corp {quest}

Bivariate Correlations with Significant Symbols

Description

corp computes bivariate correlations and their associated p-values. The function is primarily for preparing a correlation table for publication: the correlations are appended by significant symbols (e.g., asterixis), corp is simply corr.test + add_sig_cor.

Usage

corp(
  data,
  vrb.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.

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.

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

data.frame with rownames and colnames equal to vrb.nm containing the bivariate correlations with significance symbols after the correlation value, specified by the arguments p.10, p.05, p.01, and p.001 arguments. The specific elements of the return object are determined by the other arguments.

See Also

add_sig_cor for adding significant symbols to a correlation matrix, add_sig for adding significant symbols to any (atomic) vector, matrix, or (3D+) array, cor for computing only the correlation coefficients themselves corr.test for a function providing confidence intervals as well

Examples

corp(data = mtcars, vrb.nm = c("mpg","cyl","disp","hp","drat")) # no quotes b/c a data.frame
corp(data = attitude, vrb.nm = colnames(attitude))
corp(data = attitude, vrb.nm = colnames(attitude), p.10 = "'") # advance & privileges
corp(data = airquality, vrb.nm = colnames(airquality), plus = TRUE)