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
varsandidare 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_groupsandicc; 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 isNAare neither dropped nor folded into another level: they form their own stratum, labelledNAin thebycolumn 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.
Value
A base data.frame, one row per variable, or one row per variable
and by level, with the columns
variablecharacter: the column described.
ninteger: observations with a value.
n_missinginteger: observations without one.
mean,sd,min,maxnumeric, and
NA_real_for a variable that is not numeric.n_distinctinteger: distinct observed values, which is the useful summary where a mean is not.
n_groups,iccpresent only when
idis known.iccis 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.
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.
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"
)
#> variable n n_missing mean sd min max n_distinct
#> 1 browse 1422 0 -2.812940e-05 0.9890804 -3.14 2.49 398
#> 2 lectures 1422 0 -4.219409e-05 0.9889178 -2.91 3.22 399
#> 3 forum_read 1422 0 7.032349e-05 0.9890236 -2.73 2.81 400
#> 4 forum_post 1422 0 -2.109705e-05 0.9890038 -2.61 3.10 397
#> 5 attendance 1422 0 7.735584e-05 0.9889426 -2.45 2.41 401
#> n_groups icc
#> 1 106 0.19229259
#> 2 106 0.08779169
#> 3 106 0.22403384
#> 4 106 0.15034618
#> 5 106 0.22145308
# 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")
#> cohort variable n n_missing mean sd min max n_distinct
#> 1 A score 2 0 1.5 0.7071068 1 2 2
#> 2 B score 1 0 3.0 NA 3 3 1
#> 3 <NA> score 1 0 4.0 NA 4 4 1
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)
#> variable n n_missing mean sd min max n_distinct
#> 1 browse 1422 0 -2.812940e-05 0.9890804 -3.14 2.49 398
#> 2 lectures 1422 0 -4.219409e-05 0.9889178 -2.91 3.22 399
#> 3 forum_read 1422 0 7.032349e-05 0.9890236 -2.73 2.81 400
#> 4 forum_post 1422 0 -2.109705e-05 0.9890038 -2.61 3.10 397
#> 5 attendance 1422 0 7.735584e-05 0.9889426 -2.45 2.41 401
#> n_groups icc
#> 1 106 0.19229259
#> 2 106 0.08779169
#> 3 106 0.22403384
#> 4 106 0.15034618
#> 5 106 0.22145308
descriptives(fit, by = "profile")
#> profile variable n n_missing mean sd min max n_distinct
#> 1 1 browse 836 0 0.5422249 0.7644929 -2.31 2.49 285
#> 2 1 lectures 836 0 0.4279785 0.9174987 -2.13 3.22 325
#> 3 1 forum_read 836 0 0.6232895 0.6895798 -1.50 2.81 273
#> 4 1 forum_post 836 0 0.5305502 0.8280756 -2.16 3.10 307
#> 5 1 attendance 836 0 0.6477632 0.6194911 -1.03 2.41 251
#> 6 2 browse 586 0 -0.7736177 0.7227891 -3.14 1.52 254
#> 7 2 lectures 586 0 -0.6106655 0.7339385 -2.91 1.43 250
#> 8 2 forum_read 586 0 -0.8890273 0.5919444 -2.73 1.15 220
#> 9 2 forum_post 586 0 -0.7569454 0.6488317 -2.61 1.59 228
#> 10 2 attendance 586 0 -0.9239249 0.6108514 -2.45 0.83 230
#> n_groups icc
#> 1 98 0.0162963525
#> 2 98 0.0006795179
#> 3 98 -0.0086763458
#> 4 98 -0.0073164254
#> 5 98 0.0107329239
#> 6 93 -0.0078959550
#> 7 93 0.0254729179
#> 8 93 -0.0101547583
#> 9 93 -0.0135790046
#> 10 93 -0.0088693797