R/phenocov_mvmr.R

In WSpiller/MVMR: MVMR

#' phenocov_mvmr
#'
#' Uses an external phenotypic covariance matrix and summary data to estimate covariance matrices for estimated effects of individual genetic
#' variants on each exposure. The phenotypic covariance matrix should be constructed using standardised phenotype measures. The function returns a number of covariance matrices equal to the number of SNPs, where SNP and
#' row numbers reference ordered exposures.
#'
#' @param Pcov A phenotypic matrix using exposures, constructed using individual level exposure data. Columns should be ordered by exposure so as to match \code{format_mvmr}.
#' @param seBXGs A matrix containing standard errors corresponding in relation to the gene-exposure association for each SNP.
#'
#' @return A list of covariance matrices with respect to each genetic variant, retaining the ordering in \code{seBXGs}
#'
#' @author Wes Spiller; Eleanor Sanderson; Jack Bowden.
#' @references Sanderson, E., et al., An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. International Journal of Epidemiology, 2019, 48, 3, 713-727. \doi{10.1093/ije/dyy262}
#' @export
#' @examples
#' \dontrun{
#' phenocov_mvmr(Pcov, summarydata[,c(3,4)])
#' }

phenocov_mvmr<-function(Pcov,seBXGs){

  sigmalist <- vector("list", length(seBXGs[1,]))

  for(i in 1:length(seBXGs[,1])){

    sigma_mattemp<-Pcov

    for(j in 1:length(seBXGs[1,])){

      for(k in 1:length(seBXGs[1,])){

        sigma_mattemp[j,k]<-sigma_mattemp[j,k] * seBXGs[i,j] * seBXGs[i,k]

      }

    }

    sigmalist[[i]] <-sigma_mattemp

  }

  return(sigmalist)

}