loo_predict.brmsfit {brms}  R Documentation 
Compute Weighted Expectations Using LOO
Description
These functions are wrappers around the E_loo
function of the loo package.
Usage
## S3 method for class 'brmsfit'
loo_predict(
object,
type = c("mean", "var", "quantile"),
probs = 0.5,
psis_object = NULL,
resp = NULL,
...
)
## S3 method for class 'brmsfit'
loo_linpred(
object,
type = c("mean", "var", "quantile"),
probs = 0.5,
psis_object = NULL,
resp = NULL,
...
)
## S3 method for class 'brmsfit'
loo_predictive_interval(object, prob = 0.9, psis_object = NULL, ...)
Arguments
object 
An object of class 
type 
The statistic to be computed on the results.
Can by either 
probs 
A vector of quantiles to compute.
Only used if 
psis_object 
An optional object returned by 
resp 
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

prob 
For 
Value
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, 1a))
with
a = (1  p)/2
, except it transposes the result and adds informative
column names.
Examples
## Not run:
## 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 logweights can be precomputed 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)
## End(Not run)