seqmissimplic: Identification and visualization of states that best...

In seqimpute: Imputation of Missing Data in Sequence Analysis

Identification and visualization of states that best characterize sequences with missing data

Description

Function based on the seqimplic. Identification and visualization of the states that best characterize the sequence with missing data vs. the sequences without missing data at each position (time point). See the seqimplic help for more details on how it works.

Usage

seqmissimplic(data, var = NULL, ...)

Arguments

data	a data frame where missing data are coded as NA or a state sequence object built with seqdef function
var	the list of columns containing the trajectories. Default is NULL, i.e. all the columns.
...	parameters to be passed to the seqimplic function

Value

returns a seqimplic object that can be plotted and printed.

Author(s)

Kevin Emery

Examples

# For illustration purpose, we simulate missing data on the mvad dataset,
# available in the TraMineR package. The state "joblessness" state has a 
# higher probability of triggering a missing gap

## Not run: 
data(mvad, package = "TraMineR")
mvad.miss <- seqaddNA(mvad, var = 17:86, states.high = "joblessness")

# The states that best characterize sequences with missing data
implic <- seqmissimplic(mvad.miss, var = 17:86)

# Visualization of the results
plot(implic)

## End(Not run)

seqimpute documentation built on May 29, 2024, 4:35 a.m.