R: Computation of posterior residual outlying probabilities for...

bayesresiduals {LearnBayes} | R Documentation

## Computation of posterior residual outlying probabilities for a linear regression model

### Description

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

### Usage

    bayesresiduals(lmfit,post,k)

### Arguments

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

### Value

vector of posterior outlying probabilities

### Author(s)

Jim Albert

### Examples

    chirps=c(20,16.0,19.8,18.4,17.1,15.5,14.7,17.1,15.4,16.2,15,17.2,16,17,14.1)
    temp=c(88.6,71.6,93.3,84.3,80.6,75.2,69.7,82,69.4,83.3,78.6,82.6,80.6,83.5,76.3)
    X=cbind(1,chirps)
    lmfit=lm(temp~X)
    m=1000
    post=blinreg(temp,X,m)
    k=2
    bayesresiduals(lmfit,post,k)

[Package LearnBayes version 2.15 Index]