R/mr_ivw_stan.R
mr_ivw_stan.Rd
Bayesian inverse variance weighted model with a choice of prior distributions fitted using RStan.
mr_ivw_stan( data, prior = 1, n.chains = 3, n.burn = 1000, n.iter = 5000, seed = 12345, ... )
data | A data of class |
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prior | An integer for selecting the prior distributions;
|
n.chains | Numeric indicating the number of chains used in the HMC estimation in rstan, the default is |
n.burn | Numeric indicating the burn-in period of the Bayesian HMC 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 |
... | Additional arguments passed through to |
An object of class stanfit
.
Burgess, S., Butterworth, A., Thompson S.G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genetic Epidemiology, 2013, 37, 7, 658-665 doi: 10.1002/gepi.21758 .
Stan Development Team (2020). "RStan: the R interface to Stan." R package version 2.19.3, https://mc-stan.org/.
if (requireNamespace("rstan", quietly = TRUE)) { ivw_fit <- mr_ivw_stan(bmi_insulin) print(ivw_fit) rstan::traceplot(ivw_fit) }#> Inference for Stan model: mrivw. #> 3 chains, each with iter=5000; warmup=1000; thin=1; #> post-warmup draws per chain=4000, total post-warmup draws=12000. #> #> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff #> estimate 0.58 0.00 0.05 0.48 0.54 0.58 0.61 0.68 2739 #> lp__ -393.05 0.02 0.73 -395.07 -393.22 -392.77 -392.58 -392.53 2318 #> Rhat #> estimate 1 #> lp__ 1 #> #> Samples were drawn using NUTS(diag_e) at Thu Oct 7 10:00:33 2021. #> For each parameter, n_eff is a crude measure of effective sample size, #> and Rhat is the potential scale reduction factor on split chains (at #> convergence, Rhat=1).