Correlations

You can use the cor( ) function to produce correlations and the cov( ) function to produces covariances.

A simplified format is cor(x, use=, method= ) where

# Correlations/covariances among numeric variables in
# data frame mtcars. Use listwise deletion of missing data.
cor(mtcars, use="complete.obs", method="kendall")
cov(mtcars, use="complete.obs")

Unfortunately, neither cor( ) or cov( ) produce tests of significance, although you can use the cor.test( ) function to test a single correlation coefficient.

The rcorr( ) function in the Hmisc package produces correlations/covariances and significance levels for pearson and spearman correlations. However, input must be a matrix and pairwise deletion is used.

# Correlations with significance levels
library(Hmisc)
rcorr(x, type="pearson") # type can be pearson or spearman

#mtcars is a data frame
rcorr(as.matrix(mtcars))

You can use the format cor(X, Y) or rcorr(X, Y) to generate correlations between the columns of X and the columns of Y. This similar to the VAR and WITH commands in SAS PROC CORR.

# Correlation matrix from mtcars
# with mpg, cyl, and disp as rows
# and hp, drat, and wt as columns
x <- mtcars[1:3]
y <- mtcars[4:6]
cor(x, y)

Other Types of Correlations

# polychoric correlation
# x is a contingency table of counts
library(polycor)
polychor(x)

# heterogeneous correlations in one matrix
# pearson (numeric-numeric),
# polyserial (numeric-ordinal),
# and polychoric (ordinal-ordinal)
# x is a data frame with ordered factors
# and numeric variables
library(polycor)
hetcor(x)

# partial correlations
library(ggm)
data(mydata)
pcor(c("a", "b", "x", "y", "z"), var(mydata))
# partial corr between a and b controlling for x, y, z

Visualizing Correlations

Use corrgram( )to plot correlograms .

Use the pairs()or splom( )to create scatterplot matrices.

To Practice

Try this interactive course on Supervised Learning in R: Regression