mcp: residuals.mcpfit – R documentation

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residuals.mcpfit

Compute Residuals From Mcpfit Objects

Description

Equivalent to fitted(fit, ...) - fit$data[, fit$data$yvar] (or fitted(fit, ...) - newdata[, fit$data$yvar]), but with fixed arguments for fitted: rate = FALSE, which_y = 'ct', samples_format = 'tidy'.

Usage

## S3 method for class 'mcpfit'
residuals(
  object,
  newdata = NULL,
  summary = TRUE,
  probs = TRUE,
  prior = FALSE,
  varying = TRUE,
  arma = TRUE,
  nsamples = NULL,
  ...
)

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`.
`prior`	TRUE/FALSE. Plot using prior samples? Useful for `mcp(..., sample = "both")`
`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.
`...`	Currently unused

Author(s)

Jonas Kristoffer Lindeløv jonas@lindeloev.dk

Examples

residuals(ex_fit)
residuals(ex_fit, probs = c(0.1, 0.5, 0.9))  # With median and 80% credible interval.
residuals(ex_fit, summary = FALSE)  # Samples instead of summary.

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