descriptives: Describe the variables a model uses, or will use

In latents: Multilevel Latent Profile, Class and Transition Models

Describe the variables a model uses, or will use

Description

One row per variable, with the summaries you would look at before fitting and the one that decides whether a two-level model has anything to find: the intraclass correlation, the share of each variable's variance that lies between groups rather than within them.

Usage

descriptives(x, ...)

## S3 method for class 'data.frame'
descriptives(x, vars = NULL, id = NULL, by = NULL, ...)

## S3 method for class 'multilpa'
descriptives(x, by = NULL, ...)

## S3 method for class 'multilpa_transitions'
descriptives(x, by = NULL, ...)

## S3 method for class 'multilpa_covariates'
descriptives(x, by = NULL, ...)

Arguments

x	A data frame, or a fitted model of this package. A fit carries the columns it was built from, so vars and id are read from it.
...	Passed between methods.
vars	Character vector of column names to describe. Repeated names are described once. For a data frame, defaults to every numeric column that is not id.
id	Optional. The column identifying the groups rows are nested in. Supplying it adds n_groups and icc; omitting it leaves them out.
by	Optional. "profile" or "group_class" on a fitted model, or a column name on a data frame: describe each variable once per level of it, rather than once overall. Rows whose stratifier is NA are neither dropped nor folded into another level: they form their own stratum, labelled NA in the by column and reported last, so that the strata account for every input row exactly once. The stratifier is never compared with ==, so a missing value cannot select rows it does not belong to.

Details

An ICC near zero suggests little between-group mean variation in that variable. It does not rule out group classes that differ in other variables, profile prevalence, or other features of their distributions.

Value

A base data.frame, one row per variable, or one row per variable and by level, with the columns

variable

character: the column described.

n

integer: observations with a value.

n_missing

integer: observations without one.

mean,sd,min,max

numeric, and NA_real_ for a variable that is not numeric.

n_distinct

integer: distinct observed values, which is the useful summary where a mean is not.

n_groups,icc

present only when id is known. icc is the one-way random-effects estimate, and may be slightly negative when the between-group mean square falls below the within-group one; that is the estimator reporting no group structure, not an error. Rows with a missing group ID do not contribute to either statistic.

A by column comes first, named after what it splits on and holding that level's own value, NA for the stratum of rows whose stratifier is missing. For every variable, n + n_missing summed over the strata equals the number of input rows; that invariant is asserted before the table is returned. Describing by a class the model assigned keeps that assignment out of the described frame, so an indicator of the caller's own that is named profile or group_class is summarized as itself, not replaced by the assignment. A by whose name is one of the summary's own columns cannot be told apart from them and raises latents_bad_data.

References

Bliese, P. D. (2000). Within-group agreement, non-independence, and reliability. In K. J. Klein & S. W. J. Kozlowski (Eds.), Multilevel theory, research, and methods in organizations.

See Also

multilpa() to fit, diagnostics() for the after-the-fit counterpart.

Examples

descriptives(
  course_engagement,
  vars = c("browse", "lectures", "forum_read", "forum_post", "attendance"),
  id = "student"
)

# A missing stratifier is a stratum of its own, not a silent loss and not
# missingness invented in a variable that has none.
descriptives(data.frame(cohort = c("A", "A", "B", NA), score = 1:4),
             vars = "score", by = "cohort")

fit <- multilpa(
  course_engagement,
  vars = c("browse", "lectures", "forum_read", "forum_post", "attendance"),
  id = "student", n_profiles = 2, n_group_classes = 2, n_starts = 4,
  seed = 1
)
descriptives(fit)
descriptives(fit, by = "profile")

latents documentation built on Oct. 16, 2026, 5:09 p.m.