View source: R/mcpfit_methods.R
predict.mcpfit  R Documentation 
Samples from the Posterior Predictive Distribution
## S3 method for class '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 
One of:

arma 
Whether to include autoregressive effects.

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