
@@ -3497,7 +3501,7 @@
D.cl.12 8.526186 66.000000 NA NA NA
D.cl.13 9.254550 32.000000 NA NA NA
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> # or: print(mite.correlog)
> # or: print.mantel.correlog(mite.correlog)
> plot(mite.correlog)
@@ -3536,7 +3540,7 @@
D.cl.12 8.526186 66.000000 -0.054242 0.04 0.24
D.cl.13 9.254550 32.000000 -0.066677 0.02 0.26
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> plot(mite.correlog2)
>
> # NOTE: 'nperm' argument usually needs to be larger than 49.
@@ -3627,7 +3631,7 @@
Run 0 stress 0.1067169
Run 1 stress 0.1067169
... New best solution
-... procrustes: rmse 1.234853e-05 max resid 2.993581e-05
+... procrustes: rmse 1.234853e-05 max resid 2.993582e-05
*** Solution reached
> sol
@@ -3831,7 +3835,7 @@
> plot(dune.ord <- metaMDS(dune), type="text", display="sites" )
Run 0 stress 0.1192678
Run 1 stress 0.119268
-... procrustes: rmse 8.18569e-05 max resid 0.0001982896
+... procrustes: rmse 8.185687e-05 max resid 0.0001982896
*** Solution reached
> ordihull(dune.ord, dune.env$Management)
>
@@ -4021,7 +4025,7 @@
nsimul = 19)
nullmodel method ‘r2dtable’ with 19 simulations
-options: index renyi, scales 1, global TRUE
+options: index renyi, scales 1, global FALSE
alternative hypothesis: simulated median is not equal to the statistic
statistic z mean 2.5% 50% 97.5% Pr(sim.)
@@ -4033,7 +4037,7 @@
beta.2 1.0710 30.431 1.00339 0.99912 1.00340 1.0059 0.05 *
beta.3 1.1794 460.550 1.00148 1.00083 1.00148 1.0021 0.05 *
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> multipart(mite ~ ., levsm, index="renyi", scales=1, nsimul=19)
multipart object
@@ -4041,7 +4045,7 @@
scales = 1, nsimul = 19)
nullmodel method ‘r2dtable’ with 19 simulations
-options: index renyi, scales 1, global TRUE
+options: index renyi, scales 1, global FALSE
alternative hypothesis: simulated median is not equal to the statistic
statistic z mean 2.5% 50% 97.5% Pr(sim.)
@@ -4053,7 +4057,7 @@
beta.2 1.0710 33.423 1.0041 1.0015 1.0035 1.0078 0.05 *
beta.3 1.1794 419.166 1.0015 1.0008 1.0016 1.0023 0.05 *
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> multipart(mite ~ ., levsm, index="renyi", scales=1, nsimul=19, relative=TRUE)
multipart object
@@ -4061,7 +4065,7 @@
scales = 1, relative = TRUE, nsimul = 19)
nullmodel method ‘r2dtable’ with 19 simulations
-options: index renyi, scales 1, global TRUE
+options: index renyi, scales 1, global FALSE
alternative hypothesis: simulated median is not equal to the statistic
statistic z mean 2.5% 50% 97.5% Pr(sim.)
@@ -4073,7 +4077,7 @@
beta.2 0.535514 35.966 0.501994 0.500294 0.502062 0.5035 0.05 *
beta.3 0.589695 404.814 0.500885 0.500583 0.500848 0.5013 0.05 *
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> multipart(mite ~ ., levsm, index="renyi", scales=1, nsimul=19, global=TRUE)
multipart object
@@ -4093,7 +4097,7 @@
beta.2 1.2603 102.695 1.00483 0.99985 1.00484 1.0077 0.05 *
beta.3 1.1794 378.335 1.00154 1.00104 1.00144 1.0025 0.05 *
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
>
>
@@ -4155,7 +4159,7 @@
statistic z mean 2.5% 50% 97.5% Pr(sim.)
C.score 2.2588 -28.92 9.2234 8.6935 9.2384 9.6053 0.01 **
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
>
>
@@ -4283,7 +4287,7 @@
statistic z mean 2.5% 50% 97.5% Pr(sim.)
statistic 2767 -17.768 8034.6 7529.9 8052.0 8518.5 0.01 **
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> ## sequential model, one-sided test, a vector statistic
> out <- oecosimu(sipoo, decorana, "swap", burnin=100, thin=10,
+ statistic="evals", alt = "less")
@@ -4316,7 +4320,7 @@
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
+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))
@@ -4342,7 +4346,7 @@
statistic z mean 2.5% 50% 97.5% Pr(sim.)
statistic 0.64565 14.66 0.46734 0.44069 0.46760 0.4903 0.01 **
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> ## Define your own null model as a 'commsim' function: shuffle cells
> ## in each row
@@ -4366,7 +4370,7 @@
statistic z mean 2.5% 50% 97.5% Pr(sim.)
statistic 0.64565 3.1832 0.63514 0.63016 0.63441 0.6419 0.03 *
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
>
>
@@ -4644,14 +4648,14 @@
+ A1 1 89.591 1.9217 199 0.035 *
+ Use 2 91.032 1.1741 99 0.310
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Step: dune ~ Management
Df AIC F N.Perm Pr(>F)
- Management 3 89.62 2.84 99 0.01 **
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Df AIC F N.Perm Pr(>F)
+ Moisture 3 85.567 1.9764 199 0.005 **
@@ -4659,7 +4663,7 @@
+ A1 1 87.424 1.2965 99 0.240
+ Use 2 88.284 1.0510 99 0.480
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Step: dune ~ Management + Moisture
@@ -4667,7 +4671,7 @@
- Moisture 3 87.082 1.9764 99 0.02 *
- Management 3 87.707 2.1769 99 0.01 **
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Df AIC F N.Perm Pr(>F)
+ Manure 3 85.762 1.1225 99 0.26
@@ -4720,7 +4724,7 @@
- Manure 3 85.567 1.1225 99 0.30
- Moisture 3 87.517 1.5788 99 0.03 *
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Step: dune ~ Moisture + Manure
@@ -4728,7 +4732,7 @@
- Manure 4 87.707 1.8598 99 0.02 *
- Moisture 3 89.232 2.3275 99 0.01 **
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Call: rda(formula = dune ~ Moisture + Manure, data = dune.env)
@@ -4778,7 +4782,7 @@
Df AIC F N.Perm Pr(>F)
+ WatrCont 1 -84.336 25.35 199 0.005 **
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Step: R2.adj= 0.2608453
Call: mite.hel ~ WatrCont
@@ -4795,7 +4799,7 @@
Df AIC F N.Perm Pr(>F)
+ Shrub 2 -88.034 3.836 199 0.005 **
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Step: R2.adj= 0.3177536
Call: mite.hel ~ WatrCont + Shrub
@@ -4812,7 +4816,7 @@
Df AIC F N.Perm Pr(>F)
+ Substrate 6 -87.768 1.8251 199 0.005 **
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Step: R2.adj= 0.3653551
Call: mite.hel ~ WatrCont + Shrub + Substrate
@@ -4829,7 +4833,7 @@
Df AIC F N.Perm Pr(>F)
+ Topo 1 -90.924 4.5095 199 0.005 **
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Step: R2.adj= 0.4004249
Call: mite.hel ~ WatrCont + Shrub + Substrate + Topo
@@ -4846,7 +4850,7 @@
Df AIC F N.Perm Pr(>F)
+ SubsDens 1 -94.489 4.7999 199 0.005 **
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Step: R2.adj= 0.4367038
Call: mite.hel ~ WatrCont + Shrub + Substrate + Topo + SubsDens
@@ -4869,7 +4873,7 @@
+ SubsDens 0.43670 1 -94.489 4.7999 199 0.005 **
<All variables> 0.43670
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> ## Example of ordiR2step with direction = "forward"
> ## Not run:
@@ -4899,17 +4903,17 @@
> vare.mds <- monoMDS(vare.dist)
> with(varechem, ordisurf(vare.mds, Baresoil, bubble = 5))
Loading required package: mgcv
-This is mgcv 1.7-18. For overview type 'help("mgcv-package")'.
+This is mgcv 1.7-22. For overview type 'help("mgcv-package")'.
Family: gaussian
Link function: identity
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x109ecd740>
+<environment: 0x8b27e80>
Estimated degrees of freedom:
-6.4512 total = 7.451249
+6.45 total = 7.45
GCV score: 144.0039
>
@@ -4922,10 +4926,10 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x10aa363f0>
+<environment: 0x905b218>
Estimated degrees of freedom:
-6.1234 total = 7.123433
+6.12 total = 7.12
GCV score: 139.9445
>
@@ -4934,13 +4938,13 @@
> ## Get fitted values
> calibrate(fit)
1 2 3 4 5 6 7
-22.0596535 6.0185659 3.6298559 4.1000950 8.9833600 5.9067472 8.6617389
+22.0596536 6.0185658 3.6298560 4.1000950 8.9833602 5.9067474 8.6617386
8 9 10 11 12 13 14
-11.0812152 0.6432691 35.2567124 10.4452454 7.2748478 5.5780162 24.6561685
+11.0812151 0.6432692 35.2567122 10.4452454 7.2748480 5.5780162 24.6561684
15 16 17 18 19 20 21
-18.8879906 29.7642964 5.6095920 9.5945524 3.2753633 2.6966143 10.7869351
+18.8879904 29.7642960 5.6095921 9.5945523 3.2753635 2.6966144 10.7869350
22 23 24
- 2.9902832 9.8082237 7.3406581
+ 2.9902833 9.8082238 7.3406584
>
> ## Plot method
> plot(fit, what = "contour")
@@ -5045,6 +5049,10 @@
> 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
>
>
>
@@ -5071,17 +5079,17 @@
> ## 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 1.7-18. For overview type 'help("mgcv-package")'.
+This is mgcv 1.7-22. For overview type 'help("mgcv-package")'.
Family: gaussian
Link function: identity
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x10b52c508>
+<environment: 0x8434c00>
Estimated degrees of freedom:
-8.9275 total = 9.927492
+8.93 total = 9.93
GCV score: 0.001054656
> ordisurf(mite.xy, scores(pcnm1, choi=2), bubble = 4, main = "PCNM 2")
@@ -5091,10 +5099,10 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x10b144508>
+<environment: 0x98d3c30>
Estimated degrees of freedom:
-7.7529 total = 8.75294
+7.75 total = 8.75
GCV score: 0.002284958
> ordisurf(mite.xy, scores(pcnm1, choi=3), bubble = 4, main = "PCNM 3")
@@ -5104,10 +5112,10 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x10aa31460>
+<environment: 0x9b013d0>
Estimated degrees of freedom:
-8.8962 total = 9.89616
+8.9 total = 9.9
GCV score: 0.002508871
> par(op)
@@ -5335,7 +5343,7 @@
Box-Pierce test
-data: mar$residuals
+data: mar$residuals
X-squared = 0.0011, df = 1, p-value = 0.9739
> ## Graphical diagnostics
@@ -5398,7 +5406,7 @@
Groups 1 0.07931 0.079306 4.6156 0.04295 *
Residuals 22 0.37801 0.017182
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> ## Permutation test for F
> permutest(mod, pairwise = TRUE)
@@ -5419,7 +5427,7 @@
Groups 1 0.07931 0.079306 4.6156 999 0.05 *
Residuals 22 0.37801 0.017182
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
@@ -5699,7 +5707,7 @@
RDA1 1 25.282 15.096 99 0.01 **
Residual 77 128.959
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
>
>
@@ -5861,7 +5869,7 @@
0.7398422
Quantiles of Procrustes errors:
Min 1Q Median 3Q Max
-0.02033237 0.27686842 0.34124239 0.64358542 2.23460568
+0.02033237 0.27686842 0.34124239 0.64358542 2.23460567
Rotation matrix:
[,1] [,2]
@@ -5870,7 +5878,7 @@
Translation of averages:
[,1] [,2]
-[1,] 2.827507e-19 5.561347e-18
+[1,] 3.038945e-17 1.218602e-17
Scaling of target:
[1] 0.6727804
@@ -5881,9 +5889,9 @@
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
-0.28008712 1.22839229 0.65574702 0.26736966 0.02033237 0.35725398 0.28654874
+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 2.23460568 0.31901411 0.21727135
+0.69324047 0.20264726 0.29744269 0.50070811 2.23460567 0.31901411 0.21727135
10 11 21
0.32523080 0.56506437 0.78179902
>
@@ -5963,6 +5971,9 @@
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
>
>
>
@@ -6926,7 +6937,7 @@
7 71.0050 76.2863 79.3749 3.2021 -0.0840 0.933019
Expected 74.5941 71.4315 79.6440
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> plot(mod)
>
>
@@ -7079,7 +7090,7 @@
12 572.35 0.797098 531.20 425.46 527.19 642.86 0.33
7 646.54 -0.606963 678.97 576.15 681.57 770.51 0.53
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> ## Clustering of tree distances
> dtree <- treedist(dune, cl)
> plot(hclust(dtree, "aver"))
@@ -7267,7 +7278,7 @@
Model 11 0.053592 1.8453 199 0.005 **
Residual 36 0.095050
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> # RsquareAdj gives the same result as component [a] of varpart
> RsquareAdj(aFrac)
$r.squared
@@ -7344,7 +7355,7 @@
Model 2 0.013771 2.6079 199 0.005 **
Residual 36 0.095050
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> # Four explanatory tables
> mod <- varpart(mite, ~ SubsDens + WatrCont, ~Substrate + Shrub + Topo,
@@ -7504,7 +7515,7 @@
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
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
P values based on 999 permutations.
@@ -7565,14 +7576,14 @@
+ A1 1 89.591 1.9217 999 0.045 *
+ Use 2 91.032 1.1741 99 0.210
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Step: dune ~ Management
Df AIC F N.Perm Pr(>F)
- Management 3 89.62 2.84 99 0.01 **
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Df AIC F N.Perm Pr(>F)
+ Moisture 3 85.567 1.9764 299 0.0200 *
@@ -7580,7 +7591,7 @@
+ A1 1 87.424 1.2965 99 0.1800
+ Use 2 88.284 1.0510 99 0.4000
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Step: dune ~ Management + Moisture
@@ -7588,7 +7599,7 @@
- Moisture 3 87.082 1.9764 99 0.01 **
- Management 3 87.707 2.1769 99 0.01 **
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Df AIC F N.Perm Pr(>F)
+ Manure 3 85.762 1.1225 99 0.27
@@ -7624,7 +7635,7 @@
Model 6 46.425 2.6682 199 0.005 **
Residual 13 37.699
---
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> ## Permutation test of "type III" effects, or significance when a term
> ## is added to the model after all other terms
> anova(mod, by = "margin")
@@ -7637,7 +7648,7 @@
Moisture 3 17.194 1.9764 199 0.005 **
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/vegan -r 2352
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