[Vegan-commits] r2324 - pkg/vegan/tests/Examples
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Fri Oct 12 10:00:18 CEST 2012
Author: jarioksa
Date: 2012-10-12 10:00:17 +0200 (Fri, 12 Oct 2012)
New Revision: 2324
Modified:
pkg/vegan/tests/Examples/vegan-Ex.Rout.save
Log:
update tests for r2318
Modified: pkg/vegan/tests/Examples/vegan-Ex.Rout.save
===================================================================
--- pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2012-10-10 16:17:19 UTC (rev 2323)
+++ pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2012-10-12 08:00:17 UTC (rev 2324)
@@ -1,8 +1,8 @@
-R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows"
+R Under development (unstable) (2012-10-12 r60919) -- "Unsuffered Consequences"
Copyright (C) 2012 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 2.1-19
+This is vegan 2.1-21
>
> 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 1.7-21. For overview type 'help("mgcv-package")'.
Family: gaussian
Link function: identity
Formula:
y ~ poly(x1, 1) + poly(x2, 1)
-<environment: 0x102470f90>
+<environment: 0x2ab7cc0>
Total model degrees of freedom 3
GCV score: 0.0427924
@@ -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)
@@ -2345,71 +2349,71 @@
> data(dune)
> x <- dispindmorisita(dune)
> x
- imor mclu muni imst
-Belper 2.0512821 2.154361 0.158876373 0.45535255
-Empnig 20.0000000 14.852327 -9.093483518 1.00000000
-Junbuf 4.1025641 2.154361 0.158876373 0.55458486
-Junart 3.1372549 1.814843 0.406265675 0.53635966
-Airpra 8.0000000 4.463082 -1.523370880 0.61382303
-Elepal 3.7333333 1.577180 0.579438187 0.55851854
-Rumace 3.9215686 1.814843 0.406265675 0.55792432
-Viclat 3.3333333 5.617442 -2.364494506 0.25266513
-Brarut 1.1904762 1.288590 0.789719093 0.33001160
-Ranfla 2.4175824 2.065564 0.223578191 0.50981405
-Cirarv 20.0000000 14.852327 -9.093483518 1.00000000
-Hyprad 6.6666667 2.731541 -0.261685440 0.61393969
-Leoaut 0.9643606 1.261365 0.809556915 -0.09356972
-Potpal 6.6666667 5.617442 -2.364494506 0.53647558
-Poapra 1.1702128 1.294730 0.785245032 0.28876015
-Calcus 5.3333333 2.539147 -0.121498169 0.58001287
-Tripra 6.6666667 2.731541 -0.261685440 0.61393969
-Trirep 1.2210916 1.301138 0.780576445 0.36709402
-Antodo 2.6666667 1.692616 0.495325824 0.52660266
-Salrep 5.8181818 2.385233 -0.009348352 0.59744520
-Achmil 2.1666667 1.923488 0.327101099 0.50672636
-Poatri 1.4644137 1.223425 0.837201879 0.50641728
-Chealb NaN Inf -Inf NaN
-Elyrep 2.7692308 1.554093 0.596260659 0.53293787
-Sagpro 2.4210526 1.729070 0.468764025 0.51893672
-Plalan 2.4615385 1.554093 0.596260659 0.52459747
-Agrsto 1.8085106 1.294730 0.785245032 0.51373357
-Lolper 1.5849970 1.243023 0.822921342 0.50911591
-Alogen 2.5396825 1.395781 0.711614757 0.53074307
-Brohor 3.2380952 1.989452 0.279036892 0.53466422
+ imor mclu muni imst pchisq
+Belper 2.0512821 2.154361 0.158876373 0.45535255 3.451547e-02
+Empnig 20.0000000 14.852327 -9.093483518 1.00000000 5.934709e-03
+Junbuf 4.1025641 2.154361 0.158876373 0.55458486 1.503205e-05
+Junart 3.1372549 1.814843 0.406265675 0.53635966 2.066336e-05
+Airpra 8.0000000 4.463082 -1.523370880 0.61382303 3.571702e-04
+Elepal 3.7333333 1.577180 0.579438187 0.55851854 2.958285e-10
+Rumace 3.9215686 1.814843 0.406265675 0.55792432 1.530085e-07
+Viclat 3.3333333 5.617442 -2.364494506 0.25266513 1.301890e-01
+Brarut 1.1904762 1.288590 0.789719093 0.33001160 8.071762e-02
+Ranfla 2.4175824 2.065564 0.223578191 0.50981405 7.010483e-03
+Cirarv 20.0000000 14.852327 -9.093483518 1.00000000 5.934709e-03
+Hyprad 6.6666667 2.731541 -0.261685440 0.61393969 7.832274e-07
+Leoaut 0.9643606 1.261365 0.809556915 -0.09356972 5.823404e-01
+Potpal 6.6666667 5.617442 -2.364494506 0.53647558 1.055552e-02
+Poapra 1.1702128 1.294730 0.785245032 0.28876015 1.046531e-01
+Calcus 5.3333333 2.539147 -0.121498169 0.58001287 7.982634e-06
+Tripra 6.6666667 2.731541 -0.261685440 0.61393969 7.832274e-07
+Trirep 1.2210916 1.301138 0.780576445 0.36709402 6.335449e-02
+Antodo 2.6666667 1.692616 0.495325824 0.52660266 5.897217e-05
+Salrep 5.8181818 2.385233 -0.009348352 0.59744520 2.687397e-07
+Achmil 2.1666667 1.923488 0.327101099 0.50672636 9.157890e-03
+Poatri 1.4644137 1.223425 0.837201879 0.50641728 2.747301e-04
+Chealb NaN Inf -Inf NaN NaN
+Elyrep 2.7692308 1.554093 0.596260659 0.53293787 1.180195e-06
+Sagpro 2.4210526 1.729070 0.468764025 0.51893672 4.956394e-04
+Plalan 2.4615385 1.554093 0.596260659 0.52459747 1.921730e-05
+Agrsto 1.8085106 1.294730 0.785245032 0.51373357 1.142619e-05
+Lolper 1.5849970 1.243023 0.822921342 0.50911591 5.873839e-05
+Alogen 2.5396825 1.395781 0.711614757 0.53074307 3.024441e-08
+Brohor 3.2380952 1.989452 0.279036892 0.53466422 1.170437e-04
> y <- dispindmorisita(dune, unique.rm = TRUE)
> y
- imor mclu muni imst
-Belper 2.0512821 2.154361 0.158876373 0.45535255
-Junbuf 4.1025641 2.154361 0.158876373 0.55458486
-Junart 3.1372549 1.814843 0.406265675 0.53635966
-Airpra 8.0000000 4.463082 -1.523370880 0.61382303
-Elepal 3.7333333 1.577180 0.579438187 0.55851854
-Rumace 3.9215686 1.814843 0.406265675 0.55792432
-Viclat 3.3333333 5.617442 -2.364494506 0.25266513
-Brarut 1.1904762 1.288590 0.789719093 0.33001160
-Ranfla 2.4175824 2.065564 0.223578191 0.50981405
-Hyprad 6.6666667 2.731541 -0.261685440 0.61393969
-Leoaut 0.9643606 1.261365 0.809556915 -0.09356972
-Potpal 6.6666667 5.617442 -2.364494506 0.53647558
-Poapra 1.1702128 1.294730 0.785245032 0.28876015
-Calcus 5.3333333 2.539147 -0.121498169 0.58001287
-Tripra 6.6666667 2.731541 -0.261685440 0.61393969
-Trirep 1.2210916 1.301138 0.780576445 0.36709402
-Antodo 2.6666667 1.692616 0.495325824 0.52660266
-Salrep 5.8181818 2.385233 -0.009348352 0.59744520
-Achmil 2.1666667 1.923488 0.327101099 0.50672636
-Poatri 1.4644137 1.223425 0.837201879 0.50641728
-Elyrep 2.7692308 1.554093 0.596260659 0.53293787
-Sagpro 2.4210526 1.729070 0.468764025 0.51893672
-Plalan 2.4615385 1.554093 0.596260659 0.52459747
-Agrsto 1.8085106 1.294730 0.785245032 0.51373357
-Lolper 1.5849970 1.243023 0.822921342 0.50911591
-Alogen 2.5396825 1.395781 0.711614757 0.53074307
-Brohor 3.2380952 1.989452 0.279036892 0.53466422
+ imor mclu muni imst pchisq
+Belper 2.0512821 2.154361 0.158876373 0.45535255 3.451547e-02
+Junbuf 4.1025641 2.154361 0.158876373 0.55458486 1.503205e-05
+Junart 3.1372549 1.814843 0.406265675 0.53635966 2.066336e-05
+Airpra 8.0000000 4.463082 -1.523370880 0.61382303 3.571702e-04
+Elepal 3.7333333 1.577180 0.579438187 0.55851854 2.958285e-10
+Rumace 3.9215686 1.814843 0.406265675 0.55792432 1.530085e-07
+Viclat 3.3333333 5.617442 -2.364494506 0.25266513 1.301890e-01
+Brarut 1.1904762 1.288590 0.789719093 0.33001160 8.071762e-02
+Ranfla 2.4175824 2.065564 0.223578191 0.50981405 7.010483e-03
+Hyprad 6.6666667 2.731541 -0.261685440 0.61393969 7.832274e-07
+Leoaut 0.9643606 1.261365 0.809556915 -0.09356972 5.823404e-01
+Potpal 6.6666667 5.617442 -2.364494506 0.53647558 1.055552e-02
+Poapra 1.1702128 1.294730 0.785245032 0.28876015 1.046531e-01
+Calcus 5.3333333 2.539147 -0.121498169 0.58001287 7.982634e-06
+Tripra 6.6666667 2.731541 -0.261685440 0.61393969 7.832274e-07
+Trirep 1.2210916 1.301138 0.780576445 0.36709402 6.335449e-02
+Antodo 2.6666667 1.692616 0.495325824 0.52660266 5.897217e-05
+Salrep 5.8181818 2.385233 -0.009348352 0.59744520 2.687397e-07
+Achmil 2.1666667 1.923488 0.327101099 0.50672636 9.157890e-03
+Poatri 1.4644137 1.223425 0.837201879 0.50641728 2.747301e-04
+Elyrep 2.7692308 1.554093 0.596260659 0.53293787 1.180195e-06
+Sagpro 2.4210526 1.729070 0.468764025 0.51893672 4.956394e-04
+Plalan 2.4615385 1.554093 0.596260659 0.52459747 1.921730e-05
+Agrsto 1.8085106 1.294730 0.785245032 0.51373357 1.142619e-05
+Lolper 1.5849970 1.243023 0.822921342 0.50911591 5.873839e-05
+Alogen 2.5396825 1.395781 0.711614757 0.53074307 3.024441e-08
+Brohor 3.2380952 1.989452 0.279036892 0.53466422 1.170437e-04
> dim(x) ## with unique species
-[1] 30 4
+[1] 30 5
> dim(y) ## unique species removed
-[1] 27 4
+[1] 27 5
>
>
>
@@ -2611,7 +2615,7 @@
Run 17 stress 0.1825664
... New best solution
... procrustes: rmse 0.0421789 max resid 0.1544029
-Run 18 stress 0.1843199
+Run 18 stress 0.1843201
Run 19 stress 0.2570123
Run 20 stress 0.3760596
> (fit <- envfit(ord, varechem, perm = 999))
@@ -3090,7 +3094,7 @@
Run 17 stress 0.1825664
... New best solution
... procrustes: rmse 0.0421789 max resid 0.1544029
-Run 18 stress 0.1843199
+Run 18 stress 0.1843201
Run 19 stress 0.2570123
Run 20 stress 0.3760596
> stressplot(mod)
@@ -4340,7 +4344,7 @@
alternative hypothesis: simulated median is not equal to the statistic
statistic z mean 2.5% 50% 97.5% Pr(sim.)
-statistic 0.64565 13.06 0.46704 0.44057 0.46544 0.4924 0.01 **
+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
>
@@ -4364,7 +4368,7 @@
alternative hypothesis: simulated median is not equal to the statistic
statistic z mean 2.5% 50% 97.5% Pr(sim.)
-statistic 0.64565 3.1188 0.63551 0.62983 0.63557 0.6416 0.03 *
+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
>
@@ -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 1.7-21. For overview type 'help("mgcv-package")'.
Family: gaussian
Link function: identity
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x109eec260>
+<environment: 0xa05c300>
Estimated degrees of freedom:
-6.4351 total = 7.435071
+6.44 total = 7.44
GCV score: 144.1236
>
@@ -4922,10 +4926,10 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x10ab8bb78>
+<environment: 0x9df8f50>
Estimated degrees of freedom:
-6.1039 total = 7.103853
+6.1 total = 7.1
GCV score: 140.0949
>
@@ -4934,13 +4938,13 @@
> ## Get fitted values
> calibrate(fit)
1 2 3 4 5 6 7
-22.0644614 6.0132251 3.6350483 4.1019742 9.0030989 5.9202601 8.6399184
+22.0644615 6.0132250 3.6350484 4.1019743 9.0030990 5.9202602 8.6399182
8 9 10 11 12 13 14
-11.0719303 0.6561781 35.2282118 10.4346331 7.2900018 5.5710617 24.6503110
+11.0719302 0.6561783 35.2282116 10.4346331 7.2900019 5.5710617 24.6503109
15 16 17 18 19 20 21
-18.8754521 29.7286543 5.6158934 9.5869716 3.2876267 2.7111721 10.7832857
+18.8754520 29.7286540 5.6158934 9.5869715 3.2876268 2.7111723 10.7832857
22 23 24
- 3.0020413 9.8128952 7.3656932
+ 3.0020415 9.8128952 7.3656934
>
> ## 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 1.7-21. For overview type 'help("mgcv-package")'.
Family: gaussian
Link function: identity
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x10566b408>
+<environment: 0x702bcf8>
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: 0x109a7da08>
+<environment: 0x8de5c00>
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: 0x1098ed878>
+<environment: 0x80c4518>
Estimated degrees of freedom:
-8.8962 total = 9.89616
+8.9 total = 9.9
GCV score: 0.002508871
> par(op)
@@ -5869,8 +5877,8 @@
[2,] 0.1169579 0.9931369
Translation of averages:
- [,1] [,2]
-[1,] 3.893131e-18 -9.725997e-18
+ [,1] [,2]
+[1,] 1.674651e-17 2.106557e-17
Scaling of target:
[1] 0.6736868
@@ -5917,10 +5925,14 @@
>
> ### Name: radfit
> ### Title: Rank - Abundance or Dominance / Diversity Models
-> ### Aliases: radfit radfit.default radfit.data.frame AIC.radfit as.rad
-> ### coef.radfit fitted.radfit lines.radline plot.radfit.frame plot.radfit
-> ### plot.radline plot.rad radlattice points.radline summary.radfit.frame
-> ### rad.preempt rad.lognormal rad.zipf rad.zipfbrot rad.null
+> ### Aliases: radfit radfit.default radfit.data.frame AIC.radfit
+> ### AIC.radfit.frame as.rad coef.radfit coef.radfit.frame deviance.radfit
+> ### deviance.radfit.frame 'logLik, radfit' 'logLik, radfit.frame'
+> ### fitted.radfit fitted.radfit.frame lines.radline lines.radfit
+> ### plot.radfit.frame plot.radfit plot.radline plot.rad radlattice
+> ### points.radline points.radfit summary.radfit.frame rad.preempt
+> ### rad.lognormal rad.zipf rad.zipfbrot rad.null predict.radline
+> ### predict.radfit predict.radfit.frame
> ### Keywords: univar distribution
>
> ### ** Examples
@@ -5959,6 +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
>
>
>
@@ -7243,8 +7258,12 @@
[d] = Residuals 0.46206 FALSE
---
Use function 'rda' to test significance of fractions of interest
-> showvarparts(2)
-> plot(mod)
+>
+> ## argument 'bg' is passed to circle drawing, and the following
+> ## defines semitransparent colours
+> col2 <- rgb(c(1,1),c(1,0), c(0,1), 0.3)
+> showvarparts(2, bg = col2)
+> plot(mod, bg = col2)
> # Alternative way of to conduct this partitioning
> # Change the data frame with factors into numeric model matrix
> mm <- model.matrix(~ SubsDens + WatrCont + Substrate + Shrub + Topo, mite.env)[,-1]
@@ -7470,7 +7489,7 @@
Run 17 stress 0.1825664
... New best solution
... procrustes: rmse 0.0421789 max resid 0.1544029
-Run 18 stress 0.1843199
+Run 18 stress 0.1843201
Run 19 stress 0.2570123
Run 20 stress 0.3760596
> plot(ord, type = "t")
@@ -7640,14 +7659,14 @@
> ## add fitted surface of diversity to the model
> ordisurf(mod, diversity(dune), add = TRUE)
Loading required package: mgcv
-This is mgcv 1.7-18. For overview type 'help("mgcv-package")'.
+This is mgcv 1.7-21. For overview type 'help("mgcv-package")'.
Family: gaussian
Link function: identity
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x109b19340>
+<environment: 0xac90180>
Estimated degrees of freedom:
2 total = 3
@@ -8094,13 +8113,13 @@
> ## Eigevalues are numerically similar
> ca$CA$eig - ord$eig
CA1 CA2 CA3 CA4 CA5
--7.771561e-16 -2.220446e-16 6.106227e-16 -3.608225e-16 -1.110223e-16
+-9.992007e-16 -1.665335e-16 6.661338e-16 -3.330669e-16 -1.110223e-16
CA6 CA7 CA8 CA9 CA10
- 1.387779e-17 9.714451e-17 1.387779e-17 2.775558e-17 1.318390e-16
+ 2.775558e-17 1.249001e-16 1.387779e-17 1.387779e-17 1.249001e-16
CA11 CA12 CA13 CA14 CA15
- 9.714451e-17 6.938894e-18 -6.938894e-18 3.122502e-17 0.000000e+00
+ 8.326673e-17 6.938894e-18 -1.387779e-17 2.775558e-17 0.000000e+00
CA16 CA17 CA18 CA19
--3.295975e-17 2.428613e-17 2.862294e-17 5.637851e-18
+-3.295975e-17 2.428613e-17 2.949030e-17 5.637851e-18
> ## Configurations are similar when site scores are scaled by
> ## eigenvalues in CA
> procrustes(ord, ca, choices=1:19, scaling = 1)
@@ -8109,7 +8128,7 @@
procrustes(X = ord, Y = ca, choices = 1:19, scaling = 1)
Procrustes sum of squares:
- 0
+-4.263e-14
> plot(procrustes(ord, ca, choices=1:2, scaling=1))
> ## Reconstruction of non-Euclidean distances with negative eigenvalues
@@ -8127,7 +8146,7 @@
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", pos = 'CheckExEnv'),"\n")
-Time elapsed: 82.41 1.457 84.423 0 0
+Time elapsed: 25.917 0.18 26.257 0 0
> grDevices::dev.off()
null device
1
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