sirt (version 1.14-0)

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 entriesFor 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 <- categorize( dat , categorical=categorical )
# 
# # (2) multiple imputation using the mice package
# dat2 <- res$data
# VV <- ncol(dat2)
# impMethod <- rep( "sample" , VV )    # define random sampling imputation method
# names(impMethod) <- colnames(dat2)
# imp <- mice::mice( as.matrix(dat2) , impMethod = impMethod , maxit=1 , m=1 )
# dat3 <- mice::complete(imp,action=1)
# 
# # (3) decategorize dataset
# dat3a <- decategorize( dat3 , categ_design = res$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 <- categorize( dat , categorical = categorical , quant=quant)
# str(res)
# ## End(Not run)