predict_samples: Computes prediction for a each sample.

In bistablehistory: Cumulative History Analysis for Bistable Perception Time Series

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.

bistablehistory documentation built on Sept. 13, 2023, 5:07 p.m.