control_Gibbs: Control parameters for the Gibbs sampling algorithm
In tommasorigon/DLSBP: Tractable Bayesian density regression via logit stick-breaking priors
control_Gibbs R Documentation
Control parameters for the Gibbs sampling algorithm
Description
The control_Gibbs function can be used for specifying the technical settings (i.e. the number of MCMC iterations, the burn-in, and the initialization method), of the LSBP_Gibbs function.
Usage
control_Gibbs(R = 5000, burn_in = 1000, method_init = "cluster")
Arguments
R
An integer indicating the number of MCMC iterations to be computed after the burn-in.
burn_in
An integer indicating the number of MCMC iterations discarded as burn-in period.
method_init
The initialization method. The default method_init='cluster' partitions the covariates using the clara clustering algorithm. Other available options are: method_init='random' and method_init='deterministic'.
Value
The inputs are converted into a list. Missing arguments are filled with default values.
tommasorigon/DLSBP documentation built on Feb. 28, 2023, 8:50 a.m.
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| control_Gibbs | R Documentation |
Control parameters for the Gibbs sampling algorithm
Description
The control_Gibbs function can be used for specifying the technical settings (i.e. the number of MCMC iterations, the burn-in, and the initialization method), of the LSBP_Gibbs function.
Usage
control_Gibbs(R = 5000, burn_in = 1000, method_init = "cluster")
Arguments
R |
An integer indicating the number of MCMC iterations to be computed after the |
burn_in |
An integer indicating the number of MCMC iterations discarded as burn-in period. |
method_init |
The initialization method. The default |
Value
The inputs are converted into a list. Missing arguments are filled with default values.
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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