Computes predicted dominance phase durations using posterior predictive distribution.

Computes predicted dominance phase durations using fitted model.

Usage

# S3 method for 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

# \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: 4.393 seconds (Warm-up)
#> Chain 1:                5.113 seconds (Sampling)
#> Chain 1:                9.506 seconds (Total)
#> Chain 1: 
predict(br_fit)
#>  [1]       NA       NA 3.111925 3.038940 3.108320 4.978573 3.556128 4.392861
#>  [9] 3.324947 3.697454 4.186017 5.256282 4.150889 4.602041 3.813495 3.978832
#> [17] 4.107675 3.412406 3.866068 3.570002 4.050164 3.811569 4.391107 4.467650
#> [25] 3.885314 3.803299 5.091503 4.191165 5.187422 4.862474 3.569223 4.384790
#> [33] 3.821754 4.367063 4.813696 3.693666 4.361824 5.414457 4.442852 4.041422
#> [41] 2.964934 2.858787 4.374155 5.081645 3.797854 5.218570 2.600773       NA
#> [49] 2.545678       NA 2.619134 3.206757 4.191966 3.708508 5.145146 5.642685
#> [57] 3.601663       NA 2.363356 3.245703 4.477596 3.025451 3.688626 4.595827
#> [65] 4.705550 3.282014 3.727020 4.793072 4.725245 4.898412 5.619271 5.559445
#> [73] 3.974080 4.424304 4.665970       NA

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