# R/partialcor.R In kkholst/lava: Latent Variable Models

#### Documented in partialcor

```##' Calculate partial correlations
##'
##' Calculate partial correlation coefficients and confidence limits via Fishers
##' z-transform
##'
##'
##' @param formula formula speciying the covariates and optionally the outcomes
##' to calculate partial correlation for
##' @param data data.frame
##' @param level Level of confidence limits
##' @param ... Additional arguments to lower level functions
##' @return A coefficient matrix
##' @author Klaus K. Holst
##' @keywords models regression
##' @examples
##'
##' m <- lvm(c(y1,y2,y3)~x1+x2)
##' covariance(m) <- c(y1,y2,y3)~y1+y2+y3
##' d <- sim(m,500)
##' partialcor(~x1+x2,d)
##'
##' @export
partialcor <- function(formula,data,level=0.95,...) {
y <-  getoutcome(formula)
if (length(y)==0) {
preds <- all.vars(formula)
yy <- setdiff(names(data),preds)
} else {
yy <- decomp.specials(y)
preds <- attr(y,"x")
}
if (length(yy)<2)
return(NULL)
res <- c()
for (i in seq_len(length(yy)-1))
for (j in seq(i+1,length(yy))) {
f <- as.formula(paste("cbind(",yy[i],",",yy[j],")~", paste(preds,collapse="+")))
res <- rbind(res, partialcorpair(f,data,level=level))
rownames(res)[nrow(res)] <- paste(yy[i],yy[j],sep="~")
}
return(res)
}

partialcorpair <- function(formula,data,level=0.95,...) {
l <- lm(formula,data)
k <- ncol(model.matrix(l))
n <- nrow(model.matrix(l))
r <- residuals(l)
rho <- cor(r)[1,2]
zrho <- atanh(rho)
var.z <- 1/(n-k-3)
ci.z <- zrho + c(-1,1)*qnorm(1-(1-level)/2)*sqrt(var.z)
ci.rho <- tanh(ci.z)
z <- 1/sqrt(var.z)*zrho
p.z <- 2*(pnorm(-abs(z))) # p-value using z-transform for H_0: rho=0.
return(c(cor=rho,z=z,pval=p.z,lowerCI=ci.rho[1],upperCI=ci.rho[2]))
}
```
kkholst/lava documentation built on Sept. 6, 2021, 11:36 p.m.