How to perform partial correlation analysis among multiple columns and controlling by multiple covariates?

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I want to perform partial correlation analysis among multiple columns controlling by multiple covariates, and then extract r and p-value. My real data have some missing values.

I found that this answer (Pairwise partial correlation of a matrix, controlling by one variable) might be useful, so I adjusted this method into my code. Because I have missing values, so I cannot use ppcor::pcor.test(), which is described as 'Missing values are not allowed', to achieve my goal.

Here I use the built-in dataset mtcars to display the problem I met.

# load "ggm" packages to perform partial correlation analysis
library(ggm)

# subset mtcars dataset and make some datapoints as missing values
mydata <- cbind(mtcars[1:8])
mydata[4:10,3] <- rep(NA,7)
mydata[1:5,4] <- NA

# perform partial correlation analysis among the first 6 columns with the last two columns as covariates
sapply(1:(ncol(mydata)-2), function(x) sapply(1:(ncol(mydata)-2), function(y) {
  if (x == y) 1
  else ggm::pcor(c(mydata[,x], mydata[,y], mydata[,7], mydata[,8]),var(mydata))
}))

# error:
Error in S[u, u] : subscript out of bounds

I got error at this step and could neither perform partial correlation nor extract r and p-values.

Many thanks to you to help me!

Ella

sapply correlation r
1 Answers

You don't need to pass the column values in pcor function. You can pass column number or column names. Try :

sapply(1:(ncol(mydata)-2), function(x) sapply(1:(ncol(mydata)-2), function(y) {
   if (x == y) 1
   else ggm::pcor(c(x, y,7,8),var(mydata))
}))

#           [,1]       [,2] [,3] [,4]       [,5]       [,6]
#[1,]  1.0000000 -0.7208025   NA   NA  0.5717984 -0.8260219
#[2,] -0.7208025  1.0000000   NA   NA -0.6969510  0.7414846
#[3,]         NA         NA    1   NA         NA         NA
#[4,]         NA         NA   NA    1         NA         NA
#[5,]  0.5717984 -0.6969510   NA   NA  1.0000000 -0.5510354
#[6,] -0.8260219  0.7414846   NA   NA -0.5510354  1.0000000
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