[Vegan-commits] r415 - in pkg: R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Mon Jun 9 21:41:31 CEST 2008
Author: jarioksa
Date: 2008-06-09 21:41:31 +0200 (Mon, 09 Jun 2008)
New Revision: 415
Modified:
pkg/R/adonis.R
pkg/man/adonis.Rd
Log:
merged r411 from branches/1.13 to pkg/: speedup and cod updates in adonis
Modified: pkg/R/adonis.R
===================================================================
--- pkg/R/adonis.R 2008-06-09 19:33:55 UTC (rev 414)
+++ pkg/R/adonis.R 2008-06-09 19:41:31 UTC (rev 415)
@@ -1,5 +1,5 @@
`adonis` <-
- function(formula, data, permutations=5, method="bray", strata=NULL,
+ function(formula, data=NULL, permutations=5, method="bray", strata=NULL,
contr.unordered="contr.sum", contr.ordered="contr.poly",
...)
{
@@ -7,6 +7,7 @@
## frame or a matrix, and A, B, and C may be factors or continuous
## variables. data is the data frame from which A, B, and C would
## be drawn.
+ TOL <- 1e-7
lhs <- formula[[2]]
lhs <- eval(lhs, data, parent.frame()) # to force evaluation
formula[[2]] <- NULL # to remove the lhs
@@ -25,7 +26,7 @@
nterms <- length(u.grps) - 1
H.s <- lapply(2:length(u.grps),
function(j) {Xj <- rhs[, grps %in% u.grps[1:j] ]
- qrX <- qr(Xj, tol=1e-7)
+ qrX <- qr(Xj, tol=TOL)
Q <- qr.Q(qrX)
tcrossprod(Q[,1:qrX$rank])
})
@@ -40,13 +41,13 @@
ones <- matrix(1,nrow=n)
A <- -(dmat)/2
G <- -.5 * dmat %*% (I - ones%*%t(ones)/n)
- SS.Exp.comb <- sapply(H.s, function(hat) sum( diag(G %*% hat) ) )
+ SS.Exp.comb <- sapply(H.s, function(hat) sum( G * t(hat)) )
SS.Exp.each <- c(SS.Exp.comb - c(0,SS.Exp.comb[-nterms]) )
H.snterm <- H.s[[nterms]]
if (length(H.s) > 1)
for (i in length(H.s):2)
H.s[[i]] <- H.s[[i]] - H.s[[i-1]]
- SS.Res <- sum(diag( ( G %*% (I-H.snterm))))
+ SS.Res <- sum( G * t(I-H.snterm))
df.Exp <- sapply(u.grps[-1], function(i) sum(grps==i) )
df.Res <- n - qrhs$rank
## Get coefficients both for the species (if possible) and sites
@@ -62,12 +63,13 @@
F.Mod <- (SS.Exp.each/df.Exp) / (SS.Res/df.Res)
f.test <- function(H, G, I, df.Exp, df.Res, H.snterm){
- (sum( diag(G %*% H) )/df.Exp) /
- ( sum(diag( G %*% (I-H.snterm) ))/df.Res) }
+ (sum( G * t(H) )/df.Exp) /
+ (sum( G * t(I-H.snterm) )/df.Res) }
SS.perms <- function(H, G, I){
- c(SS.Exp.p = sum( diag(G%*%H) ),
- S.Res.p=sum(diag( G %*% (I-H) )) ) }
+ c(SS.Exp.p = sum( G * t(H) ),
+ S.Res.p=sum( G * t(I-H) )
+ ) }
## Permutations
if (missing(strata))
@@ -83,6 +85,7 @@
f.test(H.s[[i]], G.p[[j]], I, df.Exp[i], df.Res, H.snterm)
} )
})
+
SumsOfSqs = c(SS.Exp.each, SS.Res, sum(SS.Exp.each) + SS.Res)
tab <- data.frame(Df = c(df.Exp, df.Res, n-1),
SumsOfSqs = SumsOfSqs,
Modified: pkg/man/adonis.Rd
===================================================================
--- pkg/man/adonis.Rd 2008-06-09 19:33:55 UTC (rev 414)
+++ pkg/man/adonis.Rd 2008-06-09 19:41:31 UTC (rev 415)
@@ -3,12 +3,12 @@
\alias{adonis}
\alias{print.adonis}
-\title{Multivariate Analysis of Variance Using Distance Matrices}
+\title{Permutational Multivariate Analysis of Variance Using Distance Matrices}
\description{Analysis of variance using distance matrices --- for
partitioning distance matrices among sources of variation and fitting
linear models (e.g., factors, polynomial regression) to distance
- matrices.}
+ matrices; uses a permutation test with pseudo-F ratios.}
\usage{
adonis(formula, data, permutations = 5, method = "bray",
@@ -20,7 +20,9 @@
\item{formula}{a typical model formula such as \code{Y ~ A + B*C}, but
where \code{Y} is either a dissimilarity object (inheriting from
class \code{"dist"}) or data frame or a matrix; \code{A}, \code{B}, and
- \code{C} may be factors or continuous variables. }
+ \code{C} may be factors or continuous variables. If a dissimilarity
+ object is supplied, no species cofficients can be calculated (see
+ Value below).}
\item{data}{ the data frame from which \code{A}, \code{B}, and
\code{C} would be drawn.}
\item{permutations}{ number of replicate permutations used for the
@@ -39,8 +41,8 @@
sums of squares using semimetric and metric distance matrices. Insofar
as it partitions sums of squares of a multivariate data set, it is
directly analogous to MANOVA (multivariate analysis of
-variance). McArdle and Anderson (2001) and Anderson (2001) refer to the
-method as \dQuote{nonparametric manova}. Further, as its inputs are
+variance). M.J. Anderson (McArdle and Anderson 2001, Anderson 2001) refers to the
+method as \dQuote{permutational manova} (formerly \dQuote{nonparametric manova}). Further, as its inputs are
linear predictors, and a response matrix of an arbitrary number of
columns (2 to millions), it is a robust alternative to both parametric
MANOVA and to ordination methods for describing how variation is
@@ -75,10 +77,9 @@
as the first method.
Significance tests are done using \eqn{F}-tests based on sequential sums
-of squares from permutations of the raw data. Additional work should be
-done to validate these methods; preliminary work suggests substantive
-differences between permutations of the raw data versus permutations of
-the residuals. Further, the precise meaning of hypothesis tests will
+of squares from permutations of the raw data, and not permutations of
+residuals. Permutations of the raw data may have better small sample
+characteristics. Further, the precise meaning of hypothesis tests will
depend upon precisely what is permuted. The strata argument keeps groups
intact for a particular hypothesis test where one does not want to
permute the data among particular groups. For instance, \code{strata =
@@ -88,7 +89,8 @@
The default \code{\link{contrasts}} are different than in \R in
general. Specifically, they use \dQuote{sum} contrasts, sometimes known
as \dQuote{ANOVA} contrasts. See a useful text (e.g. Crawley,
-2002) for a transparent introduction. This is simply a personal
+2002) for a transparent introduction to linear model contrasts. This
+choice of contrasts is simply a personal
pedagogical preference. The particular contrasts can be set to any
\code{\link{contrasts}} specified in \R, including Helmert and treatment
contrasts.
@@ -102,13 +104,22 @@
This function returns typical, but limited, output for analysis of
variance (general linear models).
- \item{aov.tab }{Typical AOV table showing sources of variation,
+ \item{aov.tab}{Typical AOV table showing sources of variation,
degrees of freedom, sequential sums of squares, mean squares,
\eqn{F} statistics, partial R-squared and \eqn{P} values, based on \eqn{N}
permutations. }
- \item{coefficients }{ matrix of coefficients of the linear model, with
+ \item{coefficients}{ matrix of coefficients of the linear model, with
rows representing sources of variation and columns representing
- species. }
+ species; each column represents a fit of a species abundance to the
+ linear model. These are what you get when you fit one species to
+ your predictors. These are NOT available if you supply the distance
+ matrix in the formula, rather than the site x species matrix}
+ \item{coef.sites}{ matrix of coefficients of the linear model, with
+ rows representing sources of variation and columns representing
+ sites; each column represents a fit of a sites distances (from all
+ other sites) to the linear model.These are what you get when you
+ fit distances of one site to
+ your predictors. }
\item{f.perms}{ an \eqn{N} by \eqn{m} matrix of the null \eqn{F}
statistics for each source of variation based on \eqn{N}
permutations of the data.}
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