R/RcppExports.R

In PPMR: Probabilistic Two Sample Mendelian Randomization

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#' PMR model with individual level data
#'
#' Fit the probabilistic MR model with individual level data while accounting for 
#' the correlated instruments and horizontal pleiotropy in TWAS framework.
#'
#' @param yin standardized exposure vector (e.g. gene expression in TWAS).
#' @param zin standardized complex trait vector.
#' @param x1in standardized cis-genotype matrix in eQTL data.
#' @param x2in standardized cis-genotype matrix in GWAS data.
#' @param gammain indicator variable for constrained PMR model, with 1 for the null 
#' model that there is no horizontal pleiotropy.
#' @param alphain indicator variable for constrained PMR model, with 1 for the null 
#' model that there is no causal effect.
#' @param max_iterin The maximum iteration.
#' @param epsin The convergence tolerance of the absolute value of the difference 
#' between the nth and (n+1)th log likelihood.
#' 
#' @return a list of estimates of model parameters, including the causal effect 
#' \code{alpha}, the horizontal pleiotropy effect \code{gamma}, and the two 
#' corresponding p values
#' 
#' @author Zhongshang Yuan, Xiang Zhou.
#' 
#' @examples 
#' data(Exampleindividual)
#' attach(Exampleindividual)
#' fmH1 = PMR_individual(yin=x, zin=y, x1in=zx, x2in=zy,
#'                       gammain=0,alphain = 0,max_iterin =1000,epsin=1e-5)
#' alpha<-fmH1$alpha
#' gamma<-fmH1$gamma
#' fmH0gamma = PMR_individual(yin=x,zin= y, x1in=zx, x2in=zy,gammain=1, 
#'                            alphain = 0,max_iterin =1000,epsin=1e-5)
#' fmH0alpha = PMR_individual(yin=x, zin=y, x1in=zx, x2in=zy,gammain=0,
#'                            alphain = 1,max_iterin =1000, epsin=1e-5)
#' loglikH1=max(fmH1$loglik,na.rm=TRUE)
#' loglikH0gamma=max(fmH0gamma$loglik,na.rm=TRUE)
#' loglikH0alpha=max(fmH0alpha$loglik,na.rm=TRUE)
#' stat_alpha = 2 * (loglikH1 - loglikH0alpha)
#' pvalue_alpha = pchisq(stat_alpha,1,lower.tail=FALSE)
#' stat_gamma = 2 * (loglikH1 - loglikH0gamma)
#' pvalue_gamma = pchisq(stat_gamma,1,lower.tail=FALSE)
#' 
#' @export
#'

PMR_individual <- function(yin, zin, x1in, x2in, gammain, alphain, max_iterin, epsin) {
    .Call(`_PPMR_PMR_individual`, yin, zin, x1in, x2in, gammain, alphain, max_iterin, epsin)
}

#' PMR model with summary data
#'
#' Fit the probabilistic MR model with summary data while accounting for the 
#' correlated instruments and horizontal pleiotropy in TWAS framework.
#'
#' @param betaxin the cis-SNP effect size vector for one specific gene in eQTL 
#' data, which must be calculated based on both the standardized gene expression 
#' value and the standardized cis-genotype matrix.
#' @param betayin the cis-SNP effect size vector for one specific gene in GWAS 
#' data, which be calculated based on both the standardized complex trait value 
#' and the standardized cis-genotype matrix.
#' @param Sigma1sin the LD matrix in eQTL data.
#' @param Sigma2sin the LD matrix in GWAS data. Both \code{Sigma2sin} and 
#' \code{sigma1sin} are often the same from the reference panel.
#' @param samplen1 the sample size of eQTL data.
#' @param samplen2 the sample size of GWAS data.
#' @param gammain indicator variable for constrained model, with 1 for the null 
#' model that there is no horizontal pleiotropy.
#' @param alphain indicator variable for constrained model, with 1 for the null 
#' model that there is no causal effect.
#' @param max_iterin The maximum iteration.
#' @param epsin The convergence tolerance of the absolute value of the difference 
#' between the nth and (n+1)th log likelihood.
#' 
#' @return A list of estimates of model parameters, including the causal effect 
#' \code{alpha}, the horizontal pleiotropy effect \code{gamma}, and the two corresponding p values.
#' 
#' @author Zhongshang Yuan, Xiang Zhou.
#' 
#' @examples 
#' data(Examplesummary)
#' attach(Examplesummary)
#' fmH1=PMR_summary(betaxin=betax,betayin=betay,Sigma1sin=Sigma1,Sigma2sin=Sigma2,
#'       samplen1=n1,samplen2=n2,gammain=0,alphain=0,max_iterin =1000, epsin=1e-5)
#' fmH0alpha=PMR_summary(betaxin=betax,betayin=betay,Sigma1sin=Sigma1,Sigma2sin=Sigma2,
#'            samplen1=n1,samplen2=n2,gammain=0,alphain=1,max_iterin =1000, epsin=1e-5)
#' fmH0gamma=PMR_summary(betaxin=betax,betayin=betay,Sigma1sin=Sigma1,Sigma2sin=Sigma2,
#'            samplen1=n1,samplen2=n2,gammain=1,alphain=0,max_iterin =1000, epsin=1e-5)
#' loglikH1=max(fmH1$loglik,na.rm=TRUE)
#' loglikH0alpha=max(fmH0alpha$loglik,na.rm=TRUE)
#' loglikH0gamma=max(fmH0gamma$loglik,na.rm=TRUE)
#' stat_alpha = 2 * (loglikH1 - loglikH0alpha)
#' pvalue_alpha = pchisq(stat_alpha,1,lower.tail=FALSE)
#' stat_gamma = 2 * (loglikH1 - loglikH0gamma)
#' pvalue_gamma = pchisq(stat_gamma,1,lower.tail=FALSE)
#' 
#' @export
#'
#'
PMR_summary <- function(betaxin, betayin, Sigma1sin, Sigma2sin, samplen1, samplen2, gammain, alphain, max_iterin, epsin) {
    .Call(`_PPMR_PMR_summary`, betaxin, betayin, Sigma1sin, Sigma2sin, samplen1, samplen2, gammain, alphain, max_iterin, epsin)
}