Uses the output of [sampling()] and a target grid to generate
predicted coverage probabilities.
Usage
# S3 method for class 'imugap_fit'
predict(object, target, posterior_size = NULL, ...)Arguments
- object
an
imugap_fitobject returned bysampling()- target
a
[data.frame()]of target populations to predict for- posterior_size
optional single positive integer. When set, predict over only this many draws, taken from the end of each chain (the converged tail). Must be a multiple of the number of chains; a value that isn't is rounded up to the next multiple, with a warning. Must not exceed the number of draws in the fit. Defaults to
NULL, which uses every draw.- ...
additional arguments (currently ignored)
Value
An object of class imugap_predict wrapping the 3D array of predicted
draws and the canonical target dataset.
Details
The [predict()] method takes an imugap_fit object (typically the output of
[sampling()]) and a target grid (typically output from [create_target()]),
and generates predicted coverage probabilities for each entry in the target.
The [predict()] method can be used to generate estimated coverage for any
location, cohort, or age considered within the bounds of the original
sampling fit. Particularly, this includes enclosing locations without specific
observation data, as long as those locations are somewhere in the
locations hierarchy.
By default predict() uses every posterior draw in the fit. Supply
posterior_size to predict over a sub-sample taken from the end of each
chain; this is how the bundled predict_sim fixture is kept small. The
returned draws keep the per-chain structure (iterations x chains x targets).
When a sub-sample is taken predict() warns that it has not checked whether
those draws are adequate (chain mixing, effective sample size).
Examples
# \donttest{
# Load example fit object and target population
data("fit_sim", package = "imuGAP")
data("target_sim", package = "imuGAP")
# Generate predictions over 100 posterior draws
preds <- predict(fit_sim, target = target_sim, posterior_size = 100)
#> Warning: predict() is using a sub-sample of 100 posterior draws and does not check whether it is adequate (chain mixing, effective sample size); verify the sufficiency statistics yourself.
# }