この節の作者: Ravi Selker, Jonathon Love, Damian Dropmann

Correlation Matrix (`corrMatrix`)¶

Description¶

Correlation matrices are a way to examine linear relationships between two or more continuous variables.

Usage¶

corrMatrix(
  data,
  vars,
  pearson = TRUE,
  spearman = FALSE,
  kendall = FALSE,
  sig = TRUE,
  flag = FALSE,
  n = FALSE,
  ci = FALSE,
  ciWidth = 95,
  plots = FALSE,
  plotDens = FALSE,
  plotStats = FALSE,
  hypothesis = "corr"
)

Arguments¶

`data`	the data as a data frame
`vars`	a vector of strings naming the variables to correlate in `data`
`pearson`	`TRUE` (default) or `FALSE`, provide Pearson's R
`spearman`	`TRUE` or `FALSE` (default), provide Spearman's rho
`kendall`	`TRUE` or `FALSE` (default), provide Kendall's tau-b
`sig`	`TRUE` (default) or `FALSE`, provide significance levels
`flag`	`TRUE` or `FALSE` (default), flag significant correlations
`n`	`TRUE` or `FALSE` (default), provide the number of cases
`ci`	`TRUE` or `FALSE` (default), provide confidence intervals
`ciWidth`	a number between 50 and 99.9 (default: 95), the width of confidence intervals to provide
`plots`	`TRUE` or `FALSE` (default), provide a correlation matrix plot
`plotDens`	`TRUE` or `FALSE` (default), provide densities in the correlation matrix plot
`plotStats`	`TRUE` or `FALSE` (default), provide statistics in the correlation matrix plot
`hypothesis`	one of `'corr'` (default), `'pos'`, `'neg'` specifying the alernative hypothesis; correlated, correlated positively, correlated negatively respectively.

Details¶

For each pair of variables, a Pearson's r value indicates the strength and direction of the relationship between those two variables. A positive value indicates a positive relationship (higher values of one variable predict higher values of the other variable). A negative Pearson's r indicates a negative relationship (higher values of one variable predict lower values of the other variable, and vice-versa). A value of zero indicates no relationship (whether a variable is high or low, does not tell us anything about the value of the other variable).

More formally, it is possible to test the null hypothesis that the correlation is zero and calculate a p-value. If the p-value is low, it suggests the correlation co-efficient is not zero, and there is a linear (or more complex) relationship between the two variables.

Output¶

A results object containing:

`results$matrix`	a correlation matrix table
`results$plot`	a correlation matrix plot

Tables can be converted to data frames with asDF or as.data.frame(). For example:

results$matrix$asDF

as.data.frame(results$matrix)

Examples¶

data('mtcars')

corrMatrix(mtcars, vars = vars(mpg, cyl, disp, hp))

#
#  CORRELATION MATRIX
#
#  Correlation Matrix
#  --------------------------------------------------------------
#                           mpg      cyl       disp      hp
#  --------------------------------------------------------------
#    mpg     Pearson's r        —    -0.852    -0.848    -0.776
#            p-value            —    < .001    < .001    < .001
#
#    cyl     Pearson's r                  —     0.902     0.832
#            p-value                      —    < .001    < .001
#
#    disp    Pearson's r                            —     0.791
#            p-value                                —    < .001
#
#    hp      Pearson's r                                      —
#            p-value                                          —
#  --------------------------------------------------------------
#

Correlation Matrix (corrMatrix)¶