CircosHeatmap-aardio/dist/lib/r/site-library/xtable/doc/OtherPackagesGallery.Rnw

%\VignetteIndexEntry{xtable Other Packages Gallery}
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% !Rnw weave = knitr
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%**************************************************************************
\documentclass{article}
\usepackage[a4paper, height=24cm]{geometry} % geometry first
\usepackage{array}
\usepackage{booktabs}
\usepackage{longtable}
\usepackage{parskip}
\usepackage{rotating}
\usepackage{tabularx}
\usepackage{titlesec}
\usepackage{hyperref} % hyperref last
\titleformat\subsubsection{\bfseries\itshape}{}{0pt}{}
\newcommand\p{\vspace{2ex}}
\newcommand\code[1]{\texttt{#1}}
\newcommand\pkg[1]{\textbf{#1}}
\setcounter{tocdepth}{2}
\begin{document}

\title{\bfseries\Large The Other Packages Gallery}
\author{\bfseries David J. Scott}
\maketitle

\tableofcontents

\newpage

\section{Introduction}
This document represents a test of the functions in \pkg{xtable} which
deal with other packages.

<<set, include=FALSE>>=
library(knitr)
opts_chunk$set(fig.path = 'Figures/other', debug = TRUE, echo = TRUE)
opts_chunk$set(out.width = '0.9\\textwidth')
@

The first step is to load the package and set some options for this document.
<<package, results='asis'>>=
library(xtable)
options(xtable.floating = FALSE)
options(xtable.timestamp = "")
options(width = 60)
set.seed(1234)
@

%% \section{The packages \pkg{spdep}, \pkg{splm}, and \pkg{sphet}}

%% Code for supporting these packages and most of the examples used in
%% this section was originally provided by Martin Gubri
%% (\url{martin.gubri@framasoft.org}).

%% \subsection{The package \pkg{spdep}}
%% \label{sec:package-pkgspdep}

%% First load the package and create some objects.
%% <<dataspdep>>=
%% library(spdep)
%% data("oldcol", package = "spdep")

%% data.in.sample <- COL.OLD[1:44,]
%% data.out.of.sample <- COL.OLD[45:49,]

%% listw.in.sample <- nb2listw(subset(COL.nb, !(1:49 %in% 45:49)))
%% listw.all.sample <- nb2listw(COL.nb)

%% COL.lag.eig <- lagsarlm(CRIME ~ INC + HOVAL, data = data.in.sample,
%%                         listw.in.sample)
%% class(COL.lag.eig)
%% COL.errW.GM <- GMerrorsar(CRIME ~ INC + HOVAL, data = data.in.sample,
%%                           listw.in.sample, returnHcov = TRUE)
%% class(COL.errW.GM)
%% COL.lag.stsls <- stsls(CRIME ~ INC + HOVAL, data = data.in.sample,
%%                        listw.in.sample)
%% class(COL.lag.stsls)

%% p1 <- predict(COL.lag.eig, newdata = data.out.of.sample,
%%               listw = listw.all.sample)
%% class(p1)
%% p2 <- predict(COL.lag.eig, newdata = data.out.of.sample,
%%               pred.type = "trend", type = "trend")
%% #type option for retrocompatibility with spdep 0.5-92
%% class(p2)

%% imp.exact <- impacts(COL.lag.eig, listw = listw.in.sample)
%% class(imp.exact)
%% imp.sim <- impacts(COL.lag.eig, listw = listw.in.sample, R = 200)
%% class(imp.sim)
%% @ %def


%% \subsubsection{\code{sarlm} objects}
%% \label{sec:codesarlm-objects}

%% There is an \code{xtable} method for objects of this type.
%% <<xtablesarlm, results = 'asis'>>=
%% xtable(COL.lag.eig)
%% @ %def

%% The method for \code{xtable} actually uses the summary of the object,
%% and an identical result is obtained when using the summary of the
%% object, even if the summary contains more additional information.

%% <<xtablesarlmsumm, results = 'asis'>>=
%% xtable(summary(COL.lag.eig, correlation = TRUE))
%% @ %def

%% This same pattern applies to the other objects from this group of packages.

%% Note that additional prettying of the resulting table is possible, as
%% for any table produced using \code{xtable}. For example using the
%% \pkg{booktabs} package we get:

%% <<xtablesarlmbooktabs, results = 'asis'>>=
%% print(xtable(COL.lag.eig), booktabs = TRUE)
%% @ %def

%% \subsubsection{\code{gmsar} objects}
%% \label{sec:codegmsar-objects}


%% <<xtablegmsar, results = 'asis'>>=
%% xtable(COL.errW.GM)
%% @ %def

%% \subsubsection{\code{stsls} objects}
%% \label{sec:codestsls-objects}


%% <<xtablestsls, results = 'asis'>>=
%% xtable(COL.lag.stsls)
%% @ %def

%% \subsubsection{\code{sarlm.pred} objects}
%% \label{sec:codesarlmpred-objects}

%% \code{xtable} has a method for predictions of \code{sarlm} models.

%% <<xtablesarlmpred, results = 'asis'>>=
%% xtable(p1)
%% @ %def

%% This method transforms the \code{sarlm.pred} objects into data frames,
%% allowing any number of attributes vectors which may vary according to
%% predictor types.

%% <<xtablesarlmpred2, results = 'asis'>>=
%% xtable(p2)
%% @ %def

%% \subsubsection{\code{lagImpact} objects}
%% \label{sec:codelagimpact-objects}

%% The \code{xtable} method returns the values of direct, indirect and
%% total impacts for all the variables in the model. The class
%% \code{lagImpact} has two different sets of attributes according to if
%% simulations are used. But the \code{xtable} method always returns the
%% three components of the non-simulation case.

%% <<xtablelagimpactexact, results = 'asis'>>=
%% xtable(imp.exact)
%% @ %def

%% \p
%% <<xtablelagimpactmcmc, results = 'asis'>>=
%% xtable(imp.sim)
%% @ %def


%% \subsubsection{\code{spautolm} objects}
%% \label{sec:codespautolm-objects}

%% The need for an \code{xtable} method for \code{spautolm} was expressed
%% by Guido Schulz (\url{schulzgu@student.hu-berlin.de}), who also
%% provided an example of an object of this type. The required code was
%% implemented by David Scott (\url{d.scott@auckland.ac.nz}).

%% First create an object of the required type.

%% <<minimalexample, results = 'hide'>>=
%% library(spdep)
%% example(NY_data)
%% spautolmOBJECT <- spautolm(Z ~ PEXPOSURE + PCTAGE65P,data = nydata,
%%                            listw = listw_NY, family = "SAR",
%%                            method = "eigen", verbose = TRUE)
%% summary(spautolmOBJECT, Nagelkerke = TRUE)
%% @ %def

%% \p
%% <<spautolmclass>>=
%% class(spautolmOBJECT)
%% @ %def


%% <<xtablespautolm, results = 'asis'>>=
%% xtable(spautolmOBJECT,
%%        display = c("s",rep("f", 3), "e"), digits = 4)
%% @ %def



%% \subsection{The package \pkg{splm}}
%% \label{sec:package-pkgsplm}

%% First load the package and create some objects.
%% <<datasplm>>=
%% library(splm)
%% data("Produc", package = "plm")
%% data("usaww",  package = "splm")
%% fm <- log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp
%% respatlag <- spml(fm, data = Produc, listw = mat2listw(usaww),
%%                    model="random", spatial.error="none", lag=TRUE)
%% class(respatlag)
%% GM <- spgm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc,
%%            listw = usaww, moments = "fullweights", spatial.error = TRUE)
%% class(GM)

%% imp.spml <- impacts(respatlag, listw = mat2listw(usaww, style = "W"), time = 17)
%% class(imp.spml)
%% @ %def


%% \subsubsection{\code{splm} objects}
%% \label{sec:codesplm-objects}

%% <<xtablesplm, results = 'asis'>>=
%% xtable(respatlag)
%% @ %def


%% \p
%% <<xtablesplm1, results = 'asis'>>=
%% xtable(GM)
%% @ %def



%% The \code{xtable} method works the same on impacts of \code{splm} models.

%% <<xtablesplmimpacts, results = 'asis'>>=
%% xtable(imp.spml)
%% @ %def

%% \subsection{The package \pkg{sphet}}
%% \label{sec:package-pkgsphet}

%% First load the package and create some objects.
%% <<datasphet>>=
%% library(sphet)
%% data("columbus", package = "spdep")
%% listw <- nb2listw(col.gal.nb)
%% data("coldis", package = "sphet")
%% res.stsls <- stslshac(CRIME ~ HOVAL + INC, data = columbus, listw = listw,
%%                       distance = coldis, type = 'Triangular')
%% class(res.stsls)
%% @ %def


%% \subsubsection{\code{sphet} objects}
%% \label{sec:codesphet-objects}

%% <<xtablesphet, results = 'asis'>>=
%% xtable(res.stsls)
%% @ %def



\section{The \pkg{zoo} package}
\label{sec:pkgzoo-package}


<<zoo, results = 'asis'>>=
library(zoo)
xDate <- as.Date("2003-02-01") + c(1, 3, 7, 9, 14) - 1
as.ts(xDate)
x <- zoo(rnorm(5), xDate)
xtable(x)
@ %def


\p

<<zoots, results = 'asis'>>=
tempTs <- ts(cumsum(1 + round(rnorm(100), 0)),
              start = c(1954, 7), frequency = 12)
tempTable <- xtable(tempTs, digits = 0)
tempTable
tempZoo <- as.zoo(tempTs)
xtable(tempZoo, digits = 0)
@ %def


\section{The \pkg{survival} package}
\label{sec:pkgsurvival-package}


<<survival, results = 'asis'>>=
library(survival)
test1 <- list(time=c(4,3,1,1,2,2,3),
              status=c(1,1,1,0,1,1,0),
              x=c(0,2,1,1,1,0,0),
              sex=c(0,0,0,0,1,1,1))
coxFit <- coxph(Surv(time, status) ~ x + strata(sex), test1)
xtable(coxFit)
@ %def

\end{document}