R/mvmr_egger_rjags.R
mvmr_egger_rjags.Rd
Bayesian implementation of the MVMR-Egger model with choice of prior distributions fitted using JAGS.
mvmr_egger_rjags( object, prior = "default", betaprior = "", sigmaprior = "", orientate = 1, n.chains = 3, n.burn = 1000, n.iter = 5000, seed = NULL, rho = 0.5, ... )
object | A data object of class |
---|---|
prior | A character string for selecting the prior distributions;
|
betaprior | A character string in JAGS syntax to allow a user defined prior for the causal effect. |
sigmaprior | A character string in JAGS syntax to allow a user defined prior for the residual standard deviation. |
orientate | Numeric value to indicate the oriented exposure |
n.chains | Numeric indicating the number of chains used in the MCMC estimation, the default is |
n.burn | Numeric indicating the burn-in period of the Bayesian MCMC estimation. The default is |
n.iter | Numeric indicating the number of iterations in the Bayesian MCMC estimation. The default is |
seed | Numeric indicating the random number seed. The default is the rjags default. |
rho | Numeric indicating the correlation coefficient input into the joint prior distribution. The default value is |
... | Additional arguments passed through to |
An object of class mveggerjags
containing the following components:
if (requireNamespace("rjags", quietly = TRUE))
Bowden et. al., Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. International Journal of Epidemiology 2015. 44(2): p. 512-525. doi: 10.1093/ije/dyv080
if (FALSE) { if (requireNamespace("rjags", quietly = TRUE)) { dat <- mvmr_format(rsid = dodata$rsid, xbeta = cbind(dodata$ldlcbeta,dodata$hdlcbeta,dodata$tgbeta), ybeta = dodata$chdbeta, xse = cbind(dodata$ldlcse,dodata$hdlcse,dodata$tgse), yse = dodata$chdse) fit <- mvmr_egger_rjags(dat) summary(fit) plot(fit$samples) # 90% credible interval fitdf <- do.call(rbind.data.frame, fit$samples) cri90 <- sapply(fitdf, quantile, probs = c(0.05, 0.95)) print(cri90) } }