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Bayesian inverse variance weighted model with a choice of prior distributions fitted using RStan.

Source: R/mr_radialegger_stan.R
mr_radialegger_stan.Rd

Bayesian inverse variance weighted model with a choice of prior distributions fitted using RStan

Usage

mr_radialegger_stan(
  data,
  prior = 1,
  n.chains = 3,
  n.burn = 1000,
  n.iter = 5000,
  rho = 0.5,
  seed = 12345,
  ...
)

Arguments

data

A data of class mr_format.

prior

An integer for selecting the prior distributions;

  • 1 selects a non-informative set of priors;

  • 2 selects weakly informative priors;

  • 3 selects a pseudo-horseshoe prior on the causal effect;

  • 4 selects joint prior of the intercept and causal effect estimate.

n.chains

Numeric indicating the number of chains used in the HMC estimation in rstan, the default is 3 chains.

n.burn

Numeric indicating the burn-in period of the Bayesian HMC estimation. The default is 1000 samples.

n.iter

Numeric indicating the number of iterations in the Bayesian HMC estimation. The default is 5000 iterations.

rho

Numeric indicating the correlation coefficient input into the joint prior distribution. The default is 0.5.

seed

Numeric indicating the random number seed. The default is 12345.

...

Additional arguments passed through to rstan::sampling().

Value

An object of class rstan::stanfit.

References

Bowden, J., et al., Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. International Journal of Epidemiology, 2018. 47(4): p. 1264-1278. doi:10.1093/ije/dyy101 .

Stan Development Team (2020). "RStan: the R interface to Stan." R package version 2.19.3, https://mc-stan.org/.

Examples

if (requireNamespace("rstan", quietly = TRUE)) {
# Note we recommend setting n.burn and n.iter to larger values
suppressWarnings({
  radegger_fit <- mr_radialegger_stan(bmi_insulin, n.burn = 500, n.iter = 1000, refresh = 0L)
})
print(radegger_fit)
}
#> Inference for Stan model: mrradialegger.
#> 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 -30.27    0.69 13.02 -55.63 -38.65 -30.91 -21.49  -4.76   360 1.00
#> estimate    6.31    0.13  2.48   1.34   4.67   6.44   7.95  11.14   371 1.00
#> sigma       7.03    0.07  1.29   4.87   6.04   6.91   7.93   9.59   325 1.02
#> lp__      -33.88    0.05  1.10 -36.56 -34.50 -33.67 -33.03 -32.47   425 1.00
#> 
#> Samples were drawn using NUTS(diag_e) at Mon Aug 19 14:09:23 2024.
#> 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).