predict_history function

Computes predicted cumulative history using posterior predictive distribution.

Computes predicted cumulative history using fitted model. This is just a wrapper for predict(object, summary, probs, full_length,predict_history=history_type).

predict_history(
  object,
  history_type,
  summary = TRUE,
  probs = NULL,
  full_length = TRUE,
  ...
)

Arguments

• object: An object of class cumhist
• history_type: "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.
• 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.
• ...: Unused

Returns

If summary=FALSE, a numeric matrix iterationsN x clearN. If summary=TRUE but probs=NULL a vector of 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.

Examples

br_fit <- fit_cumhist(br_singleblock, state = "State", duration = "Duration")
history_difference_summary <- predict_history(br_fit, "difference")

# full posterior prediction samples
history_difference <- predict_history(br_fit,
                                      "difference",
                                      summary = FALSE,
                                      full_length = TRUE)

See Also

fit_cumhist, predict.cumhist