misty (version 0.7.1)

cluster.scores: 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)

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.

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.

Author

Takuya Yanagida

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.

See Also

item.scores, multilevel.descript, multilevel.icc

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))