runModel {meta4diag} | R Documentation |
Run the bivariate model.
Description
Run the bivariate model with INLA. This function is used within the main function meta4diag()
and can also be used as a separate function.
Usage
runModel(outdata, outpriors, link = "logit",
quantiles = c(0.025, 0.5, 0.975), verbose = FALSE, num.threads = 1)
Arguments
outdata |
A data file for internal use. |
outpriors |
A list of prior settings prepared for internal use, see |
link |
A string specifying the link function used in the model. Options are "logit", "probit" and "cloglog". |
quantiles |
A vector of quantiles, p(0), p(1),... to compute for each posterior marginal. The function returns, for each posterior marginal, the values x(0), x(1),... such that
The default value are c(0.025, 0.5, 0.975). Not matter what other values are going to be given, the estimates for these 3 quantiles are always returned. |
verbose |
Boolean (default:FALSE) indicating whether the program should run in a verbose mode. |
num.threads |
Maximum number of threads the inla-program will use. xFor Windows this defaults to 1, otherwise its defaults to NULL (for which the system takes over control). |
Value
A INLA
object which will be used into function makeObject()
.
Author(s)
Jingyi Guo and Andrea Riebler
References
Havard Rue, Sara Martino, and Nicholas Chopin (2009). Approximate Bayesian Inference for Latent Gaussian Models Using Integrated Nested Laplace Approximations. Journal of the Royal Statistical Society B, 71, 319-392. (www.r-inla.org)
See Also
makeData, makePriors, makeObject, meta4diag, inla
Examples
## Not run:
if(requireNamespace("INLA", quietly = TRUE)) {
require("INLA", quietly = TRUE)
data(Catheter)
outdata = makeData(data=Catheter,model.type=1,covariates="type")
outpriors = makePriors(var.prior = "invgamma", cor.prior = "normal",
var.par = c(0.25, 0.025), cor.par = c(0, 5))
runModel(outdata, outpriors, link = "logit",
quantiles = c(0.025, 0.5, 0.975), verbose = FALSE)
}
## End(Not run)