corp_ml {quest}

corp_ml decomposes correlations from multilevel data into within-group and between-group correlations as well as adds significance symbols to the end of each value. The workhorse of the function is statsBy. corp_ml is simply a combination of cor_ml and add_sig_cor.

Description

corp_ml decomposes correlations from multilevel data into within-group and between-group correlations as well as adds significance symbols to the end of each value. The workhorse of the function is statsBy. corp_ml is simply a combination of cor_ml and add_sig_cor.

Usage

corp_ml(
  data,
  vrb.nm,
  grp.nm,
  use = "pairwise.complete.obs",
  method = "pearson",
  digits = 3L,
  p.10 = "",
  p.05 = "*",
  p.01 = "**",
  p.001 = "***",
  lead.zero = FALSE,
  trail.zero = TRUE,
  plus = FALSE,
  diags = FALSE,
  lower = TRUE,
  upper = FALSE
)

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.

digits

integer vector of length 1 specifying the number of decimals to round to.

p.10

character vector of length 1 specifying which symbol to append to the end of any correlation significant at the p < .10 level.

p.05

character vector of length 1 specifying which symbol to append to the end of any correlation significant at the p < .05 level.

p.01

character vector of length 1 specifying which symbol to append to the end of any correlation significant at the p < .01 level.

p.001

character vector of length 1 specifying which symbol to append to the end of any correlation significant at the p < .001 level.

lead.zero

logical vector of length 1 specifying whether to retain a zero in front of the decimal place.

trail.zero

logical vector of length 1 specifying whether to retain zeros after the decimal place (due to rounding).

plus

logical vector of length 1 specifying whether to include a plus sign in front of positive correlations (minus signs are always in front of negative correlations).

diags

logical vector of length 1 specifying whether to retain the values in the diagonal of the correlation matrix. If TRUE, then the diagonal will be 1s with digits number of zeros after the decimal place (and no significant symbols). If FALSE, then the diagonal will be NA.

lower

logical vector of length 1 specifying whether to retain the lower triangle of the correlation matrix. If TRUE, then the lower triangle correlations and their significance symbols are retained. If FAlSE, then the lower triangle will all be NA.

upper

logical vector of length 1 specifying whether to retain the upper triangle of the correlation matrix. If TRUE, then the upper triangle correlations and their significance symbols are retained. If FAlSE, then the upper triangle will all be NA.

Value

list of two elements that are data.frames with names "within" and "between". The first data.frame has the within-group correlations with their significance symbols at the end of the statistically significant correlations based on their associated p-value. The second data.frame has the between-group correlations with their significance symbols at the end of the statistically significant correlations based on their associated p-values. The rownames and colnames of each dataframe are vrb.nm. The formatting of the two data.frames depends on several of the arguments.

See Also

cor_ml for multilevel correlations without significance symbols, corp_by for correlations with significance symbols by group, statsBy the workhorse for the corp_ml function, add_sig_cor for adding significant symbols to correlation matrices,

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
corp_ml(data = dat, vrb.nm = c("outcome","session","trt_time"), grp.nm = "case")

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