mr_egger_rjags: Bayesian implementation of the MR-Egger multivariate model...
In mrbayes: Bayesian Summary Data Models for Mendelian Randomization Studies

Description Usage Arguments Value References Examples

Bayesian implementation of the MR-Egger multivariate model with choice of prior distributions fitted using JAGS.

mr_egger_rjags(
  object,
  prior = "default",
  betaprior = "",
  sigmaprior = "",
  n.chains = 3,
  n.burn = 1000,
  n.iter = 5000,
  seed = NULL,
  rho = 0.5,
  ...
)

`object`	A data object of class `mr_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; `"joint"` selects a joint prior on the intercept and slope.
`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.
`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()`.

An object of class eggerjags 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

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 (requireNamespace("rjags", quietly = TRUE)) {
fit <- mr_egger_rjags(bmi_insulin)
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)
}