bmeta | R Documentation |
This function performers a Bayesian meta-analysis
bmeta( data, mean.mu = 0, sd.mu = 10, scale.sigma.between = 0.5, df.scale.between = 1, nr.chains = 2, nr.iterations = 10000, nr.adapt = 1000, nr.burnin = 1000, nr.thin = 1, be.quiet = FALSE, r2jags = TRUE )
data |
A data frame with at least two columns with the following names: 1) TE = treatment effect, 2) seTE = the standard error of the treatment effect. |
mean.mu |
Prior mean of the overall mean parameter mu, default value is 0. |
sd.mu |
Prior standard deviation of mu, the default value is 10. |
scale.sigma.between |
Prior scale parameter for scale gamma distribution for the precision between studies. The default value is 0.5. |
df.scale.between |
Degrees of freedom of the scale gamma distribution for the precision between studies. The default value is 1, which results in a Half Cauchy distribution for the standard deviation between studies. Larger values e.g. 30 corresponds to a Half Normal distribution. |
nr.chains |
Number of chains for the MCMC computations, default 2. |
nr.iterations |
Number of iterations after adapting the MCMC, default is 10000. Some models may need more iterations. |
nr.adapt |
Number of iterations in the adaptation process, default is 1000. Some models may need more iterations during adptation. |
nr.burnin |
Number of iteration discard for burn-in period, default is 1000. Some models may need a longer burnin period. |
nr.thin |
Thinning rate, it must be a positive integer, the default value 1. |
be.quiet |
Do not print warning message if the model does not adapt. The default value is FALSE. If you are not sure about the adaptation period choose be.quiet=TRUE. |
r2jags |
Which interface is used to link R to JAGS (rjags and R2jags), default value is R2Jags=TRUE. |
The results of the object of the class bcmeta can be extracted with R2jags or with rjags. In addition a summary, a print and a plot functions are implemented for this type of object.
This function returns an object of the class "bmeta". This object contains the MCMC output of each parameter and hyper-parameter in the model and the data frame used for fitting the model.
Verde, P.E. (2021) A Bias-Corrected Meta-Analysis Model for Combining Studies of Different Types and Quality. Biometrical Journal; 1–17.
## Not run: library(jarbes) #Example: ppvipd summary(bm1) plot(bm1, x.lim = c(-3, 1), y.lim = c(0, 3)) diagnostic(bm1, study.names = ppvipd$name, post.p.value.cut = 0.1, lwd.forest = 1, shape.forest = 4) # Example: Stemcells data("stemcells") stemcells$TE = stemcells$effect.size stemcells$seTE = stemcells$se.effect bm2 = bmeta(stemcells) summary(bm2) plot(bm2, x.lim = c(-1, 7), y.lim = c(0, 1)) diagnostic(bm2, study.names = stemcells$trial, post.p.value.cut = 0.05, lwd.forest = 0.5, shape.forest = 4) diagnostic(bm2, post.p.value.cut = 0.05, lwd.forest = 0.5, shape.forest = 4) ## End(Not run)
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