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Fits and Predictions given Draws and data

Source: R/mcpfit_methods.R
pp_eval.Rd

Fits and Predictions given Draws and data

Usage

pp_eval(
  object,
  newdata = NULL,
  summary = TRUE,
  type = "fitted",
  probs = TRUE,
  rate = TRUE,
  prior = FALSE,
  dpar = "epred",
  varying = TRUE,
  arma = TRUE,
  ndraws = NULL,
  samples_format = "tidy",
  scale = "response",
  .include_fitted = FALSE,
  nsamples = lifecycle::deprecated()
)

Arguments

object

An mcpfit object.

newdata

A tibble or a data.frame containing predictors in the model. Weighted Gaussian predictions and log-likelihoods also require the weights column. If NULL (default), the original data is used.

summary

Summarise at each x-value

type

One of:

  • "fitted": return expected values. When dpar is the name of a dpar (e.g., "mu" or "sigma"), the expected value for just this dpar is returned. See also fitted().

  • "predict": return predicted values (e.g., y_predict = rnorm(N, y_fitted, sigma_fitted) for family = gaussian()). See also predict().

  • "residuals": observed y-values minus the fitted values. See also residuals().

  • "loglik": return the log-likelihood for each sample for each data point. See also log_lik(). Requires scale = "response".

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")

dpar

What distributional parameter to evaluate. This is only relevant when type == "fitted". E.g.,

  • "epred" (default): Expected value of the full model (or NULL for compatibility with brms etc.).

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

  • "sigma": The standard deviation of the residuals.

  • "ar1", "ar2", "ma1", "ma2", etc. depending on which AR or MA coefficient you want to evaluate.

varying

One of:

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

  • FALSE No varying effects (c()).

  • "cp" or "predictor": All varying effects belonging to that part of the model.

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

arma

Whether to include AR and MA effects.

  • TRUE Compute the GARMA residual recurrence. Requires the response variable in newdata.

  • FALSE Disregard AR and MA effects. For family = gaussian(), predict() uses only sigma for residuals.

ndraws

Integer or NULL. Number of posterior draws to return/summarise. If there are varying effects, this is the number of draws 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"

scale

One of

  • "response": return on the observed scale, i.e., after applying the inverse link function.

  • "linear": return on the parameter scale (where the linear trends are modelled). A linear scale is only applicable when type == "fitted" and dpar is not NULL.

.include_fitted

Internal. Include fitted values with unsummarised predictions.

nsamples

Deprecated. Use ndraws instead.

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 draws (Nd) evaluated at each row in newdata (Nn), i.e., with Nd 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.

    • Draw descriptors: ".chain", ".iteration", ".draw" (see the posterior and tidybayes packages), and data_row, the row number in the evaluated newdata.

    • Draw values: one column for each parameter in the model.

    • The estimate. Either ".epred", ".prediction", ".residual", or ".loglik" (matching tidybayes/ggdist conventions).

  • 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.

See also

fitted.mcpfit predict.mcpfit residuals.mcpfit

Author

Jonas Kristoffer Lindeløv jonas@lindeloev.dk