corrMatrix: Correlation Matrix

In jmv: The 'jamovi' Analyses

Correlation Matrix

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.

Value

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                                          —
#  --------------------------------------------------------------
#

jmv documentation built on June 22, 2024, 10:40 a.m.