jmv

Correlation Matrix

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

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 using 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.

Example usage

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                                          —
#  ──────────────────────────────────────────────────────────────
#

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
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.

Returns

A results object containing:

results$matrix a table
results$plot an image

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

results$matrix$asDF

as.data.frame(results$matrix)