Add p-values in corrplot matrix
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?
Answer
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.