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Relate latent classes to a distal outcome (Step 3)

Source: R/tse_structural.R
tse_distal.Rd

Estimates the class-specific distribution of a distal outcome, correcting for the classification error of the Step-2 class assignments (Bakk, Tekle, and Vermunt 2013). Given a covariate model from tse_covariate(), the class prior depends on the covariates and the covariate-model uncertainty is propagated to the distal estimates.

Usage

tse_distal(
  object,
  formula,
  family = "gaussian",
  method = NULL,
  se = NULL,
  control = NULL,
  data = NULL
)

Arguments

object

A classification from tse_classify(), or a covariate model from tse_covariate() (combined model).

formula

Zo ~ 1, with the distal outcome on the left-hand side.

family

Distribution of the outcome within classes: "gaussian" (default), "poisson", "binomial", "multinomial" (nominal outcome), or the corresponding family object (gaussian(), poisson(), binomial(); canonical links only).

method, se

As for tse_covariate(). For a combined model they default to those of the covariate model.

control

Estimation settings; default: those of object.

data

Optional data frame with the distal outcome, as in tse_covariate().

Value

A tseLCA_distal object, or a tseLCA_both object when object is a covariate model.

Details

The class-specific parameters are means (gaussian, with a common within-class variance, reported as $sigma2), log means (poisson), logits (binomial), or category probabilities ("multinomial"). The estimators and standard errors are as for tse_covariate(). Use omnibus_test() to test whether the outcome differs across classes.

References

Bakk, Z., Tekle, F. B., & Vermunt, J. K. (2013). Estimating the association between latent class membership and external variables using bias-adjusted three-step approaches. Sociological Methodology, 43(1), 272–311. doi:10.1177/0081175012470644

Examples

d <- generate_data(500, "high", "distal", seed = 2)
m <- tse_lca(cbind(Y1, Y2, Y3, Y4, Y5, Y6) ~ 1, data = d, nclass = 3)
fd <- tse_distal(tse_classify(m, assignment = "proportional"), Zo ~ 1)
summary(fd)
#> Three-step latent class model: distal outcome
#>   Classes: 3   Estimator: ML   Family: gaussian   N: 500
#>   Log-lik: -2220.3460 (df = 24)   AIC: 4488.69   BIC: 4589.84
#> 
#> Distal outcome means by class:
#>       Estimate Std. Error z value Pr(>|z|)    
#> mu_C1 -0.96436    0.09113 -10.582   <2e-16 ***
#> mu_C2  1.02629    0.07548  13.596   <2e-16 ***
#> mu_C3  0.10413    0.09979   1.044    0.297    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
omnibus_test(fd)
#> 
#> 	Wald test of equal distal outcome distributions across latent classes
#> 
#> data:  gaussian distal outcome, 3 classes
#> W = 278.59, df = 2, p-value < 2.2e-16
#>