gicc: Generalized intraclass correlation coefficient for multilevel...

In gmonette/spida15: Collection of miscellaneous functions for mixed models etc. prepared for SPIDA 2009+ (development version)

Description

gicc facilitates the identification of between-subject variables versus balanced within-subject variables with hierarchical data. Methods handle factors, character variables and numerical variables. For categorical variables, Goodman-Kruskal's tau is returned and for numerical variables, the simple ICC with (variance between) / (variance between + variance within) applied to the rank (by default) of the variable. In either case, a value of 1 signifies a variable that is constant within clusters and a value of 0, a variable that is perfectly balanced within clusters.

Usage

gicc(x, by, method = 'rank', ...)

Arguments

x	a data frame, factor, character variable or a numerical variable
by	if x is a data frame, by can be a formula (e.g. ~id) evaluated in x. Otherwise, by is a variable defining clusters.
method	a character string indicating whether to work with the rank of the raw variable, x, stripped of missing values, with the raw variable itself, or with is.na(x). Can be one of "rank", raw" or "na".
...	– not used

Value

a measure of relative variability within clusters so that 1 represents no variability and 0 perfect balance.

Author(s)

G. Monette <[e-mail]>

Examples

  gicc( hs, ~ school)
  gicc( hs, ~ school, method = 'raw')

gmonette/spida15 documentation built on May 17, 2019, 7:26 a.m.