BayesSUR (version 1.1-2)

elpd: measure the prediction accuracy by the expected log pointwise predictive density

Description

Measure the prediction accuracy by the elpd (expected log pointwise predictive density). The out-of-sample predictive fit can either be estimated by Bayesian leave-one-out cross-validation (LOO) or by widely applicable information criterion (WAIC) (Vehtari et al. 2017).

Usage

elpd(object, method = "LOO")

Arguments

object

an object of class "BayesSUR"

method

the name of the prediction accuracy index. Default is the "LOO" (Bayesian LOO estimate of out-of-sample predictive fit). The other index is the "WAIC" (widely applicable information criterion). For the HRR models, both "LOO" and "WAIC" are computed based on the multivate t-distribution of the posterior predictive rather than approximation of importance sampling.

Value

Return the predictiion accuracy measure from an object of class "BayesSUR". It is elpd.loo if the argumnet method="LOO" and elpd.WAIC if method="WAIC".

References

Vehtari, A., Gelman, A., Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing, 27(5): 1413<U+2013>1432.

Examples

# NOT RUN {
data("example_eQTL", package = "BayesSUR")
hyperpar = list( a_w = 2 , b_w = 5 )

set.seed(9173)
fit <- BayesSUR(Y = example_eQTL[["blockList"]][[1]], 
                X = example_eQTL[["blockList"]][[2]],
                data = example_eQTL[["data"]], outFilePath = tempdir(),
                nIter = 100, burnin = 50, nChains = 2, gammaPrior = "hotspot",
                hyperpar = hyperpar, tmpFolder = "tmp/", output_CPO=TRUE)

## check output
# print the prediction accuracy elpd (expected log pointwise predictive density) 
# by the Bayesian LOO estimate of out-of-sample predictive fit
elpd(fit, method="LOO")

# }

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