Assign observations to latent classes (Step 2)

Computes posterior class-membership probabilities from a fitted measurement model, assigns observations to classes, and estimates the classification-error probabilities \(D_{ts} = P(W = s \mid X = t)\) between the true class \(X\) and the assigned class \(W\). These error probabilities are what the bias-adjusted Step-3 estimators (BCH and ML) correct for.

Usage

tse_classify(
  object,
  newdata = NULL,
  assignment = c("modal", "proportional"),
  control = NULL
)

# S3 method for class 'tseLCA_classify'
print(x, digits = max(3L, getOption("digits") - 4L), ...)

Arguments

object: A measurement model from tse_lca() (two or more classes).
newdata: Optional data frame to classify. Omitted: the data the measurement model was estimated on.
assignment: "modal" (default): each observation is assigned to its most likely class. "proportional": each observation is assigned to every class with its posterior probability as weight.
control: Estimation settings; default: those of object. See tse_control().
x: A tseLCA_classify object.
digits: Number of significant digits to print.
...: Unused.

Value

A tseLCA_classify object with components posteriors (n x T), classifications (modal classes), weights (the assignment weights \(P(W = s \mid Y_i)\)), D (the T x T classification-error matrix), entropy.R2 (computed from these posteriors), and data. Pass it to the Step-3 functions.

Details

The measurement model is held fixed. With newdata, observations from another sample are classified with it, e.g. to relate the classes to covariates observed only in a subsample; the uncertainty of the measurement model is then still that of the sample it was estimated on.

References

Vermunt, J. K. (2010). Latent class modeling with covariates: Two improved three-step approaches. Political Analysis, 18(4), 450–469. doi:10.1093/pan/mpq025

Examples

d <- generate_data(500, "high", "covariate", seed = 1)
m <- tse_lca(cbind(Y1, Y2, Y3, Y4, Y5, Y6) ~ 1, data = d, nclass = 3)
cl <- tse_classify(m)
cl
#> Latent class assignment (Step 2)
#>   Classes: 3   Assignment: modal   N: 500   Entropy R²: 0.8782
#> 
#> Classification error probabilities P(W = s | X = t)
#> (rows: true class X; columns: assigned class W)
#>       W=C1  W=C2  W=C3
#> X=C1 0.963 0.032 0.005
#> X=C2 0.040 0.942 0.018
#> X=C3 0.004 0.029 0.968
#> 
#> Class proportions: estimated (measurement model) and assigned
#>              C1    C2    C3
#> estimated 0.357 0.331 0.312
#> assigned  0.358 0.332 0.310
tse_classify(m, assignment = "proportional")
#> Latent class assignment (Step 2)
#>   Classes: 3   Assignment: proportional   N: 500   Entropy R²: 0.8782
#> 
#> Classification error probabilities P(W = s | X = t)
#> (rows: true class X; columns: assigned class W)
#>       W=C1  W=C2  W=C3
#> X=C1 0.935 0.058 0.007
#> X=C2 0.063 0.899 0.038
#> X=C3 0.007 0.040 0.952
#> 
#> Class proportions: estimated (measurement model) and assigned
#>              C1    C2    C3
#> estimated 0.357 0.331 0.312
#> assigned  0.357 0.331 0.312

# classify another sample with the same measurement model
tse_classify(m, newdata = d[1:200, ])
#> Latent class assignment (Step 2)
#>   Classes: 3   Assignment: modal   N: 200   Entropy R²: 0.8703
#> 
#> Classification error probabilities P(W = s | X = t)
#> (rows: true class X; columns: assigned class W)
#>       W=C1  W=C2  W=C3
#> X=C1 0.958 0.036 0.006
#> X=C2 0.048 0.945 0.007
#> X=C3 0.000 0.041 0.959
#> 
#> Class proportions: estimated (measurement model) and assigned
#>              C1    C2    C3
#> estimated 0.357 0.331 0.312
#> assigned  0.360 0.345 0.295