Data

The mtcars data is used in the following examples :

mydata <- mtcars[, c(1,3,4,5,6,7)]
head(mydata)

                   mpg disp  hp drat    wt  qsec
Mazda RX4         21.0  160 110 3.90 2.620 16.46
Mazda RX4 Wag     21.0  160 110 3.90 2.875 17.02
Datsun 710        22.8  108  93 3.85 2.320 18.61
Hornet 4 Drive    21.4  258 110 3.08 3.215 19.44
Hornet Sportabout 18.7  360 175 3.15 3.440 17.02
Valiant           18.1  225 105 2.76 3.460 20.22

Computing of the correlation matrix

The rcorr() function from Hmisc package can be used to perform Pearson or Spearman correlation test.

library(Hmisc)
res<-rcorr(as.matrix(mydata))

As an output, the rcorr() function returns a list including the following elements : - r : the correlation matrix. - P : the p-values corresponding to the significance levels of the correlations.

# Printing the correlation matrix
signif(res$r, 2)

       mpg  disp    hp   drat    wt   qsec
mpg   1.00 -0.85 -0.78  0.680 -0.87  0.420
disp -0.85  1.00  0.79 -0.710  0.89 -0.430
hp   -0.78  0.79  1.00 -0.450  0.66 -0.710
drat  0.68 -0.71 -0.45  1.000 -0.71  0.091
wt   -0.87  0.89  0.66 -0.710  1.00 -0.170
qsec  0.42 -0.43 -0.71  0.091 -0.17  1.000

# Printing the p-values of the correlations
signif(res$P,2)

         mpg    disp      hp    drat      wt    qsec
mpg       NA 9.4e-10 1.8e-07 1.8e-05 1.3e-10 1.7e-02
disp 9.4e-10      NA 7.1e-08 5.3e-06 1.2e-11 1.3e-02
hp   1.8e-07 7.1e-08      NA 1.0e-02 4.1e-05 5.8e-06
drat 1.8e-05 5.3e-06 1.0e-02      NA 4.8e-06 6.2e-01
wt   1.3e-10 1.2e-11 4.1e-05 4.8e-06      NA 3.4e-01
qsec 1.7e-02 1.3e-02 5.8e-06 6.2e-01 3.4e-01      NA

Formatting the correlation matrix in 4 column tables

The following custom function is used:

# ++++++++++++++++++++++++++++
# flattenCorrMatrix
# ++++++++++++++++++++++++++++
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    row = rownames(cormat)[row(cormat)[ut]],
    column = rownames(cormat)[col(cormat)[ut]],
    cor  =(cormat)[ut],
    p = pmat[ut]
    )
}

The flattenCorrMatrix function will format the correlation matrix into a table of 4 columns: row names, column names, the correlation coefficient between each variable and the others, and the p-values.

flattenCorrMatrix(res$r, res$P)

    row column     cor         p
1   mpg   disp -0.8476 9.380e-10
2   mpg     hp -0.7762 1.788e-07
3  disp     hp  0.7909 7.143e-08
4   mpg   drat  0.6812 1.776e-05
5  disp   drat -0.7102 5.282e-06
6    hp   drat -0.4488 9.989e-03
7   mpg     wt -0.8677 1.294e-10
8  disp     wt  0.8880 1.222e-11
9    hp     wt  0.6587 4.146e-05
10 drat     wt -0.7124 4.784e-06
11  mpg   qsec  0.4187 1.708e-02
12 disp   qsec -0.4337 1.314e-02
13   hp   qsec -0.7082 5.766e-06
14 drat   qsec  0.0912 6.196e-01
15   wt   qsec -0.1747 3.389e-01

Visualization of the correlation matrix

You can use the chart.Correlation() function from PerformanceAnalytics package to display a chart of the correlation matrix.

The histograms of the variables are shown on the diagonal. The asterisks indicate the significance levels of the correlations. Each significance level is associated to a symbol :

p-values(0, 0.001, 0.01, 0.05, 0.1, 1) <=> symbols(“***”, “**”, “*”, “.”, " “)

library(PerformanceAnalytics)
chart.Correlation(mydata, histogram=TRUE, pch=19)

Infos

This analysis has been performed with R (ver. 3.1.0).

Recommended for you