predict_samples: 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
)

Value

NumericMatrix with predicted durations for each sample.

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.