corr_matrix {AnalysisLin}

Correlation Matrix

Description

Column 1: Row names representing Variable 1 in the correlation test.

Column 2: Column names representing Variable 2 in the correlation test.

Column 3: The correlation coefficients quantifying the strength and direction of the relationship.

Column 4: The p-values associated with the correlations, indicating the statistical significance of the observed relationships. Lower p-values suggest stronger evidence against the null hypothesis.

The table provides valuable insights into the relationships between variables, helping to identify statistically significant correlations.

Usage

corr_matrix(
  data,
  type = "pearson",
  corr_plot = FALSE,
  sig.level = 0.01,
  highlight = FALSE,
  html = FALSE
)

Arguments

data

Input dataset.

type

Pearson or Spearman correlation, default is Pearson.

corr_plot

Generate a correlation matrix plot, default is false.

sig.level

Significant level. Default is 0.01.

highlight

Highlight p-value(s) that is less than sig.level, default is FALSE

html

Whether the output should be in HTML format,used when knitting into HTML. Default is FALSE.

Value

A data frame which contains row names, column names, correlation coefficients, and p-values.

A plot of the correlation if corrplot is set to be true.

Examples

data(mtcars)
corr_matrix(mtcars, type = 'pearson')