Classify new observations with a fitted model — predict.multilpa • latents

Evaluates the fitted mixture on new rows: the E-step of the fit, at its estimates, applied to data it has not seen. Rows go through the fit's own preparation – the same categorical levels, the same centering, the same missing-data handling – so a prediction on the training data reproduces the fit's own posteriors exactly.

Usage

# S3 method for class 'multilpa'
predict(object, newdata = NULL, type = c("class", "posterior", "density"), ...)

Arguments

object: A fit from multilpa() or multilca() without membership covariates.
newdata: NULL for the fitted rows, or a data frame with the fit's indicator columns (and its id column for a two-level fit).
type: "class": the modal profile and its probability, and the modal group class. "posterior": the same with every profile's probability. "density": each row's log density under the fitted mixture, marginal over the group classes (the density of a row from a new group), for scoring, outlier detection or held-out likelihood.
...: Unused.

Value

A base data.frame with one row per row of newdata: row (its position in newdata), the id column for a two-level fit, and

for "class": profile (0 is the noise component of a noise = TRUE fit) and its posterior, and for two-level fits group_class and group_posterior;
for "posterior": those columns and one probability_profile_k column per profile (and probability_noise);
for "density": log_density.

Groups

A two-level model classifies a row using its group: the group class is inferred from all of that group's rows, and it shifts the row's profile probabilities. The groups in newdata (the fit's id column) are treated as new groups, each classified from its own rows alone, which is the case of applying a model to a new cohort. A single-level fit, or a fit given id = NULL, treats every row as its own unit.

Conditions

latents_bad_data when newdata lacks an indicator or the id column, has a missing value the fit cannot handle (missing = "error"), a category the fit never saw, or a non-numeric continuous indicator.

Examples

activity <- c("browse", "lectures", "forum_read")
training <- subset(course_engagement, student <= 60)
fit <- multilpa(training, activity, id = "student", n_profiles = 2,
                n_group_classes = 2, n_starts = 2, seed = 1)
new_students <- subset(course_engagement, student > 100)
head(predict(fit, new_students))
#>   row student profile posterior group_class group_posterior
#> 1   1     101       1 0.9999700           2       0.9999999
#> 2   2     101       1 0.9386987           2       0.9999999
#> 3   3     101       1 0.9855908           2       0.9999999
#> 4   4     101       1 0.9999469           2       0.9999999
#> 5   5     101       1 0.9998584           2       0.9999999
#> 6   6     101       1 0.9982339           2       0.9999999
head(predict(fit, new_students, type = "density"))
#>   row log_density
#> 1   1   -4.079463
#> 2   2   -3.333001
#> 3   3   -4.768241
#> 4   4   -3.541111
#> 5   5   -2.854424
#> 6   6   -3.886470