lst_models_es: Estimate latent state-trait models for experience sampling...

In amayer2010/lsttheory: Latent State-Trait Theory

Estimate latent state-trait models for experience sampling data

Description

This function can be used to estimate various latent state-trait models (LST models) for experience sampling data. It is based on the revised version of the LST theory presented in Steyer, Mayer, Geiser & Cole (2015) and on Eid et al. (2017). The function is a wrapper for lst_models_es_common_trait() and lst_models_es_indicator_specific_trait().

Usage

lst_models_es(
  traitmodel,
  ntimepoints,
  data,
  nperiods = 1,
  equiv = "invar",
  ar = TRUE,
  manifest_thetacovariates = NULL,
  ...
)

Arguments

traitmodel	character. Can be one of c("singletrait", "day-specific", "indicator-specific", "day-and-indicator-specific")
ntimepoints	integer. Number of measurement occasions
data	a data.frame. This data frame contains the observed variables, sorted by time t and then by indicator i, i.e., Y11, Y21, Y31, ... Y12, Y22, Y32 ... Y15, Y25, Y35 ... etc.
nperiods	integer. Number of periods (trait periods, zeta periods, and epsilon periods).
equiv	character. Equivalence assumption. Can be one of c("invar", "period.invar", "free")
ar	logical. Should autoregressive effects be included
manifest_thetacovariates	an optional vector with variable names of manifest covariates which further explain the latent traits. Must be assessed at a single occasion.
...	further arguments passed to lower level functions

Value

object of class lstmodel

Examples

m1 <- lst_models_es(traitmodel="singletrait", ntimepoints=9, 
data=d_lst_es, nperiods=3, ar=FALSE, equiv="invar")

print(m1)

m2 <- lst_models_es(traitmodel="indicator-specific", ntimepoints=9,
data=d_lst_es, nperiods=3, ar=FALSE, equiv="invar")

print(m2)

amayer2010/lsttheory documentation built on March 1, 2025, 11:30 a.m.