Two-step estimates of covariate effects — tse

Estimates the multinomial logit of class membership on covariates with the measurement-model parameters held fixed at their Step-1 values (Bakk and Kuha 2018). Unlike the three-step estimators, the indicators enter the Step-2 likelihood directly, so no classification step is needed.

Usage

tse_twostep(object, formula, ref = 1, se = FALSE, control = NULL)

Arguments

object: A measurement model from tse_lca() (it must keep its data).
formula: One-sided covariate formula.
ref: Reference class of the multinomial logit.
se: Logical. If TRUE, the estimates and their standard errors (corrected for the Step-1 uncertainty) are obtained with the two-step estimator of multilevLCA, initialized at this model's classes; its measurement model is checked against object. If FALSE (default), only the estimates are computed, and the variance is NA.
control: Estimation settings; default: those of object.

Value

A tseLCA_twostep object (also a tseLCA_covariate).

References

Bakk, Z., & Kuha, J. (2018). Two-step estimation of models between latent classes and external variables. Psychometrika, 83(4), 871–892. doi:10.1007/s11336-017-9592-7

Examples

d <- generate_data(500, "high", "covariate", seed = 1)
m <- tse_lca(cbind(Y1, Y2, Y3, Y4, Y5, Y6) ~ 1, data = d, nclass = 3)
coef(tse_twostep(m, ~ Zp))
#> (Intercept):C2          Zp:C2 (Intercept):C3          Zp:C3 
#>      2.1934130     -0.9411383     -3.4524271      0.8971774