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

cluster.scores

R Documentation

Cluster Scores

Description

This function is used to compute group means by default.

Usage

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

Arguments

`x`	a numeric vector for computing cluster scores for a variable, matrix or data frame for computing cluster scores for more than one variable.
`cluster`	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`, vector of cluster scores is expanded to match the input vector `x`.
`names`	a character string or character vector indicating the names of the computed variables when specifying more than one variable. 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 `x` (e.g., `names = 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 `x`, but not to `cluster`.
`check`	logical: if `TRUE`, 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 x 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

dat.ml <- data.frame(id = c(1, 2, 3, 4, 5, 6, 7, 8, 9),
                     cluster = c(1, 1, 1, 2, 2, 2, 3, 3, 3),
                     x1 = c(4, 2, 5, 6, 3, 4, 1, 3, 4),
                     x2 = c(2, 5, 3, 1, 2, 7, 4, 5, 3))

# Compute cluster means and expand to match the input x
cluster.scores(dat.ml$x1, cluster = dat.ml$cluster)

# Compute standard deviation for each cluster and expand to match the input x
cluster.scores(dat.ml$x1, cluster = dat.ml$cluster, fun = "sd")

# Compute cluster means without expanding the vector
cluster.scores(dat.ml$x1, cluster = dat.ml$cluster, expand = FALSE)

# Compute cluster means and attach to 'dat.ml'
dat.ml <- cbind(dat.ml,
                cluster.scores(dat.ml[, c("x1", "x2")], cluster = dat.ml$cluster))

misty documentation built on Nov. 15, 2023, 1:06 a.m.