quest (version 0.2.0)

cor_by: Correlation Matrix by Group

Description

cor_by computes a correlation matrix for each group within numeric data. Only the correlation coefficients are determined and not any NHST information. If that is desired, use corp_by which includes significance symbols. cor_by is simply cor + by2.

Usage

cor_by(
  data,
  vrb.nm,
  grp.nm,
  use = "pairwise.complete.obs",
  method = "pearson",
  sep = ".",
  check = TRUE
)

Value

list of numeric matrices containing the correlations from each group. The listnames are the unique combinations of the grouping variables, separated by "sep" if multiple grouping variables (i.e.,

length(grp.nm) > 1) are input:

unique(interaction(data[grp.nm], sep = sep)). The rownames and colnames of each numeric matrix are vrb.nm.

Arguments

data

data.frame of data.

vrb.nm

character vector of colnames from data specifying the variables.

grp.nm

character vector of colnames from data specifying the groups.

use

character vector of length 1 specifying how to handle missing data when computing the correlations. The options are 1) "pairwise.complete.obs", 2) "complete.obs", 3) "na.or.complete", 4) "all.obs", or 5) "everything". See details of cor.

method

character vector of length 1 specifying the type of correlations to be computed. The options are 1) "pearson", 2) "kendall", or 3) "spearman". See details of cor.

sep

character vector of length 1 specifying the string to combine the group values together with. sep is only used if there are multiple grouping variables (i.e., length(grp.nm) > 1).

check

logical vector of length 1 specifying whether to check the structure of the input arguments. For example, check whether data[vrb.nm] are all mode numeric. This argument is available to allow flexibility in whether the user values informative error messages (TRUE) vs. computational efficiency (FALSE).

See Also

cor for full sample correlation matrixes, corp for full sample correlation data.frames with significance symbols, corp_by for full sample correlation data.farmes with significance symbols by group.

Examples

# one grouping variable
cor_by(airquality, vrb.nm = c("Ozone","Solar.R","Wind"), grp.nm = "Month")
cor_by(airquality, vrb.nm = c("Ozone","Solar.R","Wind"), grp.nm = "Month",
   use = "complete.obs", method = "spearman")

# two grouping variables
cor_by(mtcars, vrb.nm = c("mpg","disp","drat","wt"), grp.nm = c("vs","am"))
cor_by(mtcars, vrb.nm = c("mpg","disp","drat","wt"), grp.nm = c("vs","am"),
   use = "complete.obs", method = "spearman", sep = "_")