titanic

A dataset containing the number of survivals and the total number of persons by
passenger class, age group and gender.

datasets

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

titanic function

A data frame with 78 rows and 6 variables:<dl>
 <dt>survived</dt>
<dd>number of passengers that survived</dd> <dt>total</dt>
<dd>number of total passengers</dd> <dt>intercept</dt>
<dd>an intercept term</dd> <dt>pclass</dt>
<dd>passenger class (3 is lowest)</dd> <dt>female</dt>
<dd>binary indicator for female passenger groups</dd> <dt>age.group</dt>
<dd>age group indicator (0-5yrs, 5-10yrs, ...)</dd>
</dl>

Format

Grouped Titanic survival data. — titanic

A data frame with 78 rows and 6 variables:<dl>
 <dt>survived</dt>
<dd>number of passengers that survived</dd>

 <dt>total</dt>
<dd>number of total passengers</dd>

 <dt>intercept</dt>
<dd>an intercept term</dd>

 <dt>pclass</dt>
<dd>passenger class (3 is lowest)</dd>

 <dt>female</dt>
<dd>binary indicator for female passenger groups</dd>

 <dt>age.group</dt>
<dd>age group indicator (0-5yrs, 5-10yrs, ...)</dd>


</dl>

titanic: Grouped Titanic survival data.

Description

Usage

Arguments

Format