These functions are wrappers around the E_loo
function of the loo package.
# S3 method for brmsfit
loo_predict(
object,
type = c("mean", "var", "quantile"),
probs = 0.5,
psis_object = NULL,
resp = NULL,
...
)
# S3 method for brmsfit
loo_linpred(
object,
type = c("mean", "var", "quantile"),
probs = 0.5,
psis_object = NULL,
resp = NULL,
...
)
# S3 method for brmsfit
loo_predictive_interval(object, prob = 0.9, psis_object = NULL, ...)An object of class brmsfit.
The statistic to be computed on the results.
Can by either "mean" (default), "var", or
"quantile".
A vector of quantiles to compute.
Only used if type = quantile.
An optional object returned by psis.
If psis_object is missing then psis is executed
internally, which may be time consuming for models fit to very large datasets.
Optional names of response variables. If specified, predictions are performed only for the specified response variables.
Optional arguments passed to the underlying methods that is
log_lik, as well as
posterior_predict or
posterior_linpred.
For loo_predictive_interval, a scalar in \((0,1)\)
indicating the desired probability mass to include in the intervals. The
default is prob = 0.9 (\(90\)% intervals).
loo_predict and loo_linpred return a vector with one
element per observation. The only exception is if type = "quantile"
and length(probs) >= 2, in which case a separate vector for each
element of probs is computed and they are returned in a matrix with
length(probs) rows and one column per observation.
loo_predictive_interval returns a matrix with one row per
observation and two columns.
loo_predictive_interval(..., prob = p) is equivalent to
loo_predict(..., type = "quantile", probs = c(a, 1-a)) with
a = (1 - p)/2, except it transposes the result and adds informative
column names.
if (FALSE) {
## data from help("lm")
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
d <- data.frame(
weight = c(ctl, trt),
group = gl(2, 10, 20, labels = c("Ctl", "Trt"))
)
fit <- brm(weight ~ group, data = d)
loo_predictive_interval(fit, prob = 0.8)
## optionally log-weights can be pre-computed and reused
psis <- loo::psis(-log_lik(fit), cores = 2)
loo_predictive_interval(fit, prob = 0.8, psis_object = psis)
loo_predict(fit, type = "var", psis_object = psis)
}