fullmodel.ssq

A utility function for reference: Returns a list with R2 and sum of squares
for the OLS model encompassing all potential covariates that are included in
a bma object.

utilities

Bayesian Model Averaging for linear models with a wide choice of (customizable) priors. Built-in priors include coefficient priors (fixed, hyper-g and empirical priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches. Post-processing functions allow for inferring posterior inclusion and model probabilities, various moments, coefficient and predictive densities. Plotting functions available for posterior model size, MCMC convergence, predictive and coefficient densities, best models representation, BMA comparison. Also includes Bayesian normal-conjugate linear model with Zellner's g prior, and assorted methods.

Stefan Zeugner

Bayesian Model Averaging Library

fullmodel.ssq function

<dl><dt>yX.data</dt>
<dd>a bma object (cf. <code>bms</code>) - alternatively a
<a href="/link/data.frame?package=BMS&version=0.3.5" data-mini-rdoc="BMS::data.frame">data.frame</a> or <a href="/link/matrix?package=BMS&version=0.3.5" data-mini-rdoc="BMS::matrix">matrix</a> whose first column is the dependent
variable</dd></dl>

Arguments

OLS Statistics for the Full Model Including All Potential Covariates — fullmodel.ssq

<dl>

<dt>yX.data</dt>
<dd>a bma object (cf. <code>bms</code>) - alternatively a
<a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a> or <a href='https://rdrr.io/r/base/matrix.html'>matrix</a> whose first column is the dependent
variable</dd>

</dl>

fullmodel.ssq: OLS Statistics for the Full Model Including All Potential Covariates

Description

Usage

Value

Arguments

See Also

Examples