residuals.mcpfit: Compute Residuals From Mcpfit Objects

In mcp: Regression with Multiple Change Points

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	One of: TRUE All varying effects (fit$pars$varying). FALSE No varying effects (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 ignored.

Author(s)

Jonas Kristoffer Lindeløv [e-mail]

See Also

pp_eval fitted.mcpfit predict.mcpfit

Examples

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

mcp documentation built on April 1, 2023, 12:03 a.m.