[Vegan-commits] r2431 - in pkg/vegan: . R inst man tests/Examples
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Mon Feb 11 19:34:53 CET 2013
Author: jarioksa
Date: 2013-02-11 19:34:53 +0100 (Mon, 11 Feb 2013)
New Revision: 2431
Added:
pkg/vegan/R/tabasco.R
Modified:
pkg/vegan/NAMESPACE
pkg/vegan/inst/ChangeLog
pkg/vegan/man/vegemite.Rd
pkg/vegan/tests/Examples/vegan-Ex.Rout.save
Log:
add tabasco: a sister to vegemite to display compact tables, but hotter
Modified: pkg/vegan/NAMESPACE
===================================================================
--- pkg/vegan/NAMESPACE 2013-02-11 12:36:04 UTC (rev 2430)
+++ pkg/vegan/NAMESPACE 2013-02-11 18:34:53 UTC (rev 2431)
@@ -25,7 +25,7 @@
rda, renyiaccum, renyi, rrarefy, scores, scoverage,
showvarparts, simper, spandepth,
spantree, specaccum, specnumber, specpool2vect, specpool, spenvcor,
-stepacross, stressplot, swan, taxa2dist, taxondive, tolerance,
+stepacross, stressplot, swan, tabasco, taxa2dist, taxondive, tolerance,
treedist, treedive, treeheight, tsallisaccum, tsallis, varpart,
vectorfit, vegandocs, vegdist, vegemite, veiledspec, wascores,
wcmdscale, wisconsin)
Added: pkg/vegan/R/tabasco.R
===================================================================
--- pkg/vegan/R/tabasco.R (rev 0)
+++ pkg/vegan/R/tabasco.R 2013-02-11 18:34:53 UTC (rev 2431)
@@ -0,0 +1,81 @@
+### The function displays (ordered) heatmaps of community data. It
+### copies vegemite() for handling 'use', 'sp.ind', 'site.ind' and
+### 'select', but then switches to heatmap() to display the
+### data. Unlike heatmap(), it does not insist on showing dendrograms,
+### but only uses these for sites, and only if given as 'use'.
+
+`tabasco` <-
+ function (x, use, sp.ind = NULL, site.ind = NULL,
+ select, ...)
+{
+ Rowv <- Colv <- NA
+ if (!missing(use)) {
+ if (!is.list(use) && is.vector(use)) {
+ if (is.null(site.ind))
+ site.ind <- order(use)
+ if (is.null(sp.ind))
+ sp.ind <- order(wascores(use, x))
+ }
+ else if (inherits(use, "hclust")) {
+ if (is.null(site.ind))
+ site.ind <- use$order
+ if (is.null(sp.ind))
+ sp.ind <- order(wascores(order(site.ind), x))
+ Colv <- as.dendrogram(use)
+ }
+ else if (inherits(use, "dendrogram")) {
+ if (is.null(site.ind)) {
+ site.ind <- 1:nrow(x)
+ names(site.ind) <- rownames(x)
+ site.ind <- site.ind[labels(use)]
+ }
+ if (is.null(sp.ind))
+ sp.ind <- order(wascores(order(site.ind), x))
+ Colv <- use
+ }
+ else if (is.list(use)) {
+ tmp <- scores(use, choices = 1, display = "sites")
+ if (is.null(site.ind))
+ site.ind <- order(tmp)
+ if (is.null(sp.ind))
+ sp.ind <- try(order(scores(use, choices = 1,
+ display = "species")))
+ if (inherits(sp.ind, "try-error"))
+ sp.ind <- order(wascores(tmp, x))
+ }
+ else if (is.matrix(use)) {
+ tmp <- scores(use, choices = 1, display = "sites")
+ if (is.null(site.ind))
+ site.ind <- order(tmp)
+ if (is.null(sp.ind))
+ sp.ind <- order(wascores(tmp, x))
+ }
+ }
+ if (!is.null(sp.ind) && is.logical(sp.ind))
+ sp.ind <- (1:ncol(x))[sp.ind]
+ if (!is.null(site.ind) && is.logical(site.ind))
+ site.ind <- (1:nrow(x))[site.ind]
+ if (is.null(sp.ind))
+ sp.ind <- 1:ncol(x)
+ if (is.null(site.ind))
+ site.ind <- 1:nrow(x)
+ if (!missing(select)) {
+ if (!is.logical(select))
+ select <- sort(site.ind) %in% select
+ stake <- colSums(x[select, , drop = FALSE]) > 0
+ site.ind <- site.ind[select[site.ind]]
+ site.ind <- site.ind[!is.na(site.ind)]
+ }
+ else {
+ stake <- colSums(x[site.ind, ]) > 0
+ }
+ sp.ind <- sp.ind[stake[sp.ind]]
+ x <- x[site.ind, sp.ind]
+ x <- as.matrix(x)
+ x <- t(x)
+ sp.nam <- rownames(x)
+ sp.len <- max(nchar(sp.nam))
+ heatmap((max(x) - x), Rowv, Colv, scale = "none", ...)
+ out <- list(sites = site.ind, species = sp.ind)
+ invisible(out)
+}
Modified: pkg/vegan/inst/ChangeLog
===================================================================
--- pkg/vegan/inst/ChangeLog 2013-02-11 12:36:04 UTC (rev 2430)
+++ pkg/vegan/inst/ChangeLog 2013-02-11 18:34:53 UTC (rev 2431)
@@ -6,6 +6,9 @@
* New version opened with the release of vegan_2.0-6 on February
11, 2013.
+
+ * tabasco: a sister function of vegemite() to display a compact
+ community table using heatmap().
Version 2.1-25 (closed February 11, 2013)
Modified: pkg/vegan/man/vegemite.Rd
===================================================================
--- pkg/vegan/man/vegemite.Rd 2013-02-11 12:36:04 UTC (rev 2430)
+++ pkg/vegan/man/vegemite.Rd 2013-02-11 18:34:53 UTC (rev 2431)
@@ -1,16 +1,21 @@
\name{vegemite}
\alias{vegemite}
+\alias{tabasco}
\alias{coverscale}
-\title{Prints a Compact, Ordered Vegetation Table }
+\title{Display Compact Ordered Community Tables }
\description{
- The function prints a compact vegetation table, where species are
- rows, and each site takes only one column without spaces. The
- vegetation table can be ordered by explicit indexing, by environmental
- variables or results from an ordination or cluster analysis.
+ The functions display compact community tables. Function
+ \code{vegemite} prints text tables where species are rows, and each
+ site takes only one column without spaces. Function \code{tabasco}
+ provides interface for \code{\link{heatmap}} for a colour map of
+ the data. The community table can be ordered by explicit indexing,
+ by environmental variables or results from an ordination or cluster
+ analysis.
}
\usage{
vegemite(x, use, scale, sp.ind, site.ind, zero=".", select ,...)
+tabasco(x, use, sp.ind = NULL, site.ind = NULL, select, ...)
coverscale(x, scale=c("Braun.Blanquet", "Domin", "Hult", "Hill", "fix","log"),
maxabund)
}
@@ -32,19 +37,28 @@
\item{maxabund}{Maximum abundance used with \code{scale = "log"}.
Data maximum in the \code{select}ed subset will be used if this is
missing.}
- \item{...}{Arguments passed to \code{coverscale} (i.e., \code{maxabund}).}
+ \item{...}{Arguments passed to \code{coverscale} (i.e., \code{maxabund}) in
+ \code{vegemite} and to \code{\link{heatmap}} in \code{tabasco}.}
}
\details{
- The function prints a traditional vegetation table.
+ The function \code{vegemite} prints a traditional community table.
Unlike in ordinary data matrices, species are used as rows and sites
as columns. The table is printed in compact form: only one character
can be used for abundance, and there are no spaces between
columns. Species with no occurrences are dropped from the table.
+ Function \code{tabasco} produces a similar table as \code{vegemite}
+ using \code{\link{heatmap}}, where abundances are coded by heatmap
+ colours.
+
The parameter \code{use} can be a vector or an object from
\code{\link{hclust}}, a \code{\link{dendrogram}} or any ordination
result recognized by \code{\link{scores}} (all ordination methods in
- \pkg{vegan} and some of those not in \pkg{vegan}).
+ \pkg{vegan} and some of those not in \pkg{vegan}). The
+ \code{\link{hclust}} an \code{\link{dendrogram}} must be for
+ sites. The dendrogram is displayed in above the sites in
+ \code{tabasco}.
+
If \code{use} is a vector, it is used
for ordering sites. If \code{use} is an object from ordination, both
sites and species are arranged by the first axis.
@@ -106,15 +120,19 @@
}
\author{Jari Oksanen}
-\seealso{\code{\link{cut}} and \code{\link{approx}} for making your own
- `cover scales', \code{\link{wascores}} for weighted averages.
+\seealso{\code{\link{cut}} and \code{\link{approx}} for making your
+ own \sQuote{cover scales} for \code{vegemite}. Function
+ \code{tabasco} is based on \code{\link{heatmap}}. Both functions
+ order species with weighted averages using \code{\link{wascores}}.
}
-\note{ This function was called \code{vegetab} in older versions of
- \code{vegan}. The new name was chosen because the output is so
- compact (and to avoid confusion with the \code{vegtab} function in the
- \pkg{labdsv} package).
- }
+\note{
+
+ The name \code{vegemite} was chosen because the output is so
+ compact, and the \code{tabasco} because it is just as compact, but
+ uses heat colours.
+
+}
\examples{
data(varespec)
## Print only more common species
@@ -129,6 +147,15 @@
sel <- vegemite(varespec, use=dca, select = cl == 3, scale="Br")
# Re-create previous
vegemite(varespec, sp=sel$sp, site=sel$site, scale="Hult")
+
+## Abundance values have such a wide range that they must be rescaled
+## or all abundances will not be shown in tabasco
+tabasco(decostand(varespec, "log"), clus)
+## reorder dendrogram by the first DCA axis
+clus <- as.dendrogram(clus)
+clus <- reorder(clus, scores(dca, choices=1, display="sites"))
+tabasco(decostand(varespec, "log"), clus)
+
}
\keyword{ print }
\keyword{ manip }
Modified: pkg/vegan/tests/Examples/vegan-Ex.Rout.save
===================================================================
--- pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2013-02-11 12:36:04 UTC (rev 2430)
+++ pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2013-02-11 18:34:53 UTC (rev 2431)
@@ -1,8 +1,8 @@
-R Under development (unstable) (2013-01-21 r61719) -- "Unsuffered Consequences"
-Copyright (C) 2013 The R Foundation for Statistical Computing
+R version 2.15.2 (2012-10-26) -- "Trick or Treat"
+Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
-Platform: x86_64-unknown-linux-gnu (64-bit)
+Platform: x86_64-apple-darwin9.8.0/x86_64 (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 2.1-23
+This is vegan 2.1-26
>
> assign(".oldSearch", search(), pos = 'CheckExEnv')
> cleanEx()
@@ -161,7 +161,7 @@
Formula:
y ~ poly(x1, 1) + poly(x2, 1)
-<environment: 0x2e94a50>
+<environment: 0x10245b5b8>
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.
@@ -668,10 +668,6 @@
> 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)
@@ -679,7 +675,7 @@
Run 1 stress 0.1560544
Run 2 stress 0.08556586
... New best solution
-... procrustes: rmse 1.094382e-06 max resid 1.88838e-06
+... procrustes: rmse 1.094365e-06 max resid 1.88838e-06
*** Solution reached
> plot(mod)
> ### Hulls show treatment
@@ -701,7 +697,7 @@
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)
@@ -716,7 +712,7 @@
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
>
>
>
@@ -800,7 +796,7 @@
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)
@@ -810,7 +806,7 @@
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
@@ -823,7 +819,7 @@
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
@@ -836,7 +832,7 @@
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
@@ -847,7 +843,7 @@
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
>
>
>
@@ -1141,7 +1137,7 @@
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)
@@ -1162,7 +1158,7 @@
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)
@@ -1290,7 +1286,7 @@
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
@@ -1299,7 +1295,7 @@
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))
@@ -1563,7 +1559,7 @@
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)
@@ -2286,7 +2282,7 @@
Pearson's Chi-squared test
-data: dune
+data: dune
X-squared = 1448.956, df = 551, p-value < 2.2e-16
> deviance(cca(dune))
@@ -2641,7 +2637,7 @@
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.
@@ -3503,7 +3499,7 @@
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)
@@ -3542,7 +3538,7 @@
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.
@@ -3633,7 +3629,7 @@
Run 0 stress 0.1067169
Run 1 stress 0.1067169
... New best solution
-... procrustes: rmse 1.234853e-05 max resid 2.993582e-05
+... procrustes: rmse 1.234853e-05 max resid 2.993581e-05
*** Solution reached
> sol
@@ -3837,7 +3833,7 @@
> plot(dune.ord <- metaMDS(dune), type="text", display="sites" )
Run 0 stress 0.1192678
Run 1 stress 0.119268
-... procrustes: rmse 8.185687e-05 max resid 0.0001982896
+... procrustes: rmse 8.18569e-05 max resid 0.0001982896
*** Solution reached
> ordihull(dune.ord, dune.env$Management)
>
@@ -4039,7 +4035,7 @@
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
@@ -4059,7 +4055,7 @@
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
@@ -4079,7 +4075,7 @@
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
@@ -4099,7 +4095,7 @@
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
>
>
>
@@ -4161,7 +4157,7 @@
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
>
>
>
@@ -4289,7 +4285,7 @@
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")
@@ -4322,7 +4318,7 @@
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))
@@ -4348,7 +4344,7 @@
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
@@ -4372,7 +4368,7 @@
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
>
>
>
@@ -4650,14 +4646,14 @@
+ 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 **
@@ -4665,7 +4661,7 @@
+ 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
@@ -4673,7 +4669,7 @@
- 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
@@ -4726,7 +4722,7 @@
- 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
@@ -4734,7 +4730,7 @@
- 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)
@@ -4784,7 +4780,7 @@
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
@@ -4801,7 +4797,7 @@
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
@@ -4818,7 +4814,7 @@
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
@@ -4835,7 +4831,7 @@
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
@@ -4852,7 +4848,7 @@
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
@@ -4875,7 +4871,7 @@
+ 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:
@@ -4912,7 +4908,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x9b271c0>
+<environment: 0x10ac51d18>
Estimated degrees of freedom:
6.45 total = 7.45
@@ -4928,7 +4924,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0xa13f470>
+<environment: 0x109d00ed8>
Estimated degrees of freedom:
6.12 total = 7.12
@@ -4940,13 +4936,13 @@
> ## Get fitted values
> calibrate(fit)
1 2 3 4 5 6 7
-22.0596536 6.0185658 3.6298560 4.1000950 8.9833602 5.9067474 8.6617386
+22.0596535 6.0185659 3.6298559 4.1000950 8.9833600 5.9067472 8.6617389
8 9 10 11 12 13 14
-11.0812151 0.6432692 35.2567122 10.4452454 7.2748480 5.5780162 24.6561684
+11.0812152 0.6432691 35.2567124 10.4452454 7.2748478 5.5780162 24.6561685
15 16 17 18 19 20 21
-18.8879904 29.7642960 5.6095921 9.5945523 3.2753635 2.6966144 10.7869350
+18.8879906 29.7642964 5.6095920 9.5945524 3.2753633 2.6966143 10.7869351
22 23 24
- 2.9902833 9.8082238 7.3406584
+ 2.9902832 9.8082237 7.3406581
>
> ## Plot method
> plot(fit, what = "contour")
@@ -5063,10 +5059,6 @@
> 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
>
>
>
@@ -5100,7 +5092,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0xa1184a0>
+<environment: 0x109cb80e8>
Estimated degrees of freedom:
8.93 total = 9.93
@@ -5113,7 +5105,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x9b48ba0>
+<environment: 0x10adbb588>
Estimated degrees of freedom:
7.75 total = 8.75
@@ -5126,7 +5118,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x9dbf4a8>
+<environment: 0x108d98b90>
Estimated degrees of freedom:
8.9 total = 9.9
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/vegan -r 2431
More information about the Vegan-commits
mailing list