lsttheory: lsttheory

In amayer2010/lsttheory: Latent State-Trait Theory

lsttheory

Description

Compute several models of latent state-trait theory..

This function is the main funtion of the package and can be used to estimate various latent state-trait models (LST models). It is based on the revised version of the LST theory presented in Steyer, Mayer, Geiser & Cole (2015).

Usage

lsttheory(
  neta,
  ntheta = 0,
  data,
  addsyntax = "",
  equiv.assumption = list(tau = "cong", theta = "cong"),
  scale.invariance = list(lait0 = FALSE, lait1 = FALSE, lat0 = FALSE, lat1 = FALSE),
  ...
)

Arguments

neta	integer. Number of latent state variables eta.
ntheta	integer. Number of latent trait variables theta.
data	a data frame. This data frame only contains the observables, which will all be used to fit the LST-R model. The order of the observables Y_it should be by time t and then by indicator i, i.e., Y_11, Y_21, ..., Y_12, Y_22, ... and so forth
addsyntax	character string. Will be added to generated lavaan syntax.
equiv.assumption	list of equivalence assumptions for tau variables (tau) and theta variables. Each can be one of c("equi","ess","cong"), for equivalence ("equi"), essential equivalence ("ess"), or congenericity ("cong").
scale.invariance	list of invariance assumtions for lambda_it and lambda_t parameters
...	further arguments passed to lavaan::sem().

Value

object of class LSTModel.

References

Steyer, R., Mayer, A., Geiser, C., & Cole, D. A. (2015). A theory of states and traits - revised. Annual Review of Clinical Psychology.

Examples

m1 <- lsttheory(neta=4, ntheta=2, data=d_multitraitmultistate, 
 equiv.assumption=list(tau="cong", theta="cong"), 
 scale.invariance=list(lait0=TRUE,lait1=TRUE,lat0=TRUE,lat1=TRUE))
 
print(m1)

amayer2010/lsttheory documentation built on Nov. 3, 2023, 1:30 a.m.