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Coefficients of a fitted tseLCA model

Source: R/methods-tseLCA.R
coef.tseLCA_structural.Rd

Returns a named coefficient vector whose names match the rows and columns of vcov(), so that stats::confint() and other generic tools work.

Usage

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

# S3 method for class 'tseLCA_measurement'
coef(object, ...)

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).

matrix

Logical. If TRUE, return the coefficients in their natural matrix layout: (Q+1) x (T-1) for covariate models, T x C for multinomial distal outcomes, a list of both for tseLCA_both.

...

Further arguments (currently unused).

Value

A named numeric vector (or matrix / list if matrix = TRUE).

Details

  • Measurement models: class-size log-ratios \(\log(\pi_t/\pi_1)\) and item-response log-ratios \(\log(P(Y=k \mid t)/P(Y=0 \mid t))\). Use class_sizes() and item_probs() for the probability scale.

  • Covariate models: multinomial-logit coefficients named "<covariate>:<class>", e.g. "Zp:C2".

  • Distal-outcome models: class-specific means (gaussian), log means (poisson), logits (binomial), or category probabilities (multinomial, named "<class>:<category>").

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)
#> Warning: three_step() is deprecated as of tseLCA 2.0.0; use tseLCA() or the step-wise tse_lca(), tse_classify(), tse_covariate(), and tse_distal(). (Shown once per session; see NEWS for the new interface.)
coef(fit)
#> (Intercept):C2          Zp:C2 (Intercept):C3          Zp:C3 
#>      2.2333837     -1.1569878     -3.2742157      0.9400712 
coef(fit, matrix = TRUE)
#>                    C2         C3
#> (Intercept)  2.233384 -3.2742157
#> Zp          -1.156988  0.9400712
confint(fit)
#>                     2.5 %     97.5 %
#> (Intercept):C2  1.0068081  3.4599593
#> Zp:C2          -1.7453032 -0.5686724
#> (Intercept):C3 -4.6837138 -1.8647177
#> Zp:C3           0.5684972  1.3116452