Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
R
 
 
 
 
man
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

R/ipriorBVS: Bayesian Variable Selection for Linear Models using I-priors

Bayesian variable selection for linear models using I-priors in R. This work is part of the PhD project entitled Regression Modelling with Priors using Fisher Information Covariance Kernels (I-priors). Visit http://phd.haziqj.ml for details.

Benchmark data (Tibshirani, 1996)

A toy data set designed by Tibshirani (1996), often used to compare variable selection methods. n = 50 data points are generated from a linear model with parameters beta = c(3, 1.5, 0, 0, 2, 0, 0, 0) and sigma = 3. The X are generated from a normal distribution with mean zero, and the correlation between the ith and jth variable is 0.5 ^ abs(i - j). This is implemented in the gen_benchmark() function included in the package.

(dat <- gen_benchmark(n = 50, sd = 3, seed = 123))
## n    =  50 
## p    =  8 
## SNR2 =  1.787658

Model fit

The model fitted either using formula or non-formula syntax. We are then able to obtain posterior inclusion probabilities (PIPs) for the each variable, and also posterior model probabilities (PMPs). For comparison, Bayes factors and deviances are reported as well.

runjags::runjags.options(silent.jags = TRUE, silent.runjags = TRUE)
(mod <- ipriorBVS(y ~ ., dat))
##            PIP     1     2     3     4     5
## X.1      1.000     x     x     x     x     x
## X.2      0.840     x     x     x     x     x
## X.3      0.568           x     x            
## X.4      0.524                 x     x     x
## X.5      0.644     x     x                 x
## X.6      0.294                              
## X.7      0.480                 x            
## X.8      0.238                              
## PMP            0.061 0.048 0.041 0.040 0.037
## BF             1.000 0.785 0.662 0.648 0.604
## Deviance       93.76 92.07 91.42 96.29 94.16

Coefficients

The model coefficients are averaged across all probable sub-models, which yields a kind of "model-averaged" coefficients.

coef(mod)
##               PIP   Mean   S.D.   2.5%  97.5%
## (Intercept) 1.000 -0.128  0.459 -1.069  0.739
## X.1         1.000  2.707  0.636  1.588  4.053
## X.2         0.840  1.547  0.878  0.000  2.787
## X.3         0.568  0.607  0.705  0.000  2.119
## X.4         0.524  0.468  0.585 -0.002  1.727
## X.5         0.644  0.858  0.903 -0.110  2.672
## X.6         0.294 -0.158  0.399 -1.273  0.324
## X.7         0.480  0.373  0.523 -0.054  1.582
## X.8         0.238  0.054  0.246 -0.333  0.815

Copyright (C) 2017 Haziq Jamil.

About

Bayesian Variable Selection using I-priors in R

Resources

License

Releases

No releases published

Packages

No packages published

Languages