sirt: categorize – R documentation

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categorize

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

Supplementary Item Response Theory Models

v3.10-118

GPL (>= 2)

Authors

Alexander Robitzsch [aut,cre] (<https://orcid.org/0000-0002-8226-3132>)

Initial release

2021-09-22 17:45:34