corrMatrix: 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

TRUE or FALSE (default), provide the number of cases

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.

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)

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.

Examples

Run this code

# NOT RUN {
data('mtcars')

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

#
#  CORRELATION MATRIX
#
#  Correlation Matrix
#  --------------------------------------------------------------
#                           mpg      cyl       disp      hp
#  --------------------------------------------------------------
#    mpg     Pearson's r        <U+2014>    -0.852    -0.848    -0.776
#            p-value            <U+2014>    < .001    < .001    < .001
#
#    cyl     Pearson's r                  <U+2014>     0.902     0.832
#            p-value                      <U+2014>    < .001    < .001
#
#    disp    Pearson's r                            <U+2014>     0.791
#            p-value                                <U+2014>    < .001
#
#    hp      Pearson's r                                      <U+2014>
#            p-value                                          <U+2014>
#  --------------------------------------------------------------
#
# }

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