[Vegan-commits] r1111 - branches/1.17/man

[noreply at r-forge.r-project.org]

Author: jarioksa
Date: 2010-01-08 19:17:00 +0100 (Fri, 08 Jan 2010)
New Revision: 1111

Removed:
   branches/1.17/man/multipart.Rd
Log:
remove multipart.Rd after removing multpart.R

Deleted: branches/1.17/man/multipart.Rd
===================================================================
--- branches/1.17/man/multipart.Rd	2010-01-08 16:44:52 UTC (rev 1110)
+++ branches/1.17/man/multipart.Rd	2010-01-08 18:17:00 UTC (rev 1111)
@@ -1,107 +0,0 @@
-\encoding{UTF-8}
-\name{multipart}
-\alias{multipart}
-\alias{print.multipart}
-\title{Multiplicative Diversity Partitioning}
-\description{
-In multiplicative diversity partitioning, mean values of alpha diversity at lower levels of a sampling 
-hierarchy are compared to the total diversity in the entire data set or the pooled samples (gamma diversity). 
-}
-\usage{
-multipart(formula, data, index=c("renyi", "tsallis"), scales = 1,
-    global = FALSE, relative = FALSE, nsimul=99, ...)
-\method{print}{multipart}(x, ...)
-}
-\arguments{
-  \item{formula}{A two sided model formula in the form \code{y ~ x}, where \code{y} 
-    is the community data matrix with samples as rows and species as column. Right 
-    hand side (\code{x}) must contain factors referring to levels of sampling hierarchy, 
-    terms from right to left will be treated as nested (first column is the lowest, 
-    last is the highest level). These variables must be factors in order to unambiguous 
-    handling. Interaction terms are not allowed.}
-  \item{data}{A data frame where to look for variables defined in the right hand side 
-    of \code{formula}. If missing, variables are looked in the global environment.}
-  \item{index}{Character, the entropy index to be calculated (see Details).}
-  \item{relative}{Logical, if \code{TRUE} then beta diversity is
-    standardized by its maximum (see Details).}
-  \item{scales}{Numeric, of length 1, the order of the generalized diversity index 
-    to be used.}
-  \item{global}{Logical, indicates the calculation of beta diversity values, see Details.}
-  \item{nsimul}{Number of permutation to use if \code{matr} is not of class 'permat'.
-    If \code{nsimul = 0}, only the \code{FUN} argument is evaluated. It is thus possible
-    to reuse the statistic values without using a null model.}
-  \item{x}{An object to print.}
-  \item{\dots}{Other arguments passed to \code{\link{oecosimu}}, i.e. 
-    \code{method}, \code{thin} or \code{burnin}.}
-}
-\details{
-Multiplicative diversity partitioning is based on Whittaker's (1972) ideas, that has 
-recently been generalised to one parametric diversity families (i.e. \enc{R\'enyi}{Renyi} 
-and Tsallis) by Jost (2006, 2007). Jost recommends to use the numbers equivalents 
-(Hill numbers), instead of pure diversities, and proofs, that this satisfies the 
-multiplicative partitioning requirements.
-
-The current implementation of \code{multipart} calculates Hill numbers based on the 
-functions \code{\link{renyi}} and \code{\link{tsallis}} (provided as \code{index} argument). 
-If values for more than one \code{scales} are desired, it should be done in separate 
-runs, because it adds extra dimensionality to the implementation, which has not been resolved 
-efficiently.
-
-Alpha diversities are then the averages of these Hill numbers for each hierarchy levels, 
-the global gamma diversity is the alpha value calculated for the highest hierarchy level. 
-When \code{global = TRUE}, beta is calculated relative to the global gamma value:
-\deqn{\beta_i = \gamma / \alpha_{i}}{beta_i = gamma / alpha_i}
-when \code{global = FALSE}, beta is calculated relative to local gamma values (local gamma
-means the diversity calculated for a particular cluster based on the pooled abundance vector):
-\deqn{\beta_ij = \alpha_{(i+1)j} / mean(\alpha_{ij})}{beta_ij = alpha_(i+1)j / mean(alpha_i)}
-where \eqn{j} is a particular cluster at hierarchy level \eqn{i}. Then beta diversity value for
-level \eqn{i} is the mean of the beta values of the clusters at that level,
-\eqn{\beta_{i} = mean(\beta_{ij})}.
-
-If \code{relative = TRUE}, the respective beta diversity values are
-standardized by their maximum expected values (\eqn{mean(\beta_{ij}) / \beta_{max,ij}})
-given as \eqn{\beta_{max,ij} = n_{j}} (the number of lower level units in a given cluster \eqn{j}).
-
-The expected diversity components are calculated \code{nsimul} times by individual based 
-randomisation of the community data matrix. This is done by the \code{"r2dtable"} method
-in \code{\link{oecosimu}} by default.
-}
-\value{
-An object of class 'multipart' with same structure as 'oecosimu' objects.
-}
-\references{
-Jost, L. (2006). Entropy and diversity.
-\emph{Oikos}, \bold{113}, 363--375.
-
-Jost, L. (2007). Partitioning diversity into independent alpha and beta components.
-\emph{Ecology}, \bold{88}, 2427--2439.
-
-Whittaker, R. (1972). Evolution and measurement of species diversity.
-\emph{Taxon}, \bold{21}, 213--251.
-}
-\author{\enc{P\'eter S\'olymos}{Peter Solymos}, \email{solymos at ualberta.ca}}
-\seealso{See \code{\link{adipart}} for additive diversity partitioning,
-  \code{\link{hiersimu}} for hierarchical null model testing
-  and \code{\link{oecosimu}} for permutation settings and calculating \eqn{p}-values.}
-\examples{
-data(mite)
-data(mite.xy)
-data(mite.env)
-## Function to get equal area partitions of the mite data
-cutter <- function (x, cut = seq(0, 10, by = 2.5)) {
-    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))}
-## The hierarchy of sample aggregation
-levsm <- data.frame(
-    l1=as.factor(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=25)
-multipart(mite ~ ., levsm, index="renyi", scales=1, nsimul=25, relative=TRUE)
-multipart(mite ~ ., levsm, index="renyi", scales=1, nsimul=25, global=TRUE)
-}
-\keyword{multivariate}