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

In LearnBayes: Functions for Learning Bayesian Inference

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)

Example output

           1            2            3            4            5            6 
8.650461e-03 1.890880e-01 1.734943e-02 9.350177e-06 3.873963e-11 3.108944e-08 
           7            8            9           10           11           12 
1.527867e-02 2.049203e-12 2.495306e-01 1.725156e-02 8.974623e-03 3.655172e-11 
          13           14           15 
1.560055e-05 1.102365e-06 5.687012e-02 

LearnBayes documentation built on May 1, 2019, 7:03 p.m.