cor_ml {quest}

Multilevel Correlation Matrices

Description

cor_ml decomposes correlations from multilevel data into within-group and between-group correlations. The workhorse of the function is statsBy.

Usage

cor_ml(data, vrb.nm, grp.nm, use = "pairwise.complete.obs", method = "pearson")

Arguments

data

data.frame of data.

vrb.nm

character vector of colnames from data specifying the variable columns.

grp.nm

character vector of length 1 of a colname from data specifying the grouping column.

use

character vector of length 1 specifying how to handle missing values when computing the correlations. The options are: 1. "pairwise.complete.obs" which uses pairwise deletion, 2. "complete.obs" which uses listwise deletion, and 3. "everything" which uses all cases and returns NA for any correlations from columns in data[vrb.nm] with missing values.

method

character vector of length 1 specifying which type of correlations to compute. The options are: 1. "pearson" for traditional Pearson product-moment correlations, 2. "kendall" for Kendall rank correlations, and 3. "spearman" for Spearman rank correlations.

Value

list with two elements named "within" and "between" each containing a numeric matrix. The first "within" matrix is the within-group correlation matrix and the second "between" matrix is the between-group correlation matrix. The rownames and colnames of each numeric matrix are vrb.nm.

See Also

corp_ml for multilevel correlations with significance symbols, cor_by for correlation matrices by group, cor for traditional, single-level correlation matrices, statsBy the workhorse for the cor_ml function,

Examples

# traditional use
tmp <- c("outcome","case","session","trt_time") # roxygen2 does not like c() inside []
dat <- as.data.frame(lmeInfo::Bryant2016)[tmp]
stats_by <- psych::statsBy(dat, group = "case") # requires you to include "case" column in dat
cor_ml(data = dat, vrb.nm = c("outcome","session","trt_time"), grp.nm = "case")

# varying the \code{use} and \code{method} arguments
cor_ml(data = airquality, vrb.nm = c("Ozone","Solar.R","Wind","Temp"), grp.nm = "Month",
   use = "pairwise", method = "pearson")
cor_ml(data = airquality, vrb.nm = c("Ozone","Solar.R","Wind","Temp"), grp.nm = "Month",
   use = "complete", method = "kendall")
cor_ml(data = airquality, vrb.nm = c("Ozone","Solar.R","Wind","Temp"), grp.nm = "Month",
   use = "everything", method = "spearman")