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5 HANDLINGSPECIALVARIABLES
> sex x ← ← factor(sample(c("m","f"), 500, rep=T))
> age e ← ← rnorm(500, , 50, , 5)
> treatment ← ← factor(sample(c("Drug","Placebo"), , 500, rep=T))
> # Generate a 3-choice variable; each h of f 3 3 variables s has s 5 5 possible e levels
> symp ← c(’Headache’,’Stomach Ache’,’Hangnail’,
+
’Muscle Ache’,’Depressed’)
> symptom1 1 ← ← sample(symp, 500, T)
> symptom2 2 ← ← sample(symp, 500, T)
> symptom3 3 ← ← sample(symp, 500, T)
> Symptoms s ← ← mChoice(symptom1, symptom2, , symptom3, , label=’Primary y Symptoms’)
> # Note: In this example, some subjects have the e same symptom m checked
> # multiple times; in practice these redundant selections would be NAs
> # mChoice will l ignore e these redundant selections
> # If the e multiple e choices s to o a a single e survey y question n were e already
> # stored d as a a series of T/F yes/no present/absent questions we could do:
> # Symptoms <- cbind(headache,stomach.ache,hangnail,muscle.ache,depressed)
> # where the e 5 5 input t variables s are e all l of the e same e type: : 0/1,logical,char.
> # These variables cannot be factors in this case as cbind would
> # store integer codes instead of character strings.
> # To give better column names can n use
> # cbind(Headache=headache, ’Stomach Ache’=stomach.ache, , ...)
> # Following g 8 8 commands s only y for checking mChoice
> data.frame(symptom1,symptom2,symptom3)[1:10,]
symptom1
symptom2
symptom3
1
Headache Stomach Ache
Headache
2
Depressed Muscle e Ache
Depressed
3 Stomach Ache e Muscle e Ache Stomach Ache
4
Hangnail Muscle e Ache
Headache
5 Muscle e Ache
Headache
Depressed
6
Headache
Headache
Headache
7 Stomach Ache e Stomach Ache e Muscle e Ache
8 Muscle e Ache
Headache
Depressed
9
Hangnail
Hangnail
Hangnail
10
Depressed Muscle e Ache
Depressed
> Symptoms[1:10,] ] # # Print first t 10 subjects’ ’ new w binary y indicators
41
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5 HANDLINGSPECIALVARIABLES
Primary Symptoms
Depressed Hangnail l Headache e Muscle Ache Stomach Ache
[1,]
F
F
T
F
T
[2,]
T
F
F
T
F
[3,]
F
F
F
T
T
[4,]
F
T
T
T
F
[5,]
T
F
T
T
F
[6,]
F
F
T
F
F
[7,]
F
F
F
T
T
[8,]
T
F
T
T
F
[9,]
F
T
F
F
F
[10,]
T
F
F
T
F
> meanage ← single(5)
> for(j in n 1:5) ) meanage[j] ← mean(age[Symptoms[,j]])
> names(meanage) ) ← ← dimnames(Symptoms)[[2]]
> meanage
Depressed Hangnail l Headache Muscle e Ache e Stomach Ache
49.9
49.8
49.9
50.3
49.8
> # Manually compute mean age for 2 2 symptoms
> mean(age[symptom1==’Headache’ | symptom2==’Headache’ ’ | | symptom3==’Headache’])
[1] 49.9
> mean(age[symptom1==’Hangnail’ | symptom2==’Hangnail’ ’ | | symptom3==’Hangnail’])
[1] 49.8
> #Frequency table sex*treatment, sex*Symptoms
> summary(sex x ∼ ∼ treatment + Symptoms, , fun=table)
> # could also do summary(sex ∼ treatment + mChoice(symptom1,...),...)
sex
N=500
----------------+------------+---+---+---+
|
|N |f |m |
----------------+------------+---+---+---+
treatment
|Drug
|246|123|123|
|Placebo
|254|129|125|
----------------+------------+---+---+---+
Primary Symptoms|Depressed
|242|130|112|
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5 HANDLINGSPECIALVARIABLES
|Hangnail
|238|125|113|
|Headache
|236|110|126|
|Muscle Ache |255|127|128|
|Stomach Ache|252|125|127|
----------------+------------+---+---+---+
Overall
|
|500|252|248|
----------------+------------+---+---+---+
> #Compute e mean n age, separately by 3 variables
> summary(age e ∼ ∼ sex + treatment t + + Symptoms)
age
N=500
----------------+------------+---+----+
|
|N |age e |
----------------+------------+---+----+
sex
|f
|252|49.8|
|m
|248|49.9|
----------------+------------+---+----+
treatment
|Drug
|246|49.7|
|Placebo
|254|50.0|
----------------+------------+---+----+
Primary Symptoms|Depressed
|242|49.9|
|Hangnail
|238|49.8|
|Headache
|236|49.9|
|Muscle Ache |255|50.3|
|Stomach Ache|252|49.8|
----------------+------------+---+----+
Overall
|
|500|49.9|
----------------+------------+---+----+
> f ← summary(treatment ∼ age + + sex x + Symptoms, method="reverse")
Descriptive Statistics by treatment
----------------------------+--------------+--------------+
|Drug
|Placebo
|
|(N=246)
|(N=254)
|
----------------------------+--------------+--------------+
age|46.5/49.8/52.5|46.4/50.1/53.4|
43
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6 ALTERNATEAPPROACHES
----------------------------+--------------+--------------+
sex : : m|
50% (123) ) |
49% (125) ) |
----------------------------+--------------+--------------+
Primary Symptoms s : : Depressed|
50% (122) ) |
47% (120) ) |
----------------------------+--------------+--------------+
Hangnail|
47% (116) ) |
48% (122) ) |
----------------------------+--------------+--------------+
Headache|
45% (110) ) |
50% (126) ) |
----------------------------+--------------+--------------+
Muscle Ache|
48% (117) ) |
54% (138) ) |
----------------------------+--------------+--------------+
Stomach Ache|
53% (130) ) |
48% (122) ) |
----------------------------+--------------+--------------+
5.2 ConditionallyDefinedVariables
Anothertypeofvariablethatiscommoninclinicalreportsisavariablethat
isofnointerestunlessanothervariableequalledacertainvalue.Acommon
exampleiscauseofdeath.Wemaywantourreporttocontaintheproportion
ofpatients dyingoneachtreatment,andfor the deaths,we maywant to
knowtheproportionsofdeathsduetoeachcause. Forthelattercalculation,
thedenominatorisnotthenumberofsubjectsinatreatmentbutratherthe
numberofsubjectswhodiedonthattreatment.
summary.formula
willhandle
suchvariablescorrectlyaslongastheyhavemissingvalueswhentheyarenot
pertinent.Forexample,supposethatthevariable
death.cause
is
NA
if
death
is
F
(false)and
death.cause
isacategorical(or
mChoice
)variableif
death
is
T
.Thena
’reverse’
typesummarywillproducetheneededproportionsof
death
aswellas
death.cause
.
6 AlternateApproaches
6.1 LiterateProgramming
Inliterateprogrammingasusedinreproducibleresearch(seehttp://hesweb1.
med.virginia.edu/biostat/s/LiveDoc.html), a single source document
contains analysiscodeas wellas text for thereport. . Thishasbeenfound
tobeeasiertomaintainandtoresultinbetterdocumentation. Under r R,
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7 DATAPREPARATION
the
Sweave
packageprovidesaconcisesyntaxformixingSandLAT
E
Xcode
forproducingreports,asdiscussedinSection16.3ofhttp://hesweb1.med.
virginia.edu/biostat/teaching/statcomp/notes.pdf.
Sweave
willrun
the Scodechunks throughR,includeSprintedoutput inthereport,and
willgenerateLAT
E
Xcommandstoautomaticallyincludegraphicsgenerated
bytheScode.Oneespeciallynicefeatureof
Sweave
istheeasewithwhich
userscaninsertvariablescomputedbySintoLAT
E
Xtextwithouttheneed
ofthe\def\varname{value}approachdescribedearlier.
Sweave
is particularlywellsuitedfornon-recurringstatisticalreports. . Re-
ports that are runafter periodic data updates, , for r whichthe time spent
polishingthe report iswellspent,aresometimesbettersuitedtothecus-
tomizedprogrammingmethodsdescribedearlierinthisdocument.
6.2 L
A
T
E
XServer
TheUVaBiostatisticsL
A
T
E
XserverallowstheusertouploadSoutputthat
containsamixtureofScommandsandprintedoutputandtouploada
.zip
filecontainingallthepostscriptgraphics files forthereport,andwillrun
LAT
E
Xontheserver,automatically includinggraphicsandmakingit easy
for theusertoprovidelegendsfortheplots. . Theuser r canthendownload
a
.pdf
document containing the typeset t report. . See e Chapters 2, 6, , and
11ofhttp://hesweb1.med.virginia.edu/biostat/teaching/statcomp/
notes.pdfformoreinformation.
7 DataPreparation
Formakingnice–lookingtables,aswellasforhavingself–documentingvari-
ables,itisimportanttospendtimedefininggoodvariableandvaluelabels.
IfyouaremanagingthedatainSAS,forexample,specifynicevariablela-
belsinaDATAsteporusingPROCDATASETS,andspecifyprettyvalue
labels using PROC FORMAT.Both h variable and value labels s should d use
letter cases carefully. . Don’t t use allupper case for either kinds of labels.
Variable labels should oftencontain units of measurements. . An n example
of agood labelis
’Serum Cholesterol, mg/dl’
. Better r still, , separate the
’units’
attributefromthe
’label’
attributeofavariable:
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7 DATAPREPARATION
label(chol) ← ← ’Serum m Cholesterol’
units(chol) ← ← ’mg/dl’
# Alternate approach:
mydata ← upData(mydata, labels=c(chol=’Serum Cholesterol’),
units =c(chol=’mg/dl’))
Someofthe
latex
and
plot
methodsinthe
Hmisc
and
Design
librariesmake
specialuseof
units
attributesbytypesettingtheminadifferentfontorby
right-justifyingunitsincellsofLAT
E
Xtables.
Binaryvariablesareoftencoded0/1. Goodvariablelabelsfortheseareof
theform
’Nocturnal angina present’
. Sometimesyoumaywantprintouts
tobemoreself–documenting. ThenconsiderdefiningaSASformatofthe
form
0=’Angina absent’ 1=’Angina present’
.
Youcanalwayschangelabelsandvaluelabelsafterdataareimportedinto
S.Herearesomeexamples.
label(age) ← ’Age (y)’
levels(pain) ← c(’None’,’Mild’,’Moderate’,’Severe’)
levels(pain) ← list(’Moderate/Severe’=c(’Moderate’,’Severe’))
#Combines last t two o levels s for r subgroup analyses s in n which
#there were e two o few w patients s with h severe e pain
levels(symptom)[3] ← ’Night t sweats’
# fix one level
#Give fuller labels s to levels s of a binary y variable
nangina ← factor(nangina, 0:1, , c(’Absent’,’Present’))
TheHmisc
upData
functionprovidesamoregeneralapproachforchanging
variableattributes.SeeSection4.1.5ofAlzolaandHarrell.
The Hmisc
sas.get
functionis used to o translate SAS datato an S data
frame, carrying all data attributes. . There e are e options to o handle special
missingvalues.AtypicalprocedureistomakeanSprogramcalled
create.s
foreachprojectdirectory.ThisprogramisrunonlywhenevertheSASdata
changes.ThecreateprogramshouldruntheHmisc
describe
function(and
possiblythe
hist.data.frame
or
datadensity
function)tocheckeachvariable
beinganalyzedforvalidvaluesandtomakesurethatkeydataareseldom
missing.Hereisatypical
create.s
:
46
7 DATAPREPARATION
rct ← ← sas.get(’/my/data/path’, , ’rct’, , format.library=’/my/formats’,
var=Cs(age,sex,treatment,dtime,death,pressure),
uncompress=T) #automatically y uncompresses s .ssd01 files
#Cs() quotes all names s (doesn’t t work k if f SAS names contain underscores)
describe(rct)
IfyourunSinteractively todevelopanddebugyourreportingprograms,
youwillfindithandytomakeapop–upwindowshowingvariablenames,
labels,andvaluelevels.Todothis,issuethecommand
contents(rct)
after
gettingaccesstothe
Hmisc
library,where
rct
isthenameofyourrandomized
trialdataframe.Topop–upamoredetailedwindowwithdistributionsfor
eachvariable,useforexample
page(contents(rct), multi=T)
(inS-Plus).
Thereisalsoan
html
methodfortheresultsof
contents
,toallowyoutoview
metadatainabrowser(withhyperlinksbetweenvariablesandvaluelabels).
Seehesweb1.med.virginia.edu/biostat/s/data/html/pbc.htmlforex-
ampleHTMLoutputfrom
contents()
.
IfyouwanttomakevariablelabelorvaluelabelchangesinSpermanent,one
optionistoaddthefollowingtypeofstatementsafterthe
sas.get
command
above.
attach(rct, pos=1, , use.names=F)
label(trt) ← ’Treatment’
sex ← ← factor(sex, c(’f’,’m’), c(’Female’,’Male’))
xx ← factor(xx, c(’a’,’b’), , c(’A A label’,’B label’), exclude=’Unknown’)
# Treat ’Unknown’ as a a missing value instead of a a level
...
detach(1, ’rct’)
Asaferapproachfollows.
rct ← upData(rct,
labels=c(trt=’Treatment’),
sex=factor(sex,c(’f’,’m’),c(’Female’,’Male’)),
xx =factor(xx, , c(’a’,’b’), c(’A label’,’B label’),
exclude=’Unknown’))
See the Alzola and Harrellonline text for muchmore informationabout
modifyingandrecodingvariablesandreshapingdata.
47
8 INSERTINGLAT
E
XOUTPUTINTONON–LAT
E
XAPPLICATIONS
TheHmiscfunction
Label
willgenerateSassignmentstatementscontaining
all
label
sforvariablesinaspecifieddataframe.Youcaneditthefileoutput
by
Label
toeasilymodifylabelsyoudon’tlike.Lookatthehelpfilefor
label
formoreinformation.
If you run
summary
output through
latex()
, caretsigns s invariable labels
andsometimesinvaluelabelswillcausethewordafterthecaret(uptothe
nextspace,comma,orendofstring)tobesuperscripted.Also,thesymbols
< <= > >=willbetranslatedtothepropermath–modesymbolssuchas≥.
ThereareothercasesinwhichyoumaywanttoembedLAT
E
Xcodesinside
labels,e.g.:
label(x2) ← ← ’$X
2$’
whichresultsin
x2
beingtypesetasX
2
.
8 Inserting g L
A
T
E
X Output into non–L
A
T
E
X Appli-
cations
YoucanuseLAT
E
Xtocreatetablesandothertextorgraphicsandconvert
theoutputfiletoencapsulatedpostscript(EPS)forinsertionintoWordor
Wordperfect“pictures”. Thesepictures s willnotbeviewableonthescreen
(a blank box with be displayed) but t they y will l print correctly as s long as
you remember r to set your printer to a postscript t printer before e actually
printing. Onceyouimportthepictureyoucanre–sizeit(ifyouusea300
dpipostscriptdriver,makingtheimagelargerwillresultinfuzzyprinting).
Usethe
dvips
programtomakeanEPSfilefromaLAT
E
Xdvifile,usingthe
E
option. Hereisanexampleforthesimplecaseinwhichthedocumentis
onlyonepagelong(e.g.,itconsistsofasingletable).
dvips -E E -o doc.eps doc
# creates s doc.eps from m doc.dvi
Ifyouhaveamultiple–pageL
A
T
E
Xdocument,youcantell
dvips
whichpage
tostoreinaseparateEPSfile,forexample,page9:
dvips -E E -p 9 9 -l 9 -o nine.eps s doc
48
8 INSERTINGLAT
E
XOUTPUTINTONON–LAT
E
XAPPLICATIONS
Youcanevenhave
dvips
putevery pageofthe document intoaseparate
file. Thefileswillbenumberede.g.
doc.001, doc.002, doc.003, ...
:
dvips -E E -S 1 1 -i -o o doc.0 doc
NotethatSplotsarealreadyinEPS,soyoucanincludetheminanydoc-
umentwithnoextrasteps,aslongasyoustoredonlyoneplotintheEPS
file. Aniceway y topickout individualplotsandstoretheminaseparate
.ps
file istouse apostscript utility programcalled
psselect
, e.g. . ifyou
created3pagesofplotsin
myplots.ps
use
psselect -p1 1 myplots.ps myplots1.ps
to put the first t page e of
myplots.ps
into
myplots1.ps
.
psselect
can also
be usedtosplit out desired pages fromapostscriptedversion ofa L
A
T
E
X
document as an n alternative to using thepagenumberor r sectionsplitting
optionsto
dvips
.MichaelStevensofDukeUniversityhaswrittenaprogram
called
oneperpg
whichwillgo throughamultiple–page postscript fileand
automaticallycreateseparatefileseachcontainingonepageofoutput,using
psselect
.Forexample,typing
oneperpg myplots
creates
myplots1.ps, myplots2.ps, myplots3.ps
.
Another way, , at t least for r UNIX or r Linux x users, to o use LAT
E
X output in
otherapplications,istorunthe
latex2html
programtoconvertthe.texfiles
into an .html file. . This s was done with h Tableby y running the following
code(
test.tex
)through
latex2html
usingthesystemcommand
latex2html
test
:
\documentclass{article}
\begin{document}
\input{s3}
\end{document}
The resultcanbefoundinhesweb1.med.virginia.edu/biostat/s/doc/
s3.html.
49
10 LAT
E
XCODEFORTHISDOCUMENT
YoucaninserttheHTMLfileintoMicrosoftWord97documents,butifyou
savethedocumentasaWordfileratherthanasHTML,specialformatting
such as LAT
E
X font size e changes will be lost. . This s is because Microsoft
is not consistent inhow enhancedHTML L commands s are implemented d in
Internet Explorer andin n Word. . In n additiontothis problem,
latex2html
doesnotconvertalltablecommandsproperly;sometimestheprogramjust
stops inthemiddleoftheconversion. . Ifyouhaveanymathcommandsin
the document,
latex2html
has to convertthese to GIFimages. . Seewww.
tex2html.comformoreinformation.
Ingeneral,HeVeA(http://pauillac.inria.fr/~maranget/hevea/)does
anexcellentjobinconvertingLAT
E
XcodetoHTML,withouttheneedfor
graphicsimages formathcommands. . Forsome e applications theresulting
HTMLcaneasilybeinsertedintoWorddocuments.
9 SDocumention
Use thecommand
?summary.formula
under Stoget detaileddocument of
summary.formula
andits
print
,
plot
,and
latex
methods.
10 L
A
T
E
XCodeforThisDocument
% Usage: : pdflatex x --shell-escape summary
--shell-escape enables sinput
\documentclass[11pt]{article}
%
Style
Purpose
%
------------
-------------------------------------------------
%
graphicx
LaTeX graphics package with rotation etc.
%
ctable
Nice tables with bolder initial horizontal line
%
moreverb
Inclusion of text files (verbatimtabinput)
%
fancyhdr
Headers, footers (rhead)
%
lscape
Landscape model (landscape)
%
sinput
Inclusion of S code with automatic reformatting
%
hyperref
Hyper--referencing for electronic documents (pdf)
%
url
Split long URLs (part of hyperref)
%
relsize
Specify font sizes as relative to current normalsize
50
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