I've got 400 store departments and I'm running (Pearson) correlations between all the departments. How can I output the 'N' (number of cases) and the significance level (p value)?
I'm using the cor function. Here is my current code which works fine:
numprod <- ncol(data) - 2;
matrix <- as.matrix(data[ ,2:numprod]);
AllChannels <- cbind(matrix(nrow = numprod-1,"All channels"),cor(matrix, use="all.obs", method="pearson"));
In SPSS, when you run a correlation it outputs the correlation coefficient, N and significance. This is my desired result.
Thanks all!
Lucas
If it's just the length of one of the vectors then use length. If you want the inferential calculations for the correlation coefficient equaling 0 then use cor.test (as the help page for ?cor tells you.) If it's the number of degrees of freedom for the test then look more closely at ?cor.test.
> cor.test(1:10,2:11)
Pearson's product-moment correlation
data: 1:10 and 2:11
t = 134217728, df = 8, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
1 1
sample estimates:
cor
1
The result of cor.test will be a list, so it's not going to be useful to use cbind. The Hmisc package has rcorr:
install.packages("Hmisc")
library(Hmisc)
x <- c(-2, -1, 0, 1, 2)
y <- c(4, 1, 0, 1, 4)
z <- c(1, 2, 3, 4, NA)
v <- c(1, 2, 3, 4, 5)
rcorr(cbind(x,y,z,v))
# ======== Returns a list with three elements:
> rcorr(cbind(x,y,z,v))
x y z v
x 1 0.00 1.00 1
y 0 1.00 -0.75 0
z 1 -0.75 1.00 1
v 1 0.00 1.00 1
n
x y z v
x 5 5 4 5
y 5 5 4 5
z 4 4 5 4
v 5 5 4 5
P
x y z v
x 1.0000 0.0000 0.0000
y 1.0000 0.2546 1.0000
z 0.0000 0.2546 0.0000
v 0.0000 1.0000 0.0000