[Vegan-commits] r2560 - pkg/vegan/tests/Examples
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Thu Jul 11 13:44:53 CEST 2013
Author: jarioksa
Date: 2013-07-11 13:44:52 +0200 (Thu, 11 Jul 2013)
New Revision: 2560
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 2013-07-11 11:39:10 UTC (rev 2559)
+++ pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2013-07-11 11:44:52 UTC (rev 2560)
@@ -1,8 +1,7 @@
-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-pc-linux-gnu (64-bit)
+R Under development (unstable) (2013-07-10 r63264) -- "Unsuffered Consequences"
+Copyright (C) 2013 The R Foundation for Statistical Computing
+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.
@@ -23,9 +22,9 @@
> options(warn = 1)
> library('vegan')
Loading required package: permute
-This is vegan 2.1-29
+This is vegan 2.1-32
>
-> assign(".oldSearch", search(), pos = 'CheckExEnv')
+> base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
> cleanEx()
> nameEx("BCI")
> ### * BCI
@@ -154,14 +153,14 @@
> plot(ef)
> ordisurf(mod ~ pH, varechem, knots = 1, add = TRUE)
Loading required package: mgcv
-This is mgcv 1.7-22. For overview type 'help("mgcv-package")'.
+This is mgcv 1.7-24. For overview type 'help("mgcv-package")'.
Family: gaussian
Link function: identity
Formula:
y ~ poly(x1, 1) + poly(x2, 1)
-<environment: 0x1db31c0>
+<environment: 0x3371698>
Total model degrees of freedom 3
REML score: -3.185099
@@ -227,7 +226,7 @@
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 +304,14 @@
> 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 +347,14 @@
> 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:
@@ -395,27 +394,27 @@
dune ~ 1
Df AIC F N.Perm Pr(>F)
-+ Moisture 3 86.608 2.2536 199 0.010 **
++ Moisture 3 86.608 2.2536 199 0.005 **
+ Management 3 86.935 2.1307 199 0.005 **
-+ A1 1 87.411 2.1400 199 0.020 *
++ A1 1 87.411 2.1400 199 0.025 *
<none> 87.657
-+ Manure 4 88.832 1.5251 199 0.025 *
-+ Use 2 89.134 1.1431 99 0.250
++ Manure 4 88.832 1.5251 199 0.020 *
++ Use 2 89.134 1.1431 99 0.260
---
-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
Df AIC F N.Perm Pr(>F)
<none> 86.608
-+ 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 **
++ 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 **
---
-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
@@ -447,38 +446,38 @@
> add1(m0, scope=formula(mbig), test="perm")
Df AIC F N.Perm Pr(>F)
<none> 89.620
-A1 1 89.591 1.9217 199 0.070 .
+A1 1 89.591 1.9217 199 0.030 *
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 0.180
+Use 2 91.032 1.1741 99 0.170
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)
-<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
+ 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
---
-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")
Df AIC F N.Perm Pr(>F)
<none> 85.567
-Management 3 87.707 2.1769 199 0.010 **
-Moisture 3 87.082 1.9764 199 0.015 *
+Management 3 87.707 2.1769 199 0.005 **
+Moisture 3 87.082 1.9764 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
> add1(m0, scope=formula(mbig), test="perm")
Df AIC F N.Perm Pr(>F)
<none> 85.567
-A1 1 86.220 0.8359 99 0.72
-Use 2 86.842 0.8027 99 0.77
-Manure 3 85.762 1.1225 99 0.26
+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
>
>
>
@@ -536,7 +535,7 @@
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 +555,7 @@
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 +571,7 @@
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 +588,7 @@
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 +606,7 @@
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 +641,7 @@
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.
@@ -697,7 +696,7 @@
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 +711,7 @@
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 +795,7 @@
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 +805,7 @@
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 +818,7 @@
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 +831,7 @@
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 +842,7 @@
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
>
>
>
@@ -1134,28 +1133,34 @@
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)
Permutation test for homogeneity of multivariate dispersions
-No. of permutations: 999
+Permutation Design:
-**** STRATA ****
-Permutations are unstratified
+Blocks:
+ Defined by: none
-**** SAMPLES ****
-Permutation type: free
-Mirrored permutations for Samples?: No
+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 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)
@@ -1301,19 +1306,25 @@
Eigenvalues for PCoA axes:
PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
1.4755 0.8245 0.4218 0.3456 0.2159 0.1688 0.1150 0.1060
-> permutest(mod2, control = permControl(nperm = 100))
+> permutest(mod2, control = how(nperm = 100))
Permutation test for homogeneity of multivariate dispersions
-No. of permutations: 100
+Permutation Design:
-**** STRATA ****
-Permutations are unstratified
+Blocks:
+ Defined by: none
-**** SAMPLES ****
-Permutation type: free
-Mirrored permutations for Samples?: No
+Plots:
+ Defined by: none
+Within Plots:
+ Permutation type: free
+
+Permutation details:
+ Number of permutations requested: 100
+ 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 100 0.1584
@@ -1351,25 +1362,31 @@
Eigenvalues for PCoA axes:
PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
1.4755 0.8245 0.4218 0.3456 0.2159 0.1688 0.1150 0.1060
-> permutest(mod3, control = permControl(nperm = 100))
+> permutest(mod3, control = how(nperm = 100))
Permutation test for homogeneity of multivariate dispersions
-No. of permutations: 100
+Permutation Design:
-**** STRATA ****
-Permutations are unstratified
+Blocks:
+ Defined by: none
-**** SAMPLES ****
-Permutation type: free
-Mirrored permutations for Samples?: No
+Plots:
+ Defined by: none
+Within Plots:
+ Permutation type: free
+
+Permutation details:
+ Number of permutations requested: 100
+ 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 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
@@ -1378,7 +1395,7 @@
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))
@@ -1526,8 +1543,9 @@
Subset of environmental variables with best correlation to community data.
-Correlations: spearman
-Dissimilarities: bray
+Correlations: spearman
+Dissimilarities: bray
+Metric: euclidean
Best model has 3 parameters (max. 6 allowed):
P Ca Al
@@ -1617,7 +1635,7 @@
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)
@@ -2338,7 +2356,7 @@
Pearson's Chi-squared test
-data: dune
+data: dune
X-squared = 1448.956, df = 551, p-value < 2.2e-16
> deviance(cca(dune))
@@ -2473,6 +2491,112 @@
>
>
> cleanEx()
+> nameEx("dispweight")
+> ### * dispweight
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: dispweight
+> ### Title: Dispersion-based weighting of species counts
+> ### Aliases: dispweight
+> ### Keywords: multivariate
+>
+> ### ** Examples
+>
+> data(dune)
+> data(dune.env)
+> # calculate weights
+> dpw <- dispweight(dune, dune.env$Management, nperm = 100)
+> # transformed community data
+> dpw$transformed
+ Belper Empnig Junbuf Junart Airpra Elepal Rumace Viclat Brarut
+2 1.666667 0 0.0000000 0.000000 0 0.0000000 0 0 0.000000
+13 0.000000 0 1.0759013 0.000000 0 0.0000000 0 0 0.000000
+4 1.111111 0 0.0000000 0.000000 0 0.0000000 0 0 1.196356
+16 0.000000 0 0.0000000 1.013793 0 1.6159822 0 0 2.392711
+6 0.000000 0 0.0000000 0.000000 0 0.0000000 6 0 3.589067
+1 0.000000 0 0.0000000 0.000000 0 0.0000000 0 0 0.000000
+8 0.000000 0 0.0000000 1.351724 0 0.8079911 0 0 1.196356
+5 1.111111 0 0.0000000 0.000000 0 0.0000000 5 0 1.196356
+17 0.000000 0 0.0000000 0.000000 2 0.0000000 0 0 0.000000
+15 0.000000 0 0.0000000 1.013793 0 1.0099889 0 0 2.392711
+10 1.111111 0 0.0000000 0.000000 0 0.0000000 0 1 1.196356
+11 0.000000 0 0.0000000 0.000000 0 0.0000000 0 2 2.392711
+9 0.000000 0 1.4345351 1.351724 0 0.0000000 2 0 1.196356
+18 1.111111 0 0.0000000 0.000000 0 0.0000000 0 1 3.589067
+3 1.111111 0 0.0000000 0.000000 0 0.0000000 0 0 1.196356
+20 0.000000 0 0.0000000 1.351724 0 0.8079911 0 0 2.392711
+14 0.000000 0 0.0000000 0.000000 0 0.8079911 0 0 0.000000
+19 0.000000 2 0.0000000 0.000000 3 0.0000000 0 0 1.794533
+12 0.000000 0 1.4345351 0.000000 0 0.0000000 2 0 2.392711
+7 0.000000 0 0.7172676 0.000000 0 0.0000000 3 0 1.196356
+ Ranfla Cirarv Hyprad Leoaut Potpal Poapra Calcus Tripra Trirep
+2 0.000000 0 0.0000000 5 0 4 0.000000 0 5
+13 1.076923 0 0.0000000 2 0 2 0.000000 0 2
+4 0.000000 2 0.0000000 2 0 4 0.000000 0 1
+16 1.076923 0 0.0000000 0 0 0 1.019417 0 0
+6 0.000000 0 0.0000000 3 0 3 0.000000 5 5
+1 0.000000 0 0.0000000 0 0 4 0.000000 0 0
+8 1.076923 0 0.0000000 3 0 4 0.000000 0 2
+5 0.000000 0 0.0000000 3 0 2 0.000000 2 2
+17 0.000000 0 0.6405229 2 0 1 0.000000 0 0
+15 1.076923 0 0.0000000 2 2 0 0.000000 0 1
+10 0.000000 0 0.0000000 3 0 4 0.000000 0 6
+11 0.000000 0 0.6405229 5 0 4 0.000000 0 3
+9 0.000000 0 0.0000000 2 0 4 0.000000 0 3
+18 0.000000 0 0.0000000 5 0 3 0.000000 0 2
+3 0.000000 0 0.0000000 2 0 5 0.000000 0 2
+20 2.153846 0 0.0000000 2 0 0 1.019417 0 0
+14 1.076923 0 0.0000000 2 2 0 1.359223 0 6
+19 0.000000 0 1.6013072 6 0 0 0.000000 0 2
+12 0.000000 0 0.0000000 2 0 0 0.000000 0 3
+7 0.000000 0 0.0000000 3 0 4 0.000000 2 2
+ Antodo Salrep Achmil Poatri Chealb Elyrep Sagpro Plalan
+2 0.0000000 0.000000 3 7 0 1.222222 0.0000000 0.0000000
+13 0.0000000 0.000000 0 9 1 0.000000 0.8301887 0.0000000
+4 0.0000000 0.000000 0 5 0 1.222222 2.0754717 0.0000000
+16 0.0000000 0.000000 0 2 0 0.000000 0.0000000 0.0000000
+6 1.0607143 0.000000 2 4 0 0.000000 0.0000000 2.3305085
+1 0.0000000 0.000000 1 2 0 1.222222 0.0000000 0.0000000
+8 0.0000000 0.000000 0 4 0 0.000000 0.8301887 0.0000000
+5 1.4142857 0.000000 2 6 0 1.222222 0.0000000 2.3305085
+17 1.4142857 0.000000 2 0 0 0.000000 0.0000000 0.9322034
+15 0.0000000 0.000000 0 0 0 0.000000 0.0000000 0.0000000
+10 1.4142857 0.000000 4 4 0 0.000000 0.0000000 1.3983051
+11 0.0000000 0.000000 0 0 0 0.000000 0.8301887 1.3983051
+9 0.0000000 0.000000 0 5 0 1.833333 0.8301887 0.0000000
+18 0.0000000 1.204380 0 0 0 0.000000 0.0000000 1.3983051
+3 0.0000000 0.000000 0 6 0 1.222222 0.0000000 0.0000000
+20 0.0000000 2.007299 0 0 0 0.000000 0.0000000 0.0000000
+14 0.0000000 0.000000 0 0 0 0.000000 0.0000000 0.0000000
+19 1.4142857 1.204380 0 0 0 0.000000 1.2452830 0.0000000
+12 0.0000000 0.000000 0 4 0 0.000000 1.6603774 0.0000000
+7 0.7071429 0.000000 2 5 0 0.000000 0.0000000 2.3305085
+ Agrsto Lolper Alogen Brohor
+2 0.000000 2.424242 0.7938931 1.76
+13 2.147674 0.000000 1.9847328 0.00
+4 3.436278 2.424242 0.7938931 1.32
+16 3.006743 0.000000 1.5877863 0.00
+6 0.000000 2.909091 0.0000000 0.00
+1 0.000000 3.393939 0.0000000 0.00
+8 1.718139 1.939394 1.9847328 0.00
+5 0.000000 0.969697 0.0000000 0.88
+17 0.000000 0.000000 0.0000000 0.00
+15 1.718139 0.000000 0.0000000 0.00
+10 0.000000 2.909091 0.0000000 1.76
+11 0.000000 3.393939 0.0000000 0.00
+9 1.288604 0.969697 1.1908397 0.00
+18 0.000000 0.969697 0.0000000 0.00
+3 1.718139 2.909091 2.7786260 0.00
+20 2.147674 0.000000 0.0000000 0.00
+14 1.718139 0.000000 0.0000000 0.00
+19 0.000000 0.000000 0.0000000 0.00
+12 1.718139 0.000000 3.1755725 0.00
+7 0.000000 2.909091 0.0000000 0.88
+>
+>
+>
+> cleanEx()
> nameEx("distconnected")
> ### * distconnected
>
@@ -2693,7 +2817,7 @@
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.
@@ -3555,7 +3679,7 @@
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)
@@ -3594,7 +3718,7 @@
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.
@@ -4091,7 +4215,7 @@
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
@@ -4111,7 +4235,7 @@
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
@@ -4131,7 +4255,7 @@
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
@@ -4151,7 +4275,7 @@
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
>
>
>
@@ -4213,7 +4337,7 @@
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
>
>
>
@@ -4341,7 +4465,7 @@
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 = "greater")
@@ -4374,7 +4498,7 @@
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
---
-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))
@@ -4400,7 +4524,7 @@
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
@@ -4424,7 +4548,7 @@
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
>
>
>
@@ -4634,17 +4758,28 @@
>
> ### Name: ordipointlabel
> ### Title: Ordination Plots with Points and Optimized Locations for Text
-> ### Aliases: ordipointlabel
+> ### Aliases: ordipointlabel plot.ordipointlabel
> ### Keywords: hplot aplot
>
> ### ** Examples
>
> data(dune)
> ord <- cca(dune)
-> ordipointlabel(ord)
+> plt <- ordipointlabel(ord)
>
+> ## set scaling - should be no warnings!
+> ordipointlabel(ord, scaling = 1)
>
+> ## plot then add
+> plot(ord, scaling = 3, type = "n")
+> ordipointlabel(ord, display = "species", scaling = 3, add = TRUE)
+> ordipointlabel(ord, display = "sites", scaling = 3, add = TRUE)
>
+> ## redraw plot without rerunning SANN optimisation
+> plot(plt)
+>
+>
+>
> cleanEx()
> nameEx("ordiresids")
> ### * ordiresids
@@ -4702,14 +4837,14 @@
+ 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 **
@@ -4717,7 +4852,7 @@
+ 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
@@ -4725,7 +4860,7 @@
- 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
@@ -4776,7 +4911,7 @@
- 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
@@ -4784,7 +4919,7 @@
- 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)
@@ -4832,7 +4967,7 @@
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
@@ -4849,7 +4984,7 @@
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
@@ -4866,7 +5001,7 @@
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
@@ -4883,7 +5018,7 @@
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
@@ -4900,7 +5035,7 @@
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
@@ -4923,7 +5058,7 @@
+ 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:
@@ -4953,14 +5088,14 @@
> vare.mds <- monoMDS(vare.dist)
> with(varechem, ordisurf(vare.mds, Baresoil, bubble = 5))
Loading required package: mgcv
-This is mgcv 1.7-22. For overview type 'help("mgcv-package")'.
+This is mgcv 1.7-24. For overview type 'help("mgcv-package")'.
Family: gaussian
Link function: identity
Formula:
y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
-<environment: 0x8fd4ae0>
+<environment: 0xa7eec90>
Estimated degrees of freedom:
5.63 total = 6.63
@@ -4977,7 +5112,7 @@
Formula:
y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
-<environment: 0x84775b0>
+<environment: 0x8ac38d0>
Estimated degrees of freedom:
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/vegan -r 2560
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