seqaddNA: Generation of missing data under the form of gaps, which is...

In seqimpute: Imputation of Missing Data in Sequence Analysis

Generation of missing data under the form of gaps, which is the typical form of missing data with longitudinal data. A missing completely at random (MAR) mechanism is used.

Description

Generation of missing data under the form of gaps, which is the typical form of missing data with longitudinal data. A missing completely at random (MAR) mechanism is used.

Usage

seqaddNA(
  data,
  states.high = NULL,
  propdata = 1,
  pstart.high = 0.1,
  pstart.low = 0.005,
  maxgap = 3
)

Arguments

data	either a data frame containing sequences of a multinomial variable with missing data (coded as NA) or a state sequence object built with the TraMineR package
states.high	list of states that will have a larger probability to trigger a subsequent gap of missing data
propdata	proportion of the observations on which missing data will be simulated
pstart.high	probability to start a missing data for the specified states
pstart.low	probability to start a missing data for the other states
maxgap	maximum length of a gap of missing data

Value

Returns either a data frame or a state sequence object, depending the type of data that was provided to the function

seqimpute documentation built on March 19, 2024, 3:09 a.m.