[Vegan-commits] r229 - branches/1.11-0/man

noreply at r-forge.r-project.org noreply at r-forge.r-project.org
Sun Feb 17 15:36:01 CET 2008


Author: gsimpson
Date: 2008-02-17 15:36:01 +0100 (Sun, 17 Feb 2008)
New Revision: 229

Modified:
   branches/1.11-0/man/deviance.cca.Rd
   branches/1.11-0/man/distconnected.Rd
   branches/1.11-0/man/diversity.Rd
   branches/1.11-0/man/dune.Rd
   branches/1.11-0/man/dune.taxon.Rd
Log:
Merging r228 documentation tweaks from trunk to 1.11-0 branch

Modified: branches/1.11-0/man/deviance.cca.Rd
===================================================================
--- branches/1.11-0/man/deviance.cca.Rd	2008-02-17 14:33:46 UTC (rev 228)
+++ branches/1.11-0/man/deviance.cca.Rd	2008-02-17 14:36:01 UTC (rev 229)
@@ -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: branches/1.11-0/man/distconnected.Rd
===================================================================
--- branches/1.11-0/man/distconnected.Rd	2008-02-17 14:33:46 UTC (rev 228)
+++ branches/1.11-0/man/distconnected.Rd	2008-02-17 14:36:01 UTC (rev 229)
@@ -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: branches/1.11-0/man/diversity.Rd
===================================================================
--- branches/1.11-0/man/diversity.Rd	2008-02-17 14:33:46 UTC (rev 228)
+++ branches/1.11-0/man/diversity.Rd	2008-02-17 14:36:01 UTC (rev 229)
@@ -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: branches/1.11-0/man/dune.Rd
===================================================================
--- branches/1.11-0/man/dune.Rd	2008-02-17 14:33:46 UTC (rev 228)
+++ branches/1.11-0/man/dune.Rd	2008-02-17 14:36:01 UTC (rev 229)
@@ -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: branches/1.11-0/man/dune.taxon.Rd
===================================================================
--- branches/1.11-0/man/dune.taxon.Rd	2008-02-17 14:33:46 UTC (rev 228)
+++ branches/1.11-0/man/dune.taxon.Rd	2008-02-17 14:36:01 UTC (rev 229)
@@ -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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