Bayesian implementation of the MVMR-Egger model with choice of prior distributions fitted using JAGS.
Source:R/mvmr_egger_rjags.R
mvmr_egger_rjags.Rd
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
mvmr_format
.- prior
A character string for selecting the prior distributions;
"default"
selects a non-informative set of priors;"weak"
selects weakly informative priors;"pseudo"
selects a pseudo-horseshoe prior on the causal effect;
- 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
3
chains.- n.burn
Numeric indicating the burn-in period of the Bayesian MCMC estimation. The default is
1000
samples.- n.iter
Numeric indicating the number of iterations in the Bayesian MCMC estimation. The default is
5000
iterations.- 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
0.5
.- ...
Additional arguments passed through to
rjags::jags.model()
.
Value
An object of class mveggerjags
containing the following components:
- AvgPleio
The mean of the simulated pleiotropic effect
- CausalEffect
The mean of the simulated causal effect
- StandardError
Standard deviation of the simulated causal effect
- sigma
The value of the residual standard deviation
- CredibleInterval
The credible interval for the causal effect, which includes the lower (2.5%), median (50%) and upper intervals (97.5%)
- samples
Output of the Bayesian MCMC samples
- Priors
The specified priors
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
if (requireNamespace("rjags", quietly = TRUE)) {
if (FALSE) { # \dontrun{
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)
} # }
}