mcp: tidy_samples – R documentation

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tidy_samples

Get tidy samples with or without varying effects

Description

Returns in a format useful for fit$simulate() with population parameters in wide format and varying effects in long format (the number of rows will be nsamples * n_levels_in_varying).

Usage

tidy_samples(
  fit,
  population = TRUE,
  varying = TRUE,
  absolute = FALSE,
  prior = FALSE,
  nsamples = NULL
)

Arguments

`fit`	An `mcpfit` object
`population`	TRUE: All population effects. Same as `fit$pars$population`. FALSE: No population effects. Same as `c()`. Character vector: Only include specified population parameters - see `fit$pars$population`.
`varying`	TRUE: All varying effects (`fit$pars$varying`). FALSE: No varying efects (`c()`). Character vector: Only include specified varying parameters - see `fit$pars$varying`.
`absolute`	TRUE: Returns the absolute location of all varying change points. FALSE: Just returns the varying effects. Character vector: Only do absolute transform for these varying parameters - see `fit$pars$varying`. OBS: This currently only applies to varying change points. It is not implemented for `rel()` regressors yet.
`prior`	TRUE/FALSE. Summarise prior instead of posterior?
`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.

Value

tibble of posterior draws in tidybayes format.

Author(s)

Jonas Kristoffer Lindeløv jonas@lindeloev.dk

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