predict.cumhist: Computes predicted dominance phase durations using posterior...

In bistablehistory: Cumulative History Analysis for Bistable Perception Time Series

Computes predicted dominance phase durations using posterior predictive distribution.

Description

Computes predicted dominance phase durations using fitted model.

Usage

## S3 method for class 'cumhist'
predict(
  object,
  summary = TRUE,
  probs = NULL,
  full_length = TRUE,
  predict_history = NULL,
  ...
)

Arguments

object	An object of class cumhist
summary	Whether summary statistics should be returned instead of raw sample values. Defaults to TRUE
probs	The percentiles used to compute summary, defaults to NULL (no CI).
full_length	Only for summary = TRUE, whether the summary table should include rows with no predictions. I.e., rows with mixed phases, first/last dominance phase in the run, etc. See preprocess_data(). Defaults to TRUE.
predict_history	Option to predict a cumulative history state (or their difference). It is disabled by default by setting it to NULL. You can specify "1" or "2" for cumulative history for the first or second perceptual states (with indexes 1 and 2, respectively), "dominant" or "suppressed" for cumulative history for states that either dominant or suppressed during the following phase, "difference" for difference between suppressed and dominant. See cumulative history vignette for details.
...	Unused

Value

If summary=FALSE, a numeric matrix iterationsN x clearN. If summary=TRUE but probs=NULL a vector of mean predicted durations or requested cumulative history values. If summary=TRUE and probs is not NULL, a data.frame with a column "Predicted" (mean) and a column for each specified quantile.

See Also

fit_cumhist

Examples

br_fit <- fit_cumhist(br_singleblock, state = "State", duration = "Duration")
predict(br_fit)

# full posterior prediction samples
predictions_samples <- predict(br_fit, summary=FALSE)

bistablehistory documentation built on Sept. 13, 2023, 5:07 p.m.