1 %\VignetteIndexEntry{xtable List of Tables Gallery}
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2 %\VignetteDepends{xtable, spdep, splm, sphet}
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3 %\VignetteKeywords{LaTeX, HTML, table}
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4 %\VignettePackage{xtable}
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6 % \VignetteEngine{knitr::knitr}
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7 %**************************************************************************
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8 \documentclass{article}
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9 \usepackage[a4paper, height=24cm]{geometry} % geometry first
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11 \usepackage{booktabs}
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12 \usepackage{longtable}
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13 \usepackage{parskip}
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14 \usepackage{rotating}
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15 \usepackage{tabularx}
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16 \usepackage{titlesec}
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17 \usepackage{hyperref} % hyperref last
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18 \titleformat\subsubsection{\bfseries\itshape}{}{0pt}{}
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19 \newcommand\p{\vspace{2ex}}
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20 \newcommand\code[1]{\texttt{#1}}
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21 \newcommand\pkg[1]{\textbf{#1}}
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22 \setcounter{tocdepth}{2}
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25 \title{\bfseries\Large The Other Packages Gallery}
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26 \author{\bfseries David J. Scott}
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33 \section{Introduction}
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34 This document represents a test of the functions in \pkg{xtable} which
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35 deal with other packages.
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37 <<set, include=FALSE>>=
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39 opts_chunk$set(fig.path = 'Figures/other', debug = TRUE, echo = TRUE)
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40 opts_chunk$set(out.width = '0.9\\textwidth')
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43 The first step is to load the package and set some options for this document.
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44 <<package, results='asis'>>=
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46 options(xtable.floating = FALSE)
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47 options(xtable.timestamp = "")
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52 \section{The packages \pkg{spdep}, \pkg{splm}, and \pkg{sphet}}
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54 Code for supporting these packages and most of the examples used in
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55 this section was originally provided by Martin Gubri
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56 (\url{martin.gubri@framasoft.org}).
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58 \subsection{The package \pkg{spdep}}
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59 \label{sec:package-pkgspdep}
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61 First load the package and create some objects.
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64 data("oldcol", package = "spdep")
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65 COL.lag.eig <- lagsarlm(CRIME ~ INC + HOVAL, data = COL.OLD[],
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68 COL.errW.GM <- GMerrorsar(CRIME ~ INC + HOVAL, data = COL.OLD,
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69 nb2listw(COL.nb, style = "W"),
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72 COL.lag.stsls <- stsls(CRIME ~ INC + HOVAL, data = COL.OLD,
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74 class(COL.lag.stsls)
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76 p1 <- predict(COL.lag.eig, newdata = COL.OLD[45:49,],
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77 listw = nb2listw(COL.nb))
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79 p2 <- predict(COL.lag.eig, newdata = COL.OLD[45:49,],
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80 pred.type = "trend", type = "trend")
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81 #type option for retrocompatibility with spdep 0.5-92
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84 imp.exact <- impacts(COL.lag.eig, listw = nb2listw(COL.nb))
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86 imp.sim <- impacts(COL.lag.eig, listw = nb2listw(COL.nb), R = 200)
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91 \subsubsection{\code{sarlm} objects}
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92 \label{sec:codesarlm-objects}
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94 There is an \code{xtable} method for objects of this type.
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95 <<xtablesarlm, results = 'asis'>>=
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99 The method for \code{xtable} actually uses the summary of the object,
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100 and an identical result is obtained when using the summary of the
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101 object, even if the summary contains more additional information.
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103 <<xtablesarlmsumm, results = 'asis'>>=
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104 xtable(summary(COL.lag.eig, correlation = TRUE))
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107 This same pattern applies to the other objects from this group of packages.
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109 Note that additional prettying of the resulting table is possible, as
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110 for any table produced using \code{xtable}. For example using the
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111 \pkg{booktabs} package we get:
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113 <<xtablesarlmbooktabs, results = 'asis'>>=
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114 print(xtable(COL.lag.eig), booktabs = TRUE)
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117 \subsubsection{\code{gmsar} objects}
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118 \label{sec:codegmsar-objects}
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121 <<xtablegmsar, results = 'asis'>>=
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122 xtable(COL.errW.GM)
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125 \subsubsection{\code{stsls} objects}
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126 \label{sec:codestsls-objects}
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129 <<xtablestsls, results = 'asis'>>=
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130 xtable(COL.lag.stsls)
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133 \subsubsection{\code{sarlm.pred} objects}
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134 \label{sec:codesarlmpred-objects}
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136 \code{xtable} has a method for predictions of \code{sarlm} models.
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138 <<xtablesarlmpred, results = 'asis'>>=
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142 This method transforms the \code{sarlm.pred} objects into data frames,
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143 allowing any number of attributes vectors which may vary according to
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146 <<xtablesarlmpred2, results = 'asis'>>=
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150 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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151 %% xtable.lagImpactMat removed because of problems with unexported function
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152 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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154 %% \subsubsection{\code{lagImpact} objects}
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155 %% \label{sec:codelagimpact-objects}
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157 %% The \code{xtable} method returns the values of direct, indirect and
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158 %% total impacts for all the variables in the model. The class
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159 %% \code{lagImpact} have two different sets of attributes according to if
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160 %% simulations are used. But the \code{xtable} method always returns the
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161 %% three components of the non-simulation case.
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163 %% <<xtablelagimpactexact, results = 'asis'>>=
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164 %% xtable(imp.exact)
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168 %% <<xtablelagimpactmcmc, results = 'asis'>>=
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173 \subsubsection{\code{spautolm} objects}
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174 \label{sec:codespautolm-objects}
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176 The need for an \code{xtable} method for \code{spautolm} was expressed
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177 by Guido Schulz (\url{schulzgu@student.hu-berlin.de}), who also
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178 provided an example of an object of this type. The required code was
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179 implemented by David Scott (\url{d.scott@auckland.ac.nz}).
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181 First create an object of the required type.
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183 <<minimalexample, results = 'hide'>>=
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186 spautolmOBJECT <- spautolm(Z ~ PEXPOSURE + PCTAGE65P,data = nydata,
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187 listw = listw_NY, family = "SAR",
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188 method = "eigen", verbose = TRUE)
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189 summary(spautolmOBJECT, Nagelkerke = TRUE)
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194 class(spautolmOBJECT)
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198 <<xtablespautolm, results = 'asis'>>=
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199 xtable(spautolmOBJECT,
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200 display = c("s",rep("f", 3), "e"), digits = 4)
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205 \subsection{The package \pkg{splm}}
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206 \label{sec:package-pkgsplm}
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208 First load the package and create some objects.
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211 data("Produc", package = "plm")
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212 data("usaww", package = "splm")
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213 fm <- log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp
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214 respatlag <- spml(fm, data = Produc, listw = mat2listw(usaww),
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215 model="random", spatial.error="none", lag=TRUE)
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217 GM <- spgm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc,
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218 listw = usaww, moments = "fullweights", spatial.error = TRUE)
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221 imp.spml <- impacts(respatlag, listw = mat2listw(usaww, style = "W"), time = 17)
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226 \subsubsection{\code{splm} objects}
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227 \label{sec:codesplm-objects}
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229 <<xtablesplm, results = 'asis'>>=
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235 <<xtablesplm1, results = 'asis'>>=
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240 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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241 %% xtable.lagImpactMat removed because of problems with unexported function
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242 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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244 %% The \code{xtable} method works the same on impacts of \code{splm} models.
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246 %% <<xtablesplmimpacts, results = 'asis'>>=
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247 %% xtable(imp.spml)
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250 \subsection{The package \pkg{sphet}}
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251 \label{sec:package-pkgsphet}
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253 First load the package and create some objects.
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256 data("columbus", package = "spdep")
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257 listw <- nb2listw(col.gal.nb)
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258 data("coldis", package = "sphet")
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259 res.stsls <- stslshac(CRIME ~ HOVAL + INC, data = columbus, listw = listw,
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260 distance = coldis, type = 'Triangular')
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263 res.gstsls <- gstslshet(CRIME ~ HOVAL + INC, data = columbus, listw = listw)
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266 imp.gstsls <- impacts(res.gstsls, listw = listw)
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271 \subsubsection{\code{sphet} objects}
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272 \label{sec:codesphet-objects}
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274 <<xtablesphet, results = 'asis'>>=
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279 <<xtablesphet1, results = 'asis'>>=
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283 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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284 %% xtable.lagImpactMat removed because of problems with unexported function
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285 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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286 %% \code{sphet} also provides a method for computing impacts.
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288 %% <<xtablesphetimpacts, results = 'asis'>>=
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289 %% xtable(imp.gstsls)
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292 \section{The \pkg{zoo} package}
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293 \label{sec:pkgzoo-package}
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296 <<zoo, results = 'asis'>>=
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298 xDate <- as.Date("2003-02-01") + c(1, 3, 7, 9, 14) - 1
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300 x <- zoo(rnorm(5), xDate)
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307 <<zoots, results = 'asis'>>=
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308 tempTs <- ts(cumsum(1 + round(rnorm(100), 0)),
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309 start = c(1954, 7), frequency = 12)
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310 tempTable <- xtable(tempTs, digits = 0)
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312 tempZoo <- as.zoo(tempTs)
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313 xtable(tempZoo, digits = 0)
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317 \section{The \pkg{survival} package}
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318 \label{sec:pkgsurvival-package}
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321 <<survival, results = 'asis'>>=
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323 test1 <- list(time=c(4,3,1,1,2,2,3),
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324 status=c(1,1,1,0,1,1,0),
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325 x=c(0,2,1,1,1,0,0),
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326 sex=c(0,0,0,0,1,1,1))
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327 coxFit <- coxph(Surv(time, status) ~ x + strata(sex), test1)
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