mvmr_egger_rjags {mrbayes} | R Documentation |
Bayesian implementation of the MVMR-Egger model with choice of prior distributions fitted using JAGS.
Description
Bayesian implementation of the MVMR-Egger model with choice of prior distributions fitted using JAGS.
Usage
mvmr_egger_rjags(
object,
prior = "default",
betaprior = "",
sigmaprior = "",
orientate = 1,
n.chains = 3,
n.burn = 1000,
n.iter = 5000,
seed = NULL,
rho = 0.5,
...
)
Arguments
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 |
Value
An object of class mveggerjags
containing the following components:
if (requireNamespace("rjags", quietly = TRUE))
References
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
Examples
## Not run:
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)
}
## End(Not run)