View source: R/loo_conformal.R
loo_conformal | R Documentation |
Prepares for jackknife(+) conformal prediction by performing Pareto-smoothed importance sampling to yield leave-one-out residuals.
loo_conformal(fit, ...)
## Default S3 method:
loo_conformal(
fit,
truth,
chain = NULL,
trans = function(x) x,
inv_trans = function(x) x,
est_fun = c("mean", "median"),
...
)
## S3 method for class 'stanreg'
loo_conformal(
fit,
trans = function(x) x,
inv_trans = function(x) x,
est_fun = c("mean", "median"),
...
)
## S3 method for class 'brmsfit'
loo_conformal(
fit,
trans = function(x) x,
inv_trans = function(x) x,
est_fun = c("mean", "median"),
...
)
fit |
Model fit; an object with |
... |
Ignored. |
truth |
True values to predict. Not required for |
chain |
An integer vector identifying the chain numbers for the posterior draws. Should be provided if multiple chains are used. |
trans , inv_trans |
A pair of functions to transform the predictions before performing conformal inference. |
est_fun |
Whether to use the posterior |
A modified fit
object with an additional class conformal
.
Calling predictive_interval()
on this new object will yield conformal
intervals.
Vehtari, A., Simpson, D., Gelman, A., Yao, Y., & Gabry, J. (2015). Pareto smoothed importance sampling. arXiv preprint arXiv:1507.02646.
if (requireNamespace("rstanarm", quietly=TRUE)) suppressWarnings({
library(rstanarm)
# fit a simple linear regression
m = stan_glm(mpg ~ disp + cyl, data=mtcars,
chains=1, iter=1000,
control=list(adapt_delta=0.999), refresh=0)
loo_conformal(m)
})
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