predict.mcpfit: Samples from the Posterior Predictive Distribution

In mcp: Regression with Multiple Change Points

Samples from the Posterior Predictive Distribution

Description

Samples from the Posterior Predictive Distribution

Usage

## 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",
  ...
)

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.

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.

Author(s)

Jonas Kristoffer Lindeløv [e-mail]

See Also

pp_eval fitted.mcpfit residuals.mcpfit

Examples

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)
)

mcp documentation built on April 1, 2023, 12:03 a.m.