Hmisc (version 5.3-1)

mChoice: Methods for Storing and Analyzing Multiple Choice Variables

Description

mChoice is a function that is useful for grouping variables that represent individual choices on a multiple choice question. These choices are typically factor or character values but may be of any type. Levels of component factor variables need not be the same; all unique levels (or unique character values) are collected over all of the multiple variables. Then a new character vector is formed with integer choice numbers separated by semicolons. Optimally, a database system would have exported the semicolon-separated character strings with a levels attribute containing strings defining value labels corresponding to the integer choice numbers. mChoice is a function for creating a multiple-choice variable after the fact. mChoice variables are explicitly handed by the describe and summary.formula functions. NAs or blanks in input variables are ignored.

format.mChoice will convert the multiple choice representation to text form by substituting levels for integer codes. as.double.mChoice converts the mChoice object to a binary numeric matrix, one column per used level (or all levels of drop=FALSE. This is called by the user by invoking as.numeric. There is a print method and a summary method, and a print method for the summary.mChoice object. The summary method computes frequencies of all two-way choice combinations, the frequencies of the top 5 combinations, information about which other choices are present when each given choice is present, and the frequency distribution of the number of choices per observation. This summary output is used in the describe function. The print method returns an html character string if options(prType='html') is in effect if render=FALSE or renders the html otherwise. This is used by print.describe and is most effective when short=TRUE is specified to summary.

in.mChoice creates a logical vector the same length as x whose elements are TRUE when the observation in x contains at least one of the codes or value labels in the second argument.

match.mChoice creates an integer vector of the indexes of all elements in table which contain any of the speicified levels

nmChoice returns an integer vector of the number of choices that were made

is.mChoice returns TRUE is the argument is a multiple choice variable.

Usage

mChoice(..., label='',
        sort.levels=c('original','alphabetic'), 
        add.none=FALSE, drop=TRUE, ignoreNA=TRUE)
# S3 method for mChoice
format(x, minlength=NULL, sep=";", ...)
# S3 method for mChoice
as.double(x, drop=FALSE, ...)
# S3 method for mChoice
print(x, quote=FALSE, max.levels=NULL,
       width=getOption("width"), ...)
# S3 method for mChoice
as.character(x, ...)
# S3 method for mChoice
summary(object, ncombos=5, minlength=NULL,
  drop=TRUE, short=FALSE, ...)
# S3 method for summary.mChoice
print(x, prlabel=TRUE, render=TRUE, ...)
# S3 method for mChoice
[(x, ..., drop=FALSE)
match.mChoice(x, table, nomatch=NA, incomparables=FALSE)
inmChoice(x, values, condition=c('any', 'all'))
inmChoicelike(x, values, condition=c('any', 'all'),
              ignore.case=FALSE, fixed=FALSE)
nmChoice(object)
is.mChoice(x)
# S3 method for mChoice
Summary(..., na.rm)

Value

mChoice returns a character vector of class "mChoice"

plus attributes "levels" and "label".

summary.mChoice returns an object of class

"summary.mChoice". inmChoice and inmChoicelike

return a logical vector.

format.mChoice returns a character vector, and

as.double.mChoice returns a binary numeric matrix.

nmChoice returns an integer vector.

print.summary.mChoice returns an html character string if

options(prType='html') is in effect.

See Also

label, combplotp

Examples

options(digits=3)
set.seed(3)
n <- 20
sex <- factor(sample(c("m","f"), n, rep=TRUE))
age <- rnorm(n, 50, 5)
treatment <- factor(sample(c("Drug","Placebo"), n, rep=TRUE))


# Generate a 3-choice variable; each of 3 variables has 5 possible levels
symp <- c('Headache','Stomach Ache','Hangnail',
          'Muscle Ache','Depressed')
symptom1 <- sample(symp, n, TRUE)
symptom2 <- sample(symp, n, TRUE)
symptom3 <- sample(symp, n, TRUE)
cbind(symptom1, symptom2, symptom3)[1:5,]
Symptoms <- mChoice(symptom1, symptom2, symptom3, label='Primary Symptoms')
Symptoms
print(Symptoms, long=TRUE)
format(Symptoms[1:5])
inmChoice(Symptoms,'Headache')
inmChoicelike(Symptoms, 'head', ignore.case=TRUE)
levels(Symptoms)
inmChoice(Symptoms, 3)
# Find all subjects with either of two symptoms
inmChoice(Symptoms, c('Headache','Hangnail'))
# Note: In this example, some subjects have the same symptom checked
# multiple times; in practice these redundant selections would be NAs
# mChoice will ignore these redundant selections
# Find all subjects with both symptoms
inmChoice(Symptoms, c('Headache', 'Hangnail'), condition='all')

meanage <- N <- numeric(5)
for(j in 1:5) {
 meanage[j] <- mean(age[inmChoice(Symptoms,j)])
 N[j] <- sum(inmChoice(Symptoms,j))
}
names(meanage) <- names(N) <- levels(Symptoms)
meanage
N

# Manually compute mean age for 2 symptoms
mean(age[symptom1=='Headache' | symptom2=='Headache' | symptom3=='Headache'])
mean(age[symptom1=='Hangnail' | symptom2=='Hangnail' | symptom3=='Hangnail'])

summary(Symptoms)

#Frequency table sex*treatment, sex*Symptoms
summary(sex ~ treatment + Symptoms, fun=table)
# Check:
ma <- inmChoice(Symptoms, 'Muscle Ache')
table(sex[ma])

# could also do:
# summary(sex ~ treatment + mChoice(symptom1,symptom2,symptom3), fun=table)

#Compute mean age, separately by 3 variables
summary(age ~ sex + treatment + Symptoms)


summary(age ~ sex + treatment + Symptoms, method="cross")

f <- summary(treatment ~ age + sex + Symptoms, method="reverse", test=TRUE)
f
# trio of numbers represent 25th, 50th, 75th percentile
print(f, long=TRUE)