R/mr_egger_stan.R
mr_egger_stan.Rd
Bayesian inverse variance weighted model with a choice of prior distributions fitted using Stan.
mr_egger_stan( data, prior = 1, n.chains = 3, n.burn = 1000, n.iter = 5000, seed = 12345, rho = 0.5, ... )
data | A data of class |
---|---|
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 HMC estimation. The default is |
seed | Numeric indicating the random number seed. The default is |
rho | Numeric indicating the correlation coefficient input into the joint prior distribution. The default is |
... | Additional arguments passed through to |
An object of class stanfit
.
Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. International Journal of Epidemiology, 2015, 44, 2, 512-525. doi: 10.1093/ije/dyv080 .
Stan Development Team (2020). "RStan: the R interface to Stan." R package version 2.19.3, https://mc-stan.org/.
# \donttest{ if (requireNamespace("rstan", quietly = TRUE)) { # Note we recommend setting n.burn and n.iter to larger values egger_fit <- mr_egger_stan(bmi_insulin, n.burn = 500, n.iter = 1000) print(egger_fit) }#> Warning: There were 273 divergent transitions after warmup. See #> http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup #> to find out why this is a problem and how to eliminate them.#> Warning: Examine the pairs() plot to diagnose sampling problems#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable. #> Running the chains for more iterations may help. See #> http://mc-stan.org/misc/warnings.html#bulk-ess#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable. #> Running the chains for more iterations may help. See #> http://mc-stan.org/misc/warnings.html#tail-ess#> Inference for Stan model: mregger. #> 3 chains, each with iter=1000; warmup=500; thin=1; #> post-warmup draws per chain=500, total post-warmup draws=1500. #> #> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat #> intercept -0.06 0.00 0.04 -0.14 -0.09 -0.06 -0.04 0.02 203 1.01 #> estimate 4.09 0.16 2.32 -0.57 2.62 3.96 5.62 8.65 213 1.01 #> sigma 7.72 0.07 1.23 5.43 6.74 7.77 8.71 9.78 315 1.01 #> lp__ -35.45 0.06 1.05 -38.22 -35.92 -35.26 -34.67 -34.12 272 1.00 #> #> Samples were drawn using NUTS(diag_e) at Thu Oct 7 10:00:31 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).# }