[Vegan-commits] r228 - pkg/man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Sun Feb 17 15:33:46 CET 2008
Author: gsimpson
Date: 2008-02-17 15:33:46 +0100 (Sun, 17 Feb 2008)
New Revision: 228
Modified:
pkg/man/deviance.cca.Rd
pkg/man/distconnected.Rd
pkg/man/diversity.Rd
pkg/man/dune.Rd
pkg/man/dune.taxon.Rd
Log:
More documentation tweaks
Modified: pkg/man/deviance.cca.Rd
===================================================================
--- pkg/man/deviance.cca.Rd 2008-02-17 13:55:32 UTC (rev 227)
+++ pkg/man/deviance.cca.Rd 2008-02-17 14:33:46 UTC (rev 228)
@@ -16,6 +16,7 @@
}
\usage{
\method{deviance}{cca}(object, ...)
+
\method{extractAIC}{cca}(fit, scale = 0, k = 2, ...)
}
@@ -32,35 +33,32 @@
\details{
The functions find statistics that
resemble \code{\link{deviance}} and \code{\link{AIC}} in constrained
- ordination. Actually,
- constrained ordination methods do not have log-Likelihood, which means
- that they cannot have AIC and deviance. Therefore you should not use
- these functions, and if you use them, you should not trust them. If
- you use these functions, it remains as your responsibility to check
- the adequacy of the result.
+ ordination. Actually, constrained ordination methods do not have a
+ log-Likelihood, which means that they cannot have AIC and deviance.
+ Therefore you should not use these functions, and if you use them, you
+ should not trust them. If you use these functions, it remains as your
+ responsibility to check the adequacy of the result.
- The deviance of \code{\link{cca}} is equal to Chi-square of
- the residual data matrix after fitting the constraints. The deviance of
- \code{\link{rda}} is defined as the residual sum of squares.
- The deviance function of \code{rda} is also used for
- \code{\link{capscale}}.
- Function \code{extractAIC} mimics
+ The deviance of \code{\link{cca}} is equal to the Chi-square of
+ the residual data matrix after fitting the constraints. The deviance
+ of \code{\link{rda}} is defined as the residual sum of squares. The
+ deviance function of \code{rda} is also used for
+ \code{\link{capscale}}. Function \code{extractAIC} mimics
\code{extractAIC.lm} in translating deviance to AIC.
-
+
There is little need to call these functions directly. However, they
are called implicitly in \code{\link{step}} function used in automatic
- selection of constraining variables. You should check the
- resulting model with some other criteria, because the statistics used
- here are unfounded. In particular, the penalty \code{k} is not properly
+ selection of constraining variables. You should check the resulting
+ model with some other criteria, because the statistics used here are
+ unfounded. In particular, the penalty \code{k} is not properly
defined, and the default \code{k = 2} is not justified
theoretically. If you have only continuous covariates, the \code{step}
function will base the model building on magnitude of eigenvalues, and
- the value of \code{k} only influences the stopping point (but
- variable with highest eigenvalues is not necessarily the most
- significant one in permutation
- tests in \code{\link{anova.cca}}). If you also
- have multi-class factors, the value of \code{k} will have a
- capricious effect in model building.
+ the value of \code{k} only influences the stopping point (but the
+ variables with the highest eigenvalues are not necessarily the most
+ significant in permutation tests in \code{\link{anova.cca}}). If you
+ also have multi-class factors, the value of \code{k} will have a
+ capricious effect in model building.
}
\value{
@@ -68,9 +66,10 @@
\code{extractAIC} returns effective degrees of freedom and ``AIC''.
}
\references{
- \enc{Godínez-Domínguez}{Godinez-Dominguez}, E. & Freire, J. (2003) Information-theoretic
- approach for selection of spatial and temporal models of community
- organization. \emph{Marine Ecology Progress Series} 253, 17--24.
+ \enc{Godínez-Domínguez}{Godinez-Dominguez}, E. & Freire, J. (2003)
+ Information-theoretic approach for selection of spatial and temporal
+ models of community organization. \emph{Marine Ecology Progress
+ Series} \strong{253}, 17--24.
}
\author{ Jari Oksanen }
@@ -95,7 +94,8 @@
# Stepwise selection (forward from an empty model "dune ~ 1")
step(cca(dune ~ 1, dune.env), scope = formula(ord))
# ANOVA: added variable + the first left out
-anova(cca(dune ~ Moisture + Management, dune.env), permut=200, by = "terms")
+anova(cca(dune ~ Moisture + Management, dune.env), permut=200,
+ by = "terms")
}
\keyword{ multivariate }
\keyword{ models }
Modified: pkg/man/distconnected.Rd
===================================================================
--- pkg/man/distconnected.Rd 2008-02-17 13:55:32 UTC (rev 227)
+++ pkg/man/distconnected.Rd 2008-02-17 14:33:46 UTC (rev 228)
@@ -2,7 +2,7 @@
\alias{distconnected}
\alias{no.shared}
-\title{Connectedness of Dissimilarities }
+\title{Connectedness of Dissimilarities}
\description{
Function \code{distconnected} finds groups that are connected
disregarding dissimilarities that are at or above a threshold or
@@ -16,6 +16,7 @@
}
\usage{
distconnected(dis, toolong = 1, trace = TRUE)
+
no.shared(x)
}
@@ -28,7 +29,7 @@
\item{toolong}{ Shortest dissimilarity regarded as \code{NA}.
The function uses a fuzz factor, so
that dissimilarities close to the limit will be made \code{NA}, too.
- If \code{toolong = 0} (or negative), no dissimmilarity is regarded
+ If \code{toolong = 0} (or negative), no dissimilarity is regarded
as too long.
}
\item{trace}{Summarize results of \code{distconnected}}
@@ -38,18 +39,17 @@
\details{
Data sets are disconnected if they have sample plots or groups of
sample plots which share no species with other sites or groups of
- sites. Such data sets
- cannot be sensibly ordinated by any unconstrained method, because
- these subsets cannot be related to each other. For instance,
- correspondence analysis will polarize these subsets with eigenvalue
- 1. Neither can such dissimilarities be transformed with
- \code{\link{stepacross}}, because there is no path between all points,
- and result will contain \code{NA}s. Function \code{distconnected} will
- find such subsets in dissimilarity matrices. The function will return
- a grouping vector that can be used for subsetting the
- data. If data are connected, the result vector will be all
- \eqn{1}s. The connectedness between two points can be defined either
- by a threshold \code{toolong} or using input dissimilarities
+ sites. Such data sets cannot be sensibly ordinated by any
+ unconstrained method because these subsets cannot be related to each
+ other. For instance, correspondence analysis will polarize these
+ subsets with eigenvalue 1. Neither can such dissimilarities be
+ transformed with \code{\link{stepacross}}, because there is no path
+ between all points, and result will contain \code{NA}s. Function
+ \code{distconnected} will find such subsets in dissimilarity
+ matrices. The function will return a grouping vector that can be used
+ for sub-setting the data. If data are connected, the result vector will
+ be all \eqn{1}s. The connectedness between two points can be defined
+ either by a threshold \code{toolong} or using input dissimilarities
with \code{NA}s.
Function \code{no.shared} returns a \code{dist} structure having value
Modified: pkg/man/diversity.Rd
===================================================================
--- pkg/man/diversity.Rd 2008-02-17 13:55:32 UTC (rev 227)
+++ pkg/man/diversity.Rd 2008-02-17 14:33:46 UTC (rev 228)
@@ -12,15 +12,18 @@
\usage{
diversity(x, index = "shannon", MARGIN = 1, base = exp(1))
+
rarefy(x, sample, se = FALSE, MARGIN = 1)
+
fisher.alpha(x, MARGIN = 1, se = FALSE, ...)
+
specnumber(x, MARGIN = 1)
}
\arguments{
- \item{x}{Community data matrix.}
- \item{index}{Diversity index, one of \code{shannon}, \code{simpson} or
- \code{invsimpson}.}
+ \item{x}{Community data, a matrix-like object.}
+ \item{index}{Diversity index, one of \code{"shannon"},
+ \code{"simpson"} or \code{"invsimpson"}.}
\item{MARGIN}{Margin for which the index is computed. }
\item{base}{ The logarithm \code{base} used in \code{shannon}.}
\item{sample}{Subsample size for rarefying community.}
@@ -32,8 +35,8 @@
\eqn{H' = -\sum_i p_i \log_{b} p_i}{H = -sum p_i log(b) p_i}, where
\eqn{p_i} is the proportional abundance of species \eqn{i} and \eqn{b}
is the base of the logarithm. It is most popular to use natural
- logarithms, but some argue for base \eqn{b = 2} (which makes sense, but no
- real difference).
+ logarithms, but some argue for base \eqn{b = 2} (which makes sense,
+ but no real difference).
Both variants of Simpson's index are based on \eqn{D = \sum p_i^2}{D =
sum p_i^2}. Choice \code{simpson} returns \eqn{1-D} and
@@ -48,15 +51,15 @@
The function \code{rarefy} is based on Hurlbert's (1971) formulation,
and the standard errors on Heck et al. (1975).
- Function \code{fisher.alpha} estimates the \eqn{\alpha} parameter of
+ \code{fisher.alpha} estimates the \eqn{\alpha} parameter of
Fisher's logarithmic series (see \code{\link{fisherfit}}).
The estimation is possible only for genuine
counts of individuals. The function can optionally return standard
errors of \eqn{\alpha}. These should be regarded only as rough
indicators of the accuracy: the confidence limits of \eqn{\alpha} are
- strongly
- non-symmetric and standard errors cannot be used in Normal inference.
-
+ strongly non-symmetric and the standard errors cannot be used in
+ Normal inference.
+
Function \code{specnumber} finds the number of species. With
\code{MARGIN = 2}, it finds frequencies of species. The function is
extremely simple, and shortcuts are easy in plain \R.
@@ -64,18 +67,18 @@
Better stories can be told about Simpson's index than about
Shannon's index, and still grander narratives about
rarefaction (Hurlbert 1971). However, these indices are all very
- closely related (Hill 1973), and there is no reason to despise one more than
- others (but if you are a graduate student, don't drag me in, but obey
- your Professor's orders). In particular, exponent of the Shannon
- index is linearly related to inverse Simpson (Hill 1973) although the
- former may be more sensitive to rare species. Moreover, inverse
- Simpson is asymptotically equal to rarefied species richness in sample
- of two individuals, and Fisher's \eqn{\alpha} is very similar to
- inverse Simpson.
+ closely related (Hill 1973), and there is no reason to despise one
+ more than others (but if you are a graduate student, don't drag me in,
+ but obey your Professor's orders). In particular, the exponent of the
+ Shannon index is linearly related to inverse Simpson (Hill 1973)
+ although the former may be more sensitive to rare species. Moreover,
+ inverse Simpson is asymptotically equal to rarefied species richness
+ in sample of two individuals, and Fisher's \eqn{\alpha} is very
+ similar to inverse Simpson.
}
\value{
- Vector of diversity indices or rarefied species richness values. With
+ A vector of diversity indices or rarefied species richness values. With
option \code{se = TRUE}, function \code{rarefy} returns a 2-row matrix
with rarefied richness (\code{S}) and its standard error
(\code{se}).
@@ -90,15 +93,15 @@
Fisher, R.A., Corbet, A.S. & Williams, C.B. (1943). The relation
between the number of species and the number of individuals in a
random sample of animal population. \emph{Journal of Animal Ecology}
- 12, 42--58.
+ \strong{12}, 42--58.
Heck, K.L., van Belle, G. & Simberloff, D. (1975). Explicit
calculation of the rarefaction diversity measurement and the
- determination of sufficient sample size. \emph{Ecology} 56,
+ determination of sufficient sample size. \emph{Ecology} \strong{56},
1459--1461.
Hurlbert, S.H. (1971). The nonconcept of species diversity: a critique
- and alternative parameters. \emph{Ecology} 52, 577--586.
+ and alternative parameters. \emph{Ecology} \strong{52}, 577--586.
}
@@ -106,7 +109,7 @@
diversity and Hill numbers.}
\author{ Jari Oksanen and Bob O'Hara \email{bob.ohara at helsinki.fi}
- (\code{fisher.alpha}). }
+ (\code{fisher.alpha}).}
\examples{
data(BCI)
Modified: pkg/man/dune.Rd
===================================================================
--- pkg/man/dune.Rd 2008-02-17 13:55:32 UTC (rev 227)
+++ pkg/man/dune.Rd 2008-02-17 14:33:46 UTC (rev 228)
@@ -8,32 +8,44 @@
data(dune.env)
}
\description{
-The dune meadow vegetation data \code{dune} has cover class values of 30
-species on 20 sites. The corresponding environmental data frame
-\code{dune.env} has following entries:
+ The dune meadow vegetation data, \code{dune}, has cover class values
+ of 30 species on 20 sites. The corresponding environmental data frame
+ \code{dune.env} has following entries:
}
\format{
+ For \code{dune}, a data frame of observations of 30 species at 20
+ sites.
+
+ For \code{dune.env}, a data frame of 20 observations on the following
+ 5 variables:
\describe{
- \item{A1}{a numeric vector of thickness of A1 horizon.}
- \item{Moisture}{an ordered factor with levels}
- \item{Moisture}{\code{1} < \code{2} < \code{4} < \code{5}}
- \item{Management}{a factor with levels}
- \item{Management}{\code{BF}: Biological Farming }
- \item{Management}{\code{HF}: Hobby Farming }
- \item{Management}{\code{NM}: Nature Conservation Management }
- \item{Management}{\code{SF}: Standard Farming }
- \item{Use}{an ordered factor of landuse with levels}
- \item{Use}{\code{Hayfield} < \code{Haypastu} < \code{Pasture}}
- \item{Manure}{an ordered factor with levels}
- \item{Manure}{\code{0} < \code{1} < \code{2} < \code{3} < \code{4}}
+ \item{A1:}{a numeric vector of thickness of soil A1 horizon.}
+ \item{Moisture:}{an ordered factor with levels: \code{1} < \code{2} <
+ \code{4} < \code{5}.}
+ %\item{Moisture}{\code{1} < \code{2} < \code{4} < \code{5}}
+ \item{Management:}{a factor with levels: \code{BF} (Biological
+ farming), \code{HF} (Hobby farming), \code{NM} (Nature
+ Conservation Management), and \code{SF} (Standard Farming).}
+ %\item{Management}{\code{BF}: Biological Farming }
+ %\item{Management}{\code{HF}: Hobby Farming }
+ %\item{Management}{\code{NM}: Nature Conservation Management }
+ %\item{Management}{\code{SF}: Standard Farming }
+ \item{Use:}{an ordered factor of land-use with levels: \code{Hayfield}
+ < \code{Haypastu} < \code{Pasture}.}
+ %\item{Use}{\code{Hayfield} < \code{Haypastu} < \code{Pasture}}
+ \item{Manure:}{an ordered factor with levels: \code{0} < \code{1} <
+ \code{2} < \code{3} < \code{4}.}
+ %\item{Manure}{\code{0} < \code{1} < \code{2} < \code{3} < \code{4}}
}
}
\source{
Jongman, R.H.G, ter Braak, C.J.F & van Tongeren,
O.F.R. (1987). \emph{Data Analysis in Community and Landscape
- Ecology}. Pudog, Wageningen.
+ Ecology}. Pudoc, Wageningen.
}
\examples{
data(dune)
+
+data(dune.env)
}
\keyword{datasets}
Modified: pkg/man/dune.taxon.Rd
===================================================================
--- pkg/man/dune.taxon.Rd 2008-02-17 13:55:32 UTC (rev 227)
+++ pkg/man/dune.taxon.Rd 2008-02-17 14:33:46 UTC (rev 228)
@@ -3,7 +3,8 @@
\docType{data}
\title{Taxonomic Classification of Dune Meadow Species}
\description{
- Classification table of species of \code{\link{dune}} data.
+ Classification table of the species in the \code{\link{dune}} data
+ set.
}
\usage{data(dune.taxon)}
\format{
@@ -15,17 +16,17 @@
that of mosses from Hill et al. (2006).
}
\references{
-AGP [Angiosperm Phylogeny Group] (2003) An update of the Angiosperm
-Phylogeny Group classification for the orders and families of flowering
-plants: AGP II. \emph{Bot. J. Linnean Soc.} 141, 399--436.
+ AGP [Angiosperm Phylogeny Group] (2003) An update of the Angiosperm
+ Phylogeny Group classification for the orders and families of flowering
+ plants: AGP II. \emph{Bot. J. Linnean Soc.} \strong{141}: 399--436.
-Hill, M.O et al. (2006) An annotatated checklist of the mosses of Europe
-and Macaronesia. \emph{J. Bryology} 28: 198--267.
+ Hill, M.O et al. (2006) An annotated checklist of the mosses of Europe
+ and Macaronesia. \emph{J. Bryology} \strong{28}: 198--267.
}
\note{
The data set was made to demonstrate \code{\link{taxondive}}, and will
probably be removed after a better example is found.
- }
+}
\examples{
data(dune.taxon)
}
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