matyuschenko
1/24/2017 - 8:41 AM

## correlation_matrix

correlation_matrix

``````# You'll need the PerformanceAnalytics package to produce the plot

## Correlation matrix with p-values. See http://goo.gl/nahmV for documentation of this function
cor.prob <- function (X, dfr = nrow(X) - 2) {
R <- cor(X, use="pairwise.complete.obs")
above <- row(R) < col(R)
r2 <- R[above]^2
Fstat <- r2 * dfr/(1 - r2)
R[above] <- 1 - pf(Fstat, 1, dfr)
R[row(R) == col(R)] <- NA
R
}

## Use this to dump the cor.prob output to a 4 column matrix
## with row/column indices, correlation, and p-value.
## See StackOverflow question: http://goo.gl/fCUcQ
flattenSquareMatrix <- function(m) {
if( (class(m) != "matrix") | (nrow(m) != ncol(m))) stop("Must be a square matrix.")
if(!identical(rownames(m), colnames(m))) stop("Row and column names must be equal.")
ut <- upper.tri(m)
data.frame(i = rownames(m)[row(m)[ut]],
j = rownames(m)[col(m)[ut]],
cor=t(m)[ut],
p=m[ut])
}

# get some data from the mtcars built-in dataset
mydata <- mtcars[, c(1,3,4,5,6)]

# correlation matrix
cor(mydata)

# correlation matrix with p-values
cor.prob(mydata)

# "flatten" that table
flattenSquareMatrix(cor.prob(mydata))

# plot the data
library(PerformanceAnalytics)
chart.Correlation(mydata)``````