bcmeta {jarbes} | R Documentation |
Bias-Corrected Meta-Analysis for Combining Studies of Different Types and Quality
Description
This function performers a Bayesian meta-analysis to jointly combine different types of studies. The random-effects follows a finite mixture of normal distributions.
Usage
bcmeta(
data,
mean.mu = 0,
sd.mu = 10,
scale.sigma.between = 0.5,
df.scale.between = 1,
B.lower = 0,
B.upper = 10,
a.0 = 1,
a.1 = 1,
nu = 0.5,
nu.estimate = FALSE,
b.0 = 1,
b.1 = 2,
nr.chains = 2,
nr.iterations = 10000,
nr.adapt = 1000,
nr.burnin = 1000,
nr.thin = 1,
be.quiet = FALSE,
r2jags = TRUE
)
Arguments
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. |
B.lower |
Lower bound of the bias parameter B, the default value is 0. |
B.upper |
Upper bound of the bias parameter B, the default value is 10. |
a.0 |
Parameter for the prior Beta distribution for the probability of bias. Default value is a0 = 1. |
a.1 |
Parameter for the prior Beta distribution for the probability of bias. Default value is a1 = 1. |
nu |
Parameter for the Beta distribution for the quality weights. The default value is nu = 0.5. |
nu.estimate |
If TRUE, then we estimate nu from the data. |
b.0 |
If nu.estimate = TRUE, this parameter is the shape parameter of the prior Gamma distribution for nu. |
b.1 |
If nu.estimate = TRUE, this parameter is the rate parameter of the prior Gamma distribution for nu. Note that E(nu) = b.0/b.1 and we need to choose b.0 << b.1. |
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, defualt is 1000. Some models may need more iterations during adptation. |
nr.burnin |
Number of iteration discared for burnin 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. |
Details
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.
Value
This function returns an object of the class "bcmeta". This object contains the MCMC output of each parameter and hyper-parameter in the model and the data frame used for fitting the model.
References
Verde, P. E. (2017) Two Examples of Bayesian Evidence Synthesis with the Hierarchical Meta-Regression Approach. Chap.9, pag 189-206. Bayesian Inference, ed. Tejedor, Javier Prieto. InTech.
Verde, P.E. (2021) A Bias-Corrected Meta-Analysis Model for Combining Studies of Different Types and Quality. Biometrical Journal; 1–17.
Examples
## Not run:
library(jarbes)
# Example ppvipd data
data(ppvipd)
## End(Not run)