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", ... )Arguments object
An mcpfit
object.
A tibble
or a data.frame
containing predictors in the model. If NULL
(default), the original data is used.
Summarise at each x-value
probsVector of quantiles. Only in effect when summary == TRUE
.
Boolean. For binomial models, plot on raw data (rate = FALSE
) or response divided by number of trials (rate = TRUE
). If FALSE, linear interpolation on trial number is used to infer trials at a particular x.
TRUE/FALSE. Plot using prior samples? Useful for mcp(..., sample = "both")
What to plot on the y-axis. One of
"ct"
: The central tendency which is often the mean after applying the link function (default).
"sigma"
: The variance
"ar1"
, "ar2"
, etc. depending on which order of the autoregressive effects you want to plot.
TRUE: All varying effects (fit$pars$varying
).
FALSE: No varying efects (c()
).
Character vector: Only include specified varying parameters - see fit$pars$varying
.
Whether to include autoregressive effects.
TRUE:
Compute autoregressive residuals. Requires the response variable in newdata
.
FALSE:
Disregard the autoregressive effects. For family = gaussian()
, predict()
just use sigma
for residuals.
Integer or NULL
. Number of samples to return/summarise. If there are varying effects, this is the number of samples from each varying group. NULL
means "all". Ignored if both are FALSE
. More samples trade speed for accuracy.
One of "tidy" or "matrix". Controls the output format when summary == FALSE
. See more under "value"
Currently unused
ValueIf summary = TRUE
: A tibble
with the posterior mean for each row in newdata
, If newdata
is NULL
, the data in fit$data
is used.
If summary = FALSE
and samples_format = "tidy"
: A tidybayes
tibble
with all the posterior samples (Ns
) evaluated at each row in newdata
(Nn
), i.e., with Ns x Nn
rows. If there are varying effects, the returned data is expanded with the relevant levels for each row.
The return columns are:
Predictors from newdata
.
Sample descriptors: ".chain", ".iter", ".draw" (see the tidybayes
package for more), and "data_row" (newdata
rownumber)
Sample values: one column for each parameter in the model.
The estimate. Either "predict" or "fitted", i.e., the name of the type
argument.
If summary = FALSE
and samples_format = "matrix"
: An N_draws
X nrows(newdata)
matrix with fitted/predicted values (depending on type
). This format is used by brms
and it's useful as yrep
in bayesplot::ppc_*
functions.
pp_eval
fitted.mcpfit
residuals.mcpfit
predict(ex_fit) # Evaluate at each ex_fit$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) ) # }
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