predict_samples {bistablehistory}

Computes prediction for a each sample.

Description

Computing prediction for each sample, recomputing cumulative history and uses fitted parameter values.

Usage

predict_samples(
  family,
  fixedN,
  randomN,
  lmN,
  istate,
  duration,
  is_used,
  run_start,
  session_tmean,
  irandom,
  fixed,
  tau_ind,
  mixed_state_ind,
  history_init,
  a,
  bH,
  bF,
  sigma
)

Arguments

family

int, distribution family: gamma (1), lognormal(2), or normal (3).

fixedN

int, number of fixed parameters (>= 0).

randomN

int, number of random factors (>= 1).

lmN

int, number of linear models (>= 1).

istate

IntegerVector, zero-based perceptual state 0 or 1, 2 is mixed state.

duration

DoubleVector, duration of a dominance phase.

is_used

IntegerVector, whether dominance phase is used for prediction (1) or not (0).

run_start

IntegerVector, 1 whenever a new run starts.

session_tmean

DoubleVector, average dominance phase duration.

irandom

IntegerVector, zero-based index of a random effect.

fixed

NumericMatrix, matrix with fixed effect values.

tau_ind

NumericMatrix, matrix with samples of tau for each random level.

mixed_state_ind

NumericMatrix, matrix with samples of mixed_state for each random level.

history_init

DoubleVector, Initial values of history for a run

a

NumericMatrix, matrix with samples of a (intercept) for each random level.

bH

NumericMatrix, matrix with sample of bH for each linear model and random level.

bF

NumericMatrix, matrix with sample of bF for each linear model and fixed factor.

sigma

DoubleVector, samples of sigma.

Value

NumericMatrix with predicted durations for each sample.