elpd {BayesSUR} | R Documentation |
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).
elpd(object, method = "LOO")
object |
an object of class |
method |
the name of the prediction accuracy index. Default is the |
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"
.
Vehtari, A., Gelman, A., Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing, 27(5): 1413–1432.
data("exampleEQTL", package = "BayesSUR") hyperpar = list( a_w = 2 , b_w = 5 ) set.seed(9173) fit <- BayesSUR(Y = exampleEQTL[["blockList"]][[1]], X = exampleEQTL[["blockList"]][[2]], data = exampleEQTL[["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")