[Vegan-commits] r2241 - pkg/vegan/tests/Examples
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Tue Jul 31 13:43:34 CEST 2012
Author: jarioksa
Date: 2012-07-31 13:43:33 +0200 (Tue, 31 Jul 2012)
New Revision: 2241
Modified:
pkg/vegan/tests/Examples/vegan-Ex.Rout.save
Log:
update tests
Modified: pkg/vegan/tests/Examples/vegan-Ex.Rout.save
===================================================================
--- pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2012-07-31 11:43:26 UTC (rev 2240)
+++ pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2012-07-31 11:43:33 UTC (rev 2241)
@@ -23,7 +23,7 @@
> options(warn = 1)
> library('vegan')
Loading required package: permute
-This is vegan 2.1-17
+This is vegan 2.1-18
>
> assign(".oldSearch", search(), pos = 'CheckExEnv')
> cleanEx()
@@ -161,7 +161,7 @@
Formula:
y ~ poly(x1, 1) + poly(x2, 1)
-<environment: 0x102410b90>
+<environment: 0x102422ae8>
Total model degrees of freedom 3
GCV score: 0.0427924
@@ -508,10 +508,10 @@
+ out <- rep(1, length(x))
+ for (i in 2:(length(cut) - 1))
+ out[which(x > cut[i] & x <= cut[(i + 1)])] <- i
-+ return(as.factor(out))}
++ return(out)}
> ## The hierarchy of sample aggregation
> levsm <- data.frame(
-+ l1=as.factor(1:nrow(mite)),
++ l1=1:nrow(mite),
+ l2=cutter(mite.xy$y, cut = seq(0, 10, by = 2.5)),
+ l3=cutter(mite.xy$y, cut = seq(0, 10, by = 5)),
+ l4=cutter(mite.xy$y, cut = seq(0, 10, by = 10)))
@@ -523,6 +523,8 @@
> par(mfrow=c(1,1))
> ## Additive diversity partitioning
> adipart(mite, index="richness", nsimul=19)
+adipart object
+
Call: adipart(y = mite, index = "richness", nsimul = 19)
nullmodel method ‘r2dtable’ with 19 simulations
@@ -536,6 +538,8 @@
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> adipart(mite ~ ., levsm, index="richness", nsimul=19)
+adipart object
+
Call: adipart(formula = mite ~ ., data = levsm, index = "richness",
nsimul = 19)
@@ -556,6 +560,8 @@
> ## Hierarchical null model testing
> ## diversity analysis (similar to adipart)
> hiersimu(mite, FUN=diversity, relative=TRUE, nsimul=19)
+hiersimu object
+
Call: hiersimu(y = mite, FUN = diversity, relative = TRUE, nsimul = 19)
nullmodel method ‘r2dtable’ with 19 simulations
@@ -568,6 +574,8 @@
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> hiersimu(mite ~., levsm, FUN=diversity, relative=TRUE, nsimul=19)
+hiersimu object
+
Call: hiersimu(formula = mite ~ ., data = levsm, FUN = diversity,
relative = TRUE, nsimul = 19)
@@ -585,6 +593,8 @@
> ## Hierarchical testing with the Morisita index
> morfun <- function(x) dispindmorisita(x)$imst
> hiersimu(mite ~., levsm, morfun, drop.highest=TRUE, nsimul=19)
+hiersimu object
+
Call: hiersimu(formula = mite ~ ., data = levsm, FUN = morfun,
drop.highest = TRUE, nsimul = 19)
@@ -2542,7 +2552,7 @@
> ### Name: envfit
> ### Title: Fits an Environmental Vector or Factor onto an Ordination
> ### Aliases: envfit envfit.default envfit.formula vectorfit factorfit
-> ### plot.envfit scores.envfit
+> ### plot.envfit scores.envfit labels.envfit
> ### Keywords: multivariate aplot htest
>
> ### ** Examples
@@ -2638,6 +2648,16 @@
> ordispider(ord, Moisture, col="skyblue")
> points(ord, display = "sites", col = as.numeric(Moisture), pch=16)
> plot(fit, cex=1.2, axis=TRUE, bg = rgb(1, 1, 1, 0.5))
+> ## Use shorter labels for factor centroids
+> labels(fit)
+$vectors
+[1] "A1"
+
+$factors
+[1] "Moisture1" "Moisture2" "Moisture4" "Moisture5"
+
+> plot(ord)
+> plot(fit, labels=list(factors = paste0("M", c(1,2,4,5))), bg = rgb(1,1,0,0.5))
>
>
>
@@ -3983,15 +4003,17 @@
+ out <- rep(1, length(x))
+ for (i in 2:(length(cut) - 1))
+ out[which(x > cut[i] & x <= cut[(i + 1)])] <- i
-+ return(as.factor(out))}
++ return(out)}
> ## The hierarchy of sample aggregation
> levsm <- data.frame(
-+ l1=as.factor(1:nrow(mite)),
++ l1=1:nrow(mite),
+ l2=cutter(mite.xy$y, cut = seq(0, 10, by = 2.5)),
+ l3=cutter(mite.xy$y, cut = seq(0, 10, by = 5)),
+ l4=cutter(mite.xy$y, cut = seq(0, 10, by = 10)))
> ## Multiplicative diversity partitioning
> multipart(mite, levsm, index="renyi", scales=1, nsimul=19)
+multipart object
+
Call: multipart(y = mite, x = levsm, index = "renyi", scales = 1,
nsimul = 19)
@@ -4010,6 +4032,8 @@
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> multipart(mite ~ ., levsm, index="renyi", scales=1, nsimul=19)
+multipart object
+
Call: multipart(formula = mite ~ ., data = levsm, index = "renyi",
scales = 1, nsimul = 19)
@@ -4028,6 +4052,8 @@
---
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
+
Call: multipart(formula = mite ~ ., data = levsm, index = "renyi",
scales = 1, relative = TRUE, nsimul = 19)
@@ -4046,6 +4072,8 @@
---
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
+
Call: multipart(formula = mite ~ ., data = levsm, index = "renyi",
scales = 1, global = TRUE, nsimul = 19)
@@ -4093,6 +4121,8 @@
Checkerboard Units : 2767
C-score (species mean): 2.258776
> oecosimu(sipoo, nestedchecker, "quasiswap")
+oecosimu object
+
Call: oecosimu(comm = sipoo, nestfun = nestedchecker, method =
"quasiswap")
@@ -4107,6 +4137,8 @@
statistic 2767 0.84129 2698.1 2584.4 2676.0 2858.1 0.39
> ## Another Null model and standardized checkerboard score
> oecosimu(sipoo, nestedchecker, "r00", statistic = "C.score")
+oecosimu object
+
Call: oecosimu(comm = sipoo, nestfun = nestedchecker, method = "r00",
statistic = "C.score")
@@ -4234,6 +4266,8 @@
> data(sipoo)
> ## Traditional nestedness statistics (number of checkerboard units)
> oecosimu(sipoo, nestedchecker, "r0")
+oecosimu object
+
Call: oecosimu(comm = sipoo, nestfun = nestedchecker, method = "r0")
nullmodel method ‘r0’ with 99 simulations
@@ -4251,6 +4285,8 @@
> out <- oecosimu(sipoo, decorana, "swap", burnin=100, thin=10,
+ statistic="evals", alt = "less")
> out
+oecosimu object
+
Call: oecosimu(comm = sipoo, nestfun = decorana, method = "swap",
burnin = 100, thin = 10, statistic = "evals", alternative = "less")
@@ -4292,6 +4328,8 @@
> meandist <- function(x) mean(vegdist(x, "bray"))
> mbc1 <- oecosimu(dune, meandist, "r2dtable")
> mbc1
+oecosimu object
+
Call: oecosimu(comm = dune, nestfun = meandist, method = "r2dtable")
nullmodel method ‘r2dtable’ with 99 simulations
@@ -4314,6 +4352,8 @@
> cf <- commsim("myshuffle", foo, isSeq = FALSE, binary = FALSE,
+ mode = "double")
> oecosimu(dune, meandist, cf)
+oecosimu object
+
Call: oecosimu(comm = dune, nestfun = meandist, method = cf)
nullmodel method ‘myshuffle’ with 99 simulations
@@ -4863,7 +4903,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x109e1d800>
+<environment: 0x109c47348>
Estimated degrees of freedom:
6.4351 total = 7.435071
@@ -4879,7 +4919,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x108eafe40>
+<environment: 0x109a2bc00>
Estimated degrees of freedom:
6.1039 total = 7.103853
@@ -5035,7 +5075,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x107d94b18>
+<environment: 0x1070664b0>
Estimated degrees of freedom:
8.9275 total = 9.927492
@@ -5048,7 +5088,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x10a0ad580>
+<environment: 0x108ccc378>
Estimated degrees of freedom:
7.7529 total = 8.75294
@@ -5061,7 +5101,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x109de61b8>
+<environment: 0x10a9493c8>
Estimated degrees of freedom:
8.8962 total = 9.89616
@@ -7002,6 +7042,8 @@
> ## Significance test using Null model communities.
> ## The current choice fixes only site totals.
> oecosimu(dune, treedive, "r0", tree = cl)
+oecosimu object
+
Call: oecosimu(comm = dune, nestfun = treedive, method = "r0", tree =
cl)
@@ -7603,7 +7645,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x10a886eb0>
+<environment: 0x10a89acd8>
Estimated degrees of freedom:
2 total = 3
@@ -8083,7 +8125,7 @@
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", pos = 'CheckExEnv'),"\n")
-Time elapsed: 78.943 1.479 80.482 0 0
+Time elapsed: 81.617 1.462 84.217 0 0
> grDevices::dev.off()
null device
1
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