[Vegan-commits] r271 - branches/1.11-0/inst/doc
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Thu Mar 20 09:41:29 CET 2008
Author: jarioksa
Date: 2008-03-20 09:41:29 +0100 (Thu, 20 Mar 2008)
New Revision: 271
Modified:
branches/1.11-0/inst/doc/FAQ-vegan.texi
branches/1.11-0/inst/doc/diversity-vegan.Rnw
Log:
merged r262 and r263 (but not r264)
Modified: branches/1.11-0/inst/doc/FAQ-vegan.texi
===================================================================
--- branches/1.11-0/inst/doc/FAQ-vegan.texi 2008-03-19 17:04:32 UTC (rev 270)
+++ branches/1.11-0/inst/doc/FAQ-vegan.texi 2008-03-20 08:41:29 UTC (rev 271)
@@ -85,14 +85,14 @@
@node What is vegan?, What is R?, Introduction, Introduction
@section What is vegan?
-Vegan is an R package for community ecologists. It contains most
-multivariate analysis needed in analysing ecological communities, and
-tools for diversity analysis, and other potentially useful functions.
-Vegan is not self-contained but it must be run under R statistical
-environment, and it also depends on many other R packages. Vegan is
- at url{http://www.gnu.org/philosophy/free-sw.html, free software} and
-distributed under
- at url{http://www.gnu.org/licenses/gpl.html, ,GPL2 license}.
+Vegan is an R package for community ecologists. It contains the most
+popular methods of multivariate analysis needed in analysing ecological
+communities, and tools for diversity analysis, and other potentially
+useful functions. Vegan is not self-contained but it must be run under
+R statistical environment, and it also depends on many other R
+packages. Vegan is @url{http://www.gnu.org/philosophy/free-sw.html, free
+software} and distributed under
+ at url{http://www.gnu.org/licenses/gpl.html, ,GPL2 license}.
@node What is R?, How to obtain vegan and R?, What is vegan?, Introduction
@section What is R?
@@ -138,6 +138,7 @@
@item Package @code{rgl}
is needed by @code{ordirgl}
+and @code{rgl.isomap}
@item Package @code{tcltk}
is needed by @code{orditkplot}
@@ -158,10 +159,11 @@
@section What other documentation is available for vegan?
Vegan is a fully documented R package with standard help pages. These
-are the most authoritative sources of documentation. Vegan package ships
-with other documents which can be read with @code{vegandocs} command
-(documented in the vegan help). The documents included in the vegan
-package are
+are the most authoritative sources of documentation (and as a last
+resource you can use the force and the read the source, as vegan is open
+source). Vegan package ships with other documents which can be read
+with @code{vegandocs} command (documented in the vegan help). The
+documents included in the vegan package are
@itemize
@item
Vegan @code{ChangeLog}.
@@ -184,7 +186,7 @@
@itemize
@item
- at url{http://cc.oulu.fi/~jarioksa/softhelp/vegan.html}: vegan homepage.
+ at url{http://vegan.r-forge.r-project.org/}: vegan homepage.
@item
@url{http://cc.oulu.fi/~jarioksa/opetus/metodi/vegantutor.pdf}: vegan
tutorial.
@@ -249,8 +251,7 @@
tests on the devel package, and if passed, it builds source package and
Windows binaries. You can install those packages within R with command
@code{install.packages("vegan",
-repos="http://r-forge.r-project.org/")}. However, MacOS X binaries
-are not available from R-Forge.
+repos="http://r-forge.r-project.org/")}.
@node How to report a bug in vegan?, Is it a bug or a feature?, Are there binaries for devel versions?, Introduction
@section How to report a bug in vegan?
@@ -447,7 +448,7 @@
species or sites, and then you can find scores for all points
using your complete data as @code{newdata}. The @code{predict}
functions are available for basic eigenvector methods in vegan
-(@code{cca}, @code{rda}, @code{decorana}, for an up-to-date list use
+(@code{cca}, @code{rda}, @code{decorana}, for an up-to-date list, use command
@code{methods("predict")}). You also can simulate the passive points in R by using
low weights to row and columns (this is the method used in software
with passive points). For instance, the following command makes row 3
@@ -476,11 +477,11 @@
@node I want to use Helmert or sum contrasts, What are aliased variables and how to see them?, Class variables and dummies, Ordination
@section I want to use Helmert or sum contrasts
- at code{vegan} uses standard @code{R} utilities for defining
+ at code{vegan} uses standard R utilities for defining
contrasts. The default in standard installations is to use treatment
contrasts, but you can change the behaviour globally setting
@code{options} or locally by using keyword @code{contrasts}. Please
-check the @code{R} help pages and user manuals for details.
+check the R help pages and user manuals for details.
@node What are aliased variables and how to see them?, Plotting aliased variables, I want to use Helmert or sum contrasts, Ordination
@section What are aliased variables and how to see them?
@@ -514,9 +515,9 @@
Vegan has an alternative permutation function @code{permuted.index2}
which allows restricted permutation designs for time series, line
transects, spatial grids and blocking factors. Over time, the other
-functions that currently use the older @code{permuted.index} will be updated
-to use @code{permuted.index2}, but at the moment it is used only in one
-pilot function.
+functions that currently use the older @code{permuted.index} will be
+updated to use @code{permuted.index2}, but at the moment it is still
+experimental.
@node How to use different plotting symbols in ordination graphics?, How to avoid cluttered ordination graphs?, Constrained permutations in vegan, Ordination
@section How to use different plotting symbols in ordination graphics?
@@ -546,9 +547,7 @@
to give to your @code{plot} command.)
@item Use points, and add labels to desired points using @code{identify}
-for ordination graphics, if you do not need to see all labels. You may
-need to first create an empty plot using @code{plot(..., type="n")}, if
-you are not satisfied with the default graph.
+for ordination graphics, if you do not need to see all labels.
@item Use @code{orditorp} function that uses labels only if these can be
added to a graph without overwriting other labels, and points otherwise,
Modified: branches/1.11-0/inst/doc/diversity-vegan.Rnw
===================================================================
--- branches/1.11-0/inst/doc/diversity-vegan.Rnw 2008-03-19 17:04:32 UTC (rev 270)
+++ branches/1.11-0/inst/doc/diversity-vegan.Rnw 2008-03-20 08:41:29 UTC (rev 271)
@@ -60,7 +60,8 @@
species so that $\sum_{i=1}^S p_i = 1$, and $b$ is the base of the
logarithm. It is most common to use natural logarithms (and then we
mark index as $H'$), but $b=2$ has
-theoretical justification. Shannon index is calculated with:
+theoretical justification. The default is to use natural logarithms.
+Shannon index is calculated with:
<<>>=
H <- diversity(BCI)
@
@@ -89,7 +90,7 @@
k <- sample(nrow(BCI), 6)
R <- renyi(BCI[k,])
@
-We can really regard a site more diverse if all of its Rényi
+We can really regard a site more diverse if all of its Rényi
diversities are higher than in another site. We can inspect this
graphically using the standard \texttt{plot} function for the
\texttt{renyi} result (Fig. \ref{fig:renyi}).
@@ -140,7 +141,8 @@
Equation \ref{eq:rarevar} actually is of the same form as the variance
of sum of correlated variables:
\begin{equation}
-\mathrm{var} \left(\sum x_i \right) = \sum \mathrm{var}(x_i) - 2 \mathrm{cov}(x_i, x_j)
+\mathrm{var} \left(\sum x_i \right) = \sum \mathrm{var}(x_i) - 2 \sum_{i=1}^S
+\sum_{j>i} \mathrm{cov}(x_i, x_j)
\end{equation}
The number of stems per hectare varies in our
@@ -182,7 +184,7 @@
how different two different species are. The index is much used in
aquatic ecology, in particular for studying the effects of pollution
or other degradation, which often is first evident in the loss of
-higher level taxonomic units.
+higher taxonomic units.
The two basic indecies are called taxonomic diversity ($\Delta$) and
taxonomic distinctness ($\Delta^*$):
@@ -192,14 +194,14 @@
\end{align}
These equations give the index values for a single site, and summation
goes over species $i$ and $j$, and $\omega$ are the taxonomic
-distnaces among taxa, $x$ are species abundances, and $n$ is the total
-abundance for a site. With presence absence data, both indeices
+distances among taxa, $x$ are species abundances, and $n$ is the total
+abundance for a site. With presence absence data, both indices
reduce to the same index called $\Delta^+$, and for this it is
-possible to estimate standarad deviation. There are two indices
+possible to estimate standard deviation. There are two indices
derived from $\Delta^+$: it can be multiplied with species
richness\footnote{This text normally uses upper case letter $S$ for
species richness, but lower case $s$ is used here in accordance with
- the original papers on taxonomic diversity}
+ the original papers on taxonomic diversity}
to give $s \Delta^+$, or it can be used to estimate an index of
variation in taxonomic distinctness $\Lambda^+$:
\begin{equation}
@@ -207,7 +209,7 @@
\end{equation}
We still need the taxonomic differences among species ($\omega$) to
-calculate the indices of taxonomic differences. This can be any
+calculate the indices. These can be any
distance structure among species, but usually it is found from
established hierarchic taxonomy. Typical coding is that differences
among species in the same genus is $1$, among the same family it is
@@ -221,7 +223,7 @@
Function \texttt{taxondive} implements indices of taxonomic diversity,
and \texttt{taxa2dist} can be used to convert classification tables to
-taxonomid distances either with constant or variable step lengths
+taxonomic distances either with constant or variable step lengths
between succesive categories. There is no taxonomic table for the BCI
data in \texttt{vegan}\footnote{Actually I made such a classification,
but taxonomic differences proved to be of little use in the Barro
@@ -233,7 +235,7 @@
data(dune.taxon)
taxdis <- taxa2dist(dune.taxon, varstep=TRUE)
mod <- taxondive(dune, taxdis)
-@
+@
\begin{SCfigure}
<<fig=true,echo=false>>=
plot(mod)
@@ -242,7 +244,7 @@
points are diversity values of single sites, and the funnel is their
approximate confidence intervals ($2 \times$ standard error).}
\label{fig:taxondive}
-\end{SCfigure}
+\end{SCfigure}
\section{Species abundance models}
@@ -261,7 +263,7 @@
\hat f_n = \frac{\alpha x^n}{n}
\end{equation}
where $\alpha$ is the diversity parameter, and $x$ is a nuisance
-parameter defined by $\alpha$ and total number
+parameter defined by $\alpha$ and total number
of individuals $N$ in the site, $x = N/(N-\alpha)$. Fisher's
log-series for a randomly selected plot is (Fig. \ref{fig:fisher}):
<<>>=
@@ -279,7 +281,7 @@
\end{SCfigure}
We already saw $\alpha$ as a diversity index. Now we also obtained
estimate of standard error of $\alpha$ (these also are optionally
-available in \texttt{fisher.fit}). The standard errors are based on
+available in \texttt{fisherfit}). The standard errors are based on
the second derivatives (curvature) of log-likelihood at the solution
of $\alpha$. The distribution of $\alpha$ is often non-normal
and skewed, and standard errors are of not much use. However,
@@ -315,7 +317,7 @@
logarithmic abundances in decreasing order, or against ranks of
species. These are known as ranked abundance
distribution curves, species abundance curves, dominance--diversity
-curves or Whittaker plots.
+curves or Whittaker plots.
Function \texttt{radfit} fits some of the most popular models using
maximum likelihood estimation:
\begin{align}
@@ -428,7 +430,7 @@
should be studied with respect to gradients, but almost everybody
understand that as a measure of general heterogeneity: how many more
species do you have in a collection of sites compared to an average
-site.
+site.
The best known index of beta diversity is based on the ratio of total
number of species in a collection of sites ($S$) and the average
@@ -443,7 +445,7 @@
of \texttt{vegan} function \texttt{specnumber}:
<<>>=
ncol(BCI)/mean(specnumber(BCI)) - 1
-@
+@
The index of eq. \ref{eq:beta} is problematic because $S$ increases
with the number of sites even when sites are all subsets of the same
@@ -462,14 +464,14 @@
<<>>=
beta <- vegdist(BCI, binary=TRUE)
mean(beta)
-@
+@
There are many other definitions of beta diversity in addition to
eq. \ref{eq:beta}, and many of these reduce to well known
dissimilarity indices. All commonly used indices can be found using
\texttt{designdist} function which allows defining your own
dissimilarity measures. One of the more interesting indices is based
-on the Arrhenius species--area model
+on the Arrhenius species--area model
\begin{equation}
\label{eq:arrhenius}
\hat S = c X^z
@@ -481,19 +483,19 @@
islands can be regarded as subsets of the same community, indicating
that we really should talk about gradient differences if $z > 0.3$. We
can find the value of $z$ for a pair of plots using function
-\texttt{designdist}:
+\texttt{designdist}:
<<>>=
z <- designdist(BCI, "(log(A+B-J)-log(A+B)+log(2))/log(2)")
quantile(z)
-@
+@
The size $X$ and parameter $c$ cancel out, and the index gives the
-estimate $z$ for any pair of sites.
+estimate $z$ for any pair of sites.
Function \texttt{betadisper} can be used to analyse beta diversities
with respect to classes or factors. There is no such classification
available for the Barro Colorado Island data, and the example studies
beta diversities in the management classes of the dune meadows
-(Fig. \ref{fig:betadisper}):
+(Fig. \ref{fig:betadisper}):
<<>>=
data(dune)
data(dune.env)
@@ -541,7 +543,7 @@
idea in bootstrap that if we repeat sampling (with replacement) from
the same data, we miss any many species as we missed originally.
-The variance estimators are of Chao is:
+The variance estimators of Chao is:
\begin{equation}
s^2 = f_2 \left(\frac{G^4}{4} + G^3 + \frac{G^2}{2} \right), \,
\text{where}\quad G = \frac{f_1}{f_2}
@@ -656,5 +658,9 @@
\caption{Beals smoothing for \emph{Ceiba pentandra}.}
\label{fig:beals}
\end{SCfigure}
+For the probability of the pool membership, jackknived estimates
+should be used, and concerned site and species should be removed when
+estimating the probablity, but this is not done in \texttt{beals}
+which uses the traditional equations.
\end{document}
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