LearnBayes: bayesresiduals – R documentation

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bayesresiduals

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)

LearnBayes

Functions for Learning Bayesian Inference

v2.15.1

GPL (>= 2)

Authors