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.