Creating correlation matrix p values [duplicate]

Question

I can get correlation matrix using following commands:

> df<-data.frame(x=c(5,6,5,9,4,2,1,3,5,7),y=c(3.1,2.5,3.8,5.4,6.5,2.5,1.5,8.1,7.1,6.1),z=c(5,6,4,9,2,4,1,6,2,4))
> cor(df)
           x         y        z
x  1.0000000 0.2923939 0.6566866
y  0.2923939 1.0000000 0.1167084
z 0.6566866 0.1167084 1.0000000
>

I can get individual p-values using command:

> cor.test(x,y)$p.value
[1] 0.4123234

How can I get a matrix of p-values for all these correlation coefficients? Thanks for your help.

Answer 1

You can also use the package Hmisc.

An example of how it works:

mycor <- rcorr(as.matrix(data), type="pearson")

mycor$r shows the correlation matrix, mycor$p the matrix with corresponding p-values.

Answer 2

This example calculates the p value for each of the column combinations. It is not an optimal solution (x-y and y-x p values are both calculated for example), but should provide some inspiration for you. The main trick is to use expand.grid to generate the combinations of columns, and use mapply to call cor.test on each combination:

col_combinations = expand.grid(names(df), names(df))
cor_test_wrapper = function(col_name1, col_name2, data_frame) {
    cor.test(data_frame[[col_name1]], data_frame[[col_name2]])$p.value
}
p_vals = mapply(cor_test_wrapper, 
                  col_name1 = col_combinations[[1]], 
                  col_name2 = col_combinations[[2]], 
                  MoreArgs = list(data_frame = df))
matrix(p_vals, 3, 3, dimnames = list(names(df), names(df)))
           x         y          z
x 0.00000000 0.4123234 0.03914453
y 0.41232343 0.0000000 0.74814951
z 0.03914453 0.7481495 0.00000000

Answer 3

one way is to use corr.test (notice the double r) from package psych

.. or if you're a fan of mapply and sapply you could write your own function doing this. something like:

rrapply <- function(A, FUN, ...) mapply(function(a, B) lapply(B, 
         function(x) FUN(a, x, ...)), a = A, MoreArgs = list(B = A))
cor.tests <- rrapply(df, cor.test) # a matrix of cor.tests
apply(cor.tests, 1:2, function(x) x[[1]]$p.value) # and it's there

And now you can use the same logic to make a matrix of t-tests or, say, CI's of correlations