Fits and predictions from samples and newdata

pp_eval(
  object,
  newdata = NULL,
  summary = TRUE,
  type = "fitted",
  probs = TRUE,
  rate = TRUE,
  prior = FALSE,
  which_y = "ct",
  varying = TRUE,
  arma = TRUE,
  nsamples = NULL,
  samples_format = "tidy",
  scale = "response",
  ...
)

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
type	One of: "fitted": return fitted values. See also `fitted()` "predict": return predicted values, using random dispersion around the central tendency (e.g., `y_predict = rnorm(N, y_fitted, sigma_fitted)` for `family = gaussian()`). See also `predict()`. "residuals": same as "predict" but the observed y-values are subtracted. See also `residuals()`
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 (default). `"sigma"`: The variance `"ar1"`, `"ar2"`, etc. depending on which order of the autoregressive effects you want to plot.
varying	TRUE: All varying effects (`fit$pars$varying`). FALSE: No varying efects (`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"
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).
...	Currently unused

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.

Fits and predictions from samples and newdata

Arguments

Value

See also