% Usage: knitr an
\documentclass{article}
\usepackage{relsize,setspace}  % used by latex(describe( ))
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\usepackage{lscape}            % for landscape mode tables
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\newcommand{\code}[1]{{\smaller\texttt{#1}}}
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\title{Analysis of Race and Severe Aortic Stenosis}
\author{Frank Harrell\\\smaller Department of Biostatistics\\\smaller Vanderbilt University School of Medicine}
\begin{document}
\maketitle
\tableofcontents

<<echo=FALSE>>=
require(rms)
@

\section{Univariable Descriptive Statistics}

<<describe,results='asis'>>=
Load(ras)
ras <-
  within(ras, {
    label(age) <- 'Age'
    diabetes <- ifelse(diabetes.after.review=='No', 0,
                       ifelse(diabetes.after.review=='Yes', 1,
                              ifelse(diabetes.search=='No', 0,
                                     ifelse(diabetes.search=='Yes', 1,
                                            NA))))
    htn <- ifelse(htn.after.review=='No', 0,
                  ifelse(htn.after.review=='Yes', 1,
                         ifelse(htn.search=='No', 0,
                                ifelse(htn.search=='Yes', 1,
                                       NA))))
    dialysis <- ifelse(dialysis.after.review=='No', 0,
                       ifelse(dialysis.after.review=='Yes', 1,
                              ifelse(dialysis.search=='No', 0,
                                     NA)))
   cad <- ifelse(cad.after.review=='No', 0,
                 ifelse(cad.after.review=='Yes', 1,
                        ifelse(cad.search=='No', 0,
                        ifelse(cad.search=='Yes', 1,
                                      NA))))
    gender <- factor(ifelse(gender=='Female', 'Female',
                            ifelse(gender=='Male', 'Male',
                                   NA)))
    })
latex(describe(ras), file='')
@

\section{Missing Data}
Let's look at patterns of missing values, especially which variables
are missing on the same patients.

<<naclus,top=2>>=
r <-  subset(ras, select=c(gender,race,age,bmi,ldl,diabetes,htn,dialysis,
                   cad,statin,sasr,sas.etiology,creatinine))
dd <- datadist(r)
n <- naclus(subset(r, select=-sas.etiology))
naplot(n, which='na per obs')
@

<<naplot,top=2,rt=1,ps=8>>=
options(datadist='dd')
naplot(n, which='mean na')
@

<<naplot2>>=
plot(n)
@ 

\end{document}