Variance-covariance matrix of a fitted tseLCA model — vcov.tseLCA

Row and column names match coef().

Usage

# S3 method for class 'tseLCA_structural'
vcov(
  object,
  component = c("all", "covariate", "distal"),
  step = c("three_step", "two_step"),
  ...
)

# S3 method for class 'tseLCA_measurement'
vcov(object, boundary.tol = 0.01, ...)

Arguments

object: A fitted tseLCA object.
component: For tseLCA_both objects: "all" (default; covariate then distal coefficients), "covariate", or "distal".
step: "three_step" (default) or "two_step" (the two-step estimates used to initialize Step 3; covariate models only).
...: Further arguments (currently unused).
boundary.tol: Measurement models only: parameters within this tolerance of 0 or 1 are treated as fixed. Default 1e-2.

Value

A named square matrix.

Details

Measurement models: the BHHH variance matrix of the log-ratio parameters (attribute "parameterization" records the scale).
Covariate and distal-outcome models: the Step-3 variance matrix, which includes the correction for Step-1 uncertainty unless the model was fitted with use.simple.cov = TRUE. For family = "multinomial" it is on the probability scale and rank-deficient (each class's probabilities sum to one).
tseLCA_both with component = "all": the covariate and distal blocks on the diagonal; the cross-covariances between the two sets of parameters are not computed and are NA.

Examples

d   <- generate_data(200, "high", "covariate", seed = 1)
fit <- three_step(d, paste0("Y", 1:6), n_classes = 3,
                  Zp.names = "Zp", use.simple.cov = TRUE)
vcov(fit)
#>                (Intercept):C2        Zp:C2 (Intercept):C3        Zp:C3
#> (Intercept):C2    0.391644881 -0.173583653    0.001643327 -0.002599746
#> Zp:C2            -0.173583653  0.090099886    0.016315352 -0.002300251
#> (Intercept):C3    0.001643327  0.016315352    0.517169347 -0.130664301
#> Zp:C3            -0.002599746 -0.002300251   -0.130664301  0.035941355