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approximate leave-one-out (LOO) cross-validation for Bayesian models using Pareto smoothed importance sampling (PSIS)\n\nYou must use an _ll model variation (see `findModel`).\n\n\n```r\ntoLoo(fit, ...)\n```\n\n## Arguments\n\n- `fit`: a `stanfit` object\n- `...`: Additional options passed to `loo`.\n\n## Returns\n\na loo object\n\n\n\n## Examples\n\n```r\npalist <- letters[1:10]\ndf <- twoLevelGraph(palist, 300)\ntheta <- rnorm(length(palist))\nnames(theta) <- palist\ndf <- generateItem(df, theta, th=rep(0.5, 4))\n\ndf <- filterGraph(df)\ndf <- normalizeData(df)\ndl <- prepCleanData(df)\ndl$scale <- 1.5\n\nm1 <- pcStan(\"unidim_ll\", dl)\n\nloo1 <- toLoo(m1, cores=1)\nprint(loo1)\n```\n\n## See Also\n\n`outlierTable`, `loo`"}]}]}]}]}]}],["$","div",null,{"className":"lg:w-1/3 lg:border-l px-1 sm:px-8 pt-8 sm:pt-1","children":[["$","div",null,{"className":"mb-8 space-y-8","children":[["$","a",null,{"href":"/packages/pcFactorStan","className":"w-full inline-flex items-center 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