compute_p_si: Compute choice probabilities at posterior samples
In RprobitB: Bayesian Probit Choice Modeling
compute_p_si R Documentation
Compute choice probabilities at posterior samples
Description
This function computes the probability for each observed choice at the
(normalized, burned and thinned) samples from the posterior.
These probabilities are required to compute the WAIC and the
marginal model likelihood mml.
Usage
compute_p_si(x, ncores = parallel::detectCores() - 1, recompute = FALSE)
Arguments
x
An object of class RprobitB_fit.
ncores
[integer(1)]
The number of cores for parallel computation.
If set to 1, no parallel backend is used.
recompute
[logical(1)]
Recompute the probabilities?
Value
The object x, including the object p_si, which is a matrix of
probabilities, observations in rows and posterior samples in columns.
RprobitB documentation built on Aug. 26, 2025, 1:08 a.m.
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| compute_p_si | R Documentation |
Compute choice probabilities at posterior samples
Description
This function computes the probability for each observed choice at the (normalized, burned and thinned) samples from the posterior.
These probabilities are required to compute the WAIC and the
marginal model likelihood mml.
Usage
compute_p_si(x, ncores = parallel::detectCores() - 1, recompute = FALSE)
Arguments
x |
An object of class |
ncores |
[ If set to 1, no parallel backend is used. |
recompute |
[ |
Value
The object x, including the object p_si, which is a matrix of
probabilities, observations in rows and posterior samples in columns.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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