UPG.Diag.Logit

<code>UPG.Diag.Logit</code> computes inefficiency factors, effective sample size and effective sampling rate based on the posterior distributions in a <code>UPG.Logit</code> object.

Efficient Bayesian implementations of probit, logit, multinomial logit and binomial logit models. Functions for plotting and tabulating the estimation output are available as well. Estimation is based on Gibbs sampling where the Markov chain Monte Carlo algorithms are based on the latent variable representations and marginal data augmentation algorithms described in "Gregor Zens, Sylvia Frühwirth-Schnatter & Helga Wagner (2023). Ultimate Pólya Gamma Samplers – Efficient MCMC for possibly imbalanced binary and categorical data, Journal of the American Statistical Association <doi:10.1080/01621459.2023.2259030>".

Gregor Zens

Efficient Bayesian Algorithms for Binary and Categorical Data
Regression Models

Sylvia Frühwirth-Schnatter

Helga Wagner

UPG.Diag.Logit function

<dl><dt>object</dt>
<dd>an object of class <code>UPG.Logit</code>.</dd></dl>

Arguments

Author

MCMC Diagnostics for UPG.Logit objects — UPG.Diag.Logit

<dl>

<dt>object</dt>
<dd>an object of class <code>UPG.Logit</code>.</dd>

</dl>

UPG.Diag.Logit: MCMC Diagnostics for `UPG.Logit` objects

Description

Usage

Value

Arguments

Author