categorize: Categorize and Decategorize Variables in a Data Frame

In sirt: Supplementary Item Response Theory Models

Categorize and Decategorize Variables in a Data Frame

Description

The function categorize defines categories for variables in a data frame, starting with a user-defined index (e.g. 0 or 1). Continuous variables can be categorized by defining categories by discretizing the variables in different quantile groups.

The function decategorize does the reverse operation.

Usage

categorize(dat, categorical=NULL, quant=NULL, lowest=0)

decategorize(dat, categ_design=NULL)

Arguments

dat	Data frame
categorical	Vector with variable names which should be converted into categories, beginning with integer lowest
quant	Vector with number of classes for each variables. Variables are categorized among quantiles. The vector must have names containing variable names.
lowest	Lowest category index. Default is 0.
categ_design	Data frame containing informations about categorization which is the output of categorize.

Value

For categorize, it is a list with entries

data	Converted data frame
categ_design	Data frame containing some informations about categorization

For decategorize it is a data frame.

Examples

## Not run: 
library(mice)
library(miceadds)

#############################################################################
# EXAMPLE 1: Categorize questionnaire data
#############################################################################

data(data.smallscale, package="miceadds")
dat <- data.smallscale

# (0) select dataset
dat <- dat[, 9:20 ]
summary(dat)
categorical <- colnames(dat)[2:6]

# (1) categorize data
res <- sirt::categorize( dat, categorical=categorical )

# (2) multiple imputation using the mice package
dat2 <- res$data
VV <- ncol(dat2)
impMethod <- rep( "sample", VV )    # define random sampling imputation method
names(impMethod) <- colnames(dat2)
imp <- mice::mice( as.matrix(dat2), impMethod=impMethod, maxit=1, m=1 )
dat3 <- mice::complete(imp,action=1)

# (3) decategorize dataset
dat3a <- sirt::decategorize( dat3, categ_design=res$categ_design )

#############################################################################
# EXAMPLE 2: Categorize ordinal and continuous data
#############################################################################

data(data.ma01,package="miceadds")
dat <- data.ma01
summary(dat[,-c(1:2)] )

# define variables to be categorized
categorical <- c("books", "paredu" )
# define quantiles
quant <-  c(6,5,11)
names(quant) <- c("math", "read", "hisei")

# categorize data
res <- sirt::categorize( dat, categorical=categorical, quant=quant)
str(res)

## End(Not run)

sirt documentation built on May 29, 2024, 8:43 a.m.