cluster.scores: Cluster Scores
In misty: Miscellaneous Functions 'T. Yanagida'

cluster.scores

R Documentation

Cluster Scores

Description

This function computes group means by default.

Usage

cluster.scores(data, ..., cluster,
               fun = c("mean", "sum", "median", "var", "sd", "min", "max"),
               expand = TRUE, append = TRUE, name = ".a", as.na = NULL,
               check = TRUE)

Arguments

`data`	a numeric vector for centering a predictor variable, or a data frame for centering more than one predictor variable.
`...`	an expression indicating the variable names in `data` e.g., `cluster.scores(dat, x1, x2, cluster = "cluster")`. Note that the operators `.`, `+`, `-`, `~`, `:`, `::`, and `!` can also be used to select variables, see 'Details' in the `df.subset` function.
`cluster`	a character string indicating the variable name of the cluster variable in `data`, or a vector representing the nested grouping structure (i.e., group or cluster variable).
`fun`	character string indicating the function used to compute group scores, default: `"mean"`.
`expand`	logical: if `TRUE` (default), vector of cluster scores is expanded to match the input vector `data`.
`append`	logical: if `TRUE` (default), cluster scores are appended to the data frame specified in the argument `data`.
`name`	a character string or character vector indicating the names of the computed variables. By default, variables are named with the ending `".a"` resulting in e.g. `"x1.a"` and `"x2.a"`. Variable names can also be specified using a character vector matching the number of variables specified in `data` (e.g., `name = c("cluster.x1", "cluster.x2")`).
`as.na`	a numeric vector indicating user-defined missing values, i.e. these values are converted to `NA` before conducting the analysis. Note that `as.na()` function is only applied to the argument `data`, but not to `cluster`.
`check`	logical: if `TRUE` (default), argument specification is checked.

Value

Returns a numeric vector or data frame containing cluster scores with the same length or same number of rows as data if expand = TRUE or with the length or number of rows as length(unique(cluster)) if expand = FALSE.

Author(s)

Takuya Yanagida takuya.yanagida@univie.ac.at

References

Hox, J., Moerbeek, M., & van de Schoot, R. (2018). Multilevel analysis: Techniques and applications (3rd. ed.). Routledge.

Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). Sage Publishers.

Examples

# Load data set "Demo.twolevel" in the lavaan package
data("Demo.twolevel", package = "lavaan")

# Example 1: Compute cluster means for 'y1' and expand to match the input 'y1'
cluster.scores(Demo.twolevel, y1, cluster = "cluster", append = FALSE)

# Alternative specification without using the '...' argument
cluster.scores(Demo.twolevel$y1, cluster = Demo.twolevel$cluster)

# Example 2: Compute standard deviation for each cluster
# and expand to match the input x
cluster.scores(Demo.twolevel, cluster = "cluster", fun = "sd")

# Example 3: Compute cluster means without expanding the vector
cluster.scores(Demo.twolevel, cluster = "cluster", expand = FALSE)

# Example 4: Compute cluster means for 'y1' and 'y2' and append to 'Demo.twolevel'
cluster.scores(Demo.twolevel, y1, y2, cluster = "cluster")

# Alternative specification without using the '...' argument
cbind(Demo.twolevel,
      cluster.scores(Demo.twolevel[, c("y1", "y2")], cluster = Demo.twolevel$cluster))

misty documentation built on April 4, 2025, 3:49 a.m.