pt_corr: Pairwise Pearson correlations

In PBCAR/PThelper: Systematic Cleaning, Processing, and Summarizing of Behavioral Economic Purchase Task Data

Pairwise Pearson correlations

Description

This function provides coefficients and p-values for a pairwise Pearson correlation, and can also produce a heatmap.

Usage

pt_corr(pt, heatmap = TRUE, alpha = 0.05, coef_size = 7)

Arguments

pt	A data frame which includes only variables to be included in the correlation testing.
heatmap	A logical argument on whether a heatmap should be produced. The default is TRUE.
alpha	The alpha value to determine significance, in which significant p-values will have the corresponding correlation coefficient in the heatmap denoted in bold text. The default alpha value is 0.05.
coef_size	The text size of the coefficients in the heatmap, when the heatmap argument is set to TRUE. Default is 7.

Value

A data frame with correlation coefficients and p-values for each variable included in the correlation testing. If heatmap is set to TRUE, then a ggplot2 graphical object is also returned.

Examples

### --- Example Data

pt <- data.frame("ID" = c(1:36),
"Intensity" = c(10,12,15,0,99,11,7,6,12,7,8,10,5,6,10,0,3,
                7,5,0,2,3,5,6,10,15,12,7,0,9,0,6,7,8,4,5),
"Breakpoint" = c(1,2,5,0,10,3,0.5,0.2,0.3,3,4,5,7.5,0.5,2,0,0.1,
                 0.5,0.5,0,3,2,2,1,2,3,4,1,0,2,0,5,5,7.5,2,3),
"Eta" = c(rnorm(36,0.16,0.19)))

### --- Function Example

pt2 <- pt_corr(pt[c(2:4)])

PBCAR/PThelper documentation built on May 13, 2024, 3:45 p.m.