Samples from the Posterior Predictive Distribution
# S3 method for mcpfit predict( object, newdata = NULL, summary = TRUE, probs = TRUE, rate = TRUE, prior = FALSE, which_y = "ct", varying = TRUE, arma = TRUE, nsamples = NULL, samples_format = "tidy", ... )
object  An 

newdata  A 
summary  Summarise at each xvalue 
probs  Vector of quantiles. Only in effect when 
rate  Boolean. For binomial models, plot on raw data ( 
prior  TRUE/FALSE. Plot using prior samples? Useful for 
which_y  What to plot on the yaxis. One of

varying 

arma  Whether to include autoregressive effects.

nsamples  Integer or 
samples_format  One of "tidy" or "matrix". Controls the output format when 
...  Currently unused 
If summary = TRUE
: A tibble
with the posterior mean for each row in newdata
,
If newdata
is NULL
, the data in fit$data
is used.
If summary = FALSE
and samples_format = "tidy"
: A tidybayes
tibble
with all the posterior
samples (Ns
) evaluated at each row in newdata
(Nn
), i.e., with Ns x Nn
rows. If there are
varying effects, the returned data is expanded with the relevant levels for each row.
The return columns are:
Predictors from newdata
.
Sample descriptors: ".chain", ".iter", ".draw" (see the tidybayes
package for more), and "data_row" (newdata
rownumber)
Sample values: one column for each parameter in the model.
The estimate. Either "predict" or "fitted", i.e., the name of the type
argument.
If summary = FALSE
and samples_format = "matrix"
: An N_draws
X nrows(newdata)
matrix with fitted/predicted
values (depending on type
). This format is used by brms
and it's useful as yrep
in
bayesplot::ppc_*
functions.
predict(ex_fit) # Evaluate at each ex_fit$data # \donttest{ predict(ex_fit, probs = c(0.1, 0.5, 0.9)) # With median and 80% credible interval. predict(ex_fit, summary = FALSE) # Samples instead of summary. predict( ex_fit, newdata = data.frame(time = c(5, 20, 300)), # Evaluate probs = c(0.025, 0.5, 0.975) ) # }