bayesresiduals

Computes the posterior probabilities that Bayesian residuals exceed a cutoff value for a 
linear regression model with a noninformative prior

models

A collection of functions helpful in learning the basic tenets of Bayesian statistical inference.  It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions.  It contains MCMC algorithms for summarizing posterior distributions defined by the user.  It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.

Jim Albert

LearnBayes

Functions for Learning Bayesian Inference

bayesresiduals function

<dl> <dt>lmfit</dt>
<dd>output of the regression function lm</dd> <dt>post</dt>
<dd>list with components beta, matrix of simulated draws of regression parameter, and 
sigma, vector of simulated draws of sampling standard deviation</dd> <dt>k</dt>
<dd>cut-off value that defines an outlier</dd></dl>

Arguments

Author

Computation of posterior residual outlying probabilities for a linear regression model — bayesresiduals

<dl>

 <dt>lmfit</dt>
<dd>output of the regression function lm</dd>

 <dt>post</dt>
<dd>list with components beta, matrix of simulated draws of regression parameter, and 
sigma, vector of simulated draws of sampling standard deviation</dd>

 <dt>k</dt>
<dd>cut-off value that defines an outlier</dd>

</dl>

bayesresiduals: Computation of posterior residual outlying probabilities for a linear regression model

Description

Usage

Value

Arguments

Author

Examples