Add p-values in corrplot matrix

Question

I calculated the Spearman correlation between two matrices and I'm plotting the r values using corrplot. How can I plot only the significant correlations (so only those correlations having p value lower than 0.00 and delete those having higher p value, even if are strong correlations - high value of r). I generated the correlation matrix using corr.test in psych package, so I already have the p values in cor.matrix$p

This is the code I'm using:

library(corrplot)
library(psych)
corr.test(mydata_t1, mydata_t2, method="spearman")
M <- corrplot(cor.matrix$r, method="square",type="lower",col=col1(100),is.corr=T,mar=c(1,1,1,1),tl.cex=0.5)

How can I modify it to plot only significant corelations?

Solution 1

Take a look at the examples of corrplot. do ?corrplot. It has options for doing what you want. You can plot the p-values on the graph itself, which I think is better than putting stars, as people not familiar with that terminology have one more thing to look up. to put p-values on graph do this corrplot(cor.matrix$r, p.mat = cor.matrix$p, insig = "p-value") where cor.matrix is object holding the result of cor.test. The insig option can put:

• p-values (as shown above)
• blank out insignificant correlations with corrplot(cor.matrix$r, p.mat = cor.matrix$p, insig = "blank")`
• Cross out (put a X on) insignificant correlations) with option corrplot(cor.matrix$r, p.mat = cor.matrix$p, insig = "pch") (DEFAULT)
• Do nothing with to the plot, with corrplot(cor.matrix$r, p.mat = cor.matrix$p, insig = "n")

If you do want stars, p-value on the correlation matrix plot - take a look at this thread Correlation Corrplot Configuration

Though I have to say I really like @sven hohenstein's elegant subset solution.

Solution 2

Create a copy of cor.mat and replace the corresponding correlation coefficients with zero:

cor.matrix2 <- cor.matrix

# find cells with p-values > 0.05 and replace corresponding
# correlations coefficients with zero
cor.matrix2$r[cor.matrix2$p > 0.05] <- 0

# use this matrix for corrplot
M <- corrplot(cor.matrix2$r, method="square",type="lower",col=col1(100),
              is.corr=T,mar=c(1,1,1,1),tl.cex=0.5)

The replaced values will appear as a white cell.

Solution 3

What you are asking is similar to what subset does:

Return subsets of vectors, matrices or data frames which meet conditions.

So you can do:

cor.matrix <- subset(cor.matrix, p<0.00)
P <- corrplot(cor.matrix$r, method="square",type="lower",col=col1(100),is.corr=T,mar=c(1,1,1,1),tl.cex=0.5)