Response Pattern in a Binary Matrix

Computes different statistics of the response pattern in a binary matrix.

Usage

md.pattern.sirt(dat)

Arguments

dat

A binary data matrix

Value

A list with following entries:

dat

Original dataset

dat.resp1

Indices for responses of 1's

dat.resp0

Indices for responses of 0's

resp_patt

Vector of response patterns

unique_resp_patt

Unique response patterns

unique_resp_patt_freq

Frequencies of unique response patterns

unique_resp_patt_firstobs

First observation in original dataset dat of a unique response pattern

freq1

Frequencies of 1's

freq0

Frequencies of 0's

dat.ordered

Dataset according to response patterns

See also

See also the md.pattern function in the mice package.

Examples

#############################################################################
# EXAMPLE 1: Response patterns
#############################################################################
set.seed(7654)
N <- 21         # number of rows
I <- 4          # number of columns
dat <- matrix( 1*( stats::runif(N*I) > .3 ), N, I )
res <- sirt::md.pattern.sirt(dat)
# plot of response patterns
res$dat.ordered
image( z=t(res$dat.ordered), y=1:N, x=1:I, xlab="Items", ylab="Persons")
# 0's are yellow and 1's are red

#############################################################################
# EXAMPLE 2: Item response patterns for dataset data.read
#############################################################################

data(data.read)
dat <- data.read  ; N <- nrow(dat) ; I <- ncol(dat)
# order items according to p values
dat <- dat[, order(colMeans(dat, na.rm=TRUE )) ]
# analyzing response pattern
res <- sirt::md.pattern.sirt(dat)
res$dat.ordered
image( z=t(res$dat.ordered), y=1:N, x=1:I, xlab="Items", ylab="Persons")