compute_p_si: Compute choice probabilities at posterior samples
In RprobitB: Bayesian Probit Choice Modeling
View source: R/model_selection.R
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
This function is parallelized, set the number of cores here.
recompute
Set to TRUE to 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 May 29, 2024, 7:59 a.m.
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.
View source: R/model_selection.R
| 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 |
This function is parallelized, set the number of cores here. |
recompute |
Set to |
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.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.