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).

Usage

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

Value

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.

See also

fit_cumhist, predict.cumhist

Examples

# \donttest{
br_fit <- fit_cumhist(br_singleblock, state = "State", duration = "Duration")
#> 
#> SAMPLING FOR MODEL 'historylm' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 1: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 1: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 1: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 1: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 4.338 seconds (Warm-up)
#> Chain 1:                4.486 seconds (Sampling)
#> Chain 1:                8.824 seconds (Total)
#> Chain 1: 
history_difference_summary <- predict_history(br_fit, "difference")

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