mcp (version 0.3.3)

predict.mcpfit: Samples from the Posterior Predictive Distribution

Description

Samples from the Posterior Predictive Distribution

Usage

# 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",
  ...
)

Value

• 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.

Arguments

object

An mcpfit object.

newdata

A tibble or a data.frame containing predictors in the model. If NULL (default), the original data is used.

summary

Summarise at each x-value

probs

Vector of quantiles. Only in effect when summary == TRUE.

rate

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.

prior

TRUE/FALSE. Plot using prior samples? Useful for mcp(..., sample = "both")

which_y

What to plot on the y-axis. One of

• "ct": The central tendency which is often the mean after applying the link function.

• "sigma": The variance

• "ar1", "ar2", etc. depending on which order of the autoregressive effects you want to plot.

varying

One of:

• TRUE All varying effects (fit$pars$varying).

• FALSE No varying effects (c()).

• Character vector: Only include specified varying parameters - see fit$pars$varying.

arma

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.

nsamples

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.

samples_format

One of "tidy" or "matrix". Controls the output format when summary == FALSE. See more under "value"

...

Currently ignored.

Author

Jonas Kristoffer Lindeløv

See Also

pp_eval fitted.mcpfit residuals.mcpfit

Examples

# \donttest{
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)
)
# }