mcp: fitted.mcpfit – R documentation

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mcp

fitted.mcpfit

Expected Values from the Posterior Predictive Distribution

Description

Expected Values from the Posterior Predictive Distribution

Usage

## S3 method for class 'mcpfit'
fitted(
  object,
  newdata = NULL,
  summary = TRUE,
  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
`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.

Author(s)

Jonas Kristoffer Lindeløv jonas@lindeloev.dk

Examples

fitted(ex_fit)
fitted(ex_fit, probs = c(0.1, 0.5, 0.9))  # With median and 80% credible interval.
fitted(ex_fit, summary = FALSE)  # Samples instead of summary.
fitted(ex_fit,
       newdata = data.frame(time = c(-5, 20, 300)),  # New data
       probs = c(0.025, 0.5, 0.975))

mcp

Regression with Multiple Change Points

v0.3.0

GPL-2

Authors

Jonas Kristoffer Lindeløv [aut, cre] (<https://orcid.org/0000-0003-4565-0595>)

Initial release

2020-07-27