R/predictive_error.R
predictive_error.RdGeneric function and default method for computing predictive errors \(y - y^{rep}\) (in-sample, for observed \(y\)) or \(y - \tilde{y}\) (out-of-sample, for new or held-out \(y\)). See predictive_error.stanreg() in the rstanarm package for an example.
predictive_error(object, ...)
# S3 method for default
predictive_error(object, y, ...)The object to use.
Arguments passed to methods. See the methods in the rstanarm package for examples.
For the default method, a vector of y values the same length as
the number of columns in the matrix used as object.
predictive_error() methods should return a \(D\) by \(N\)
matrix, where \(D\) is the number of draws from the posterior predictive distribution and \(N\) is the number of data points being predicted per draw.
The default method just takes object to be a matrix and y to be a
vector.
The rstanarm package (mc-stan.org/rstanarm) for example methods (CRAN, GitHub).
Guidelines and recommendations for developers of R packages interfacing with Stan and a demonstration getting a simple package working can be found in the vignettes included with rstantools and at mc-stan.org/rstantools/articles.
# default method
y <- rnorm(10)
ypred <- matrix(rnorm(500), 50, 10)
pred_errors <- predictive_error(ypred, y)
dim(pred_errors)
#> [1] 50 10
head(pred_errors)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 0.5273861 0.07515823 1.513581 1.4204596 -3.4765209 -0.40151181
#> [2,] -3.3664225 0.81258188 2.744778 -0.8234573 -1.3396243 -1.43882134
#> [3,] -0.5546069 2.37300064 1.780391 -0.2672072 0.7954730 -0.41251601
#> [4,] -0.2880340 -0.76885376 0.723278 -0.5925147 -0.7830652 1.52582725
#> [5,] -0.5131371 3.30739106 1.233684 0.3282228 -0.8251587 0.01187749
#> [6,] 0.1832783 1.69463620 0.412581 1.1282433 -2.4243409 -0.55389915
#> [,7] [,8] [,9] [,10]
#> [1,] 0.3291168 0.7518855 1.0182404 2.1367075
#> [2,] -0.9216418 1.3862216 0.6818612 1.1718269
#> [3,] -0.7428718 2.1940964 0.7415774 -1.0785962
#> [4,] 1.6353221 1.0977800 2.3479025 0.9887451
#> [5,] -0.3287231 2.4549490 0.3920619 2.0133653
#> [6,] 0.6492479 0.7439346 0.9827939 2.3187384
# Also see help("predictive_error", package = "rstanarm")