summary and print methods for Bayesian model averaging objects
created by bas Bayesian Adaptive Sampling
Arguments
- x
object of class 'bas'
- digits
optional number specifying the number of digits to display
- ...
other parameters to be passed to
print.default
Details
The print methods display a view similar to print.lm . The summary
methods display a view specific to Bayesian model averaging giving the top 5
highest probability models represented by their inclusion indicators.
Summaries of the models include the Bayes Factor (BF) of each model to the
model with the largest marginal likelihood, the posterior probability of the
models, R2, dim (which includes the intercept) and the log of the marginal
likelihood.
Author
Merlise Clyde clyde@stat.duke.edu
Examples
library(MASS)
data(UScrime)
UScrime[, -2] <- log(UScrime[, -2])
crime.bic <- bas.lm(y ~ ., data = UScrime, n.models = 2^15, prior = "BIC", initprobs = "eplogp")
print(crime.bic)
#>
#> Call:
#> bas.lm(formula = y ~ ., data = UScrime, n.models = 2^15, prior = "BIC",
#> initprobs = "eplogp")
#>
#>
#> Marginal Posterior Inclusion Probabilities:
#> Intercept M So Ed Po1 Po2 LF
#> 1.0000 0.9335 0.3277 0.9910 0.7247 0.4602 0.2935
#> M.F Pop NW U1 U2 GDP Ineq
#> 0.3298 0.4963 0.8346 0.3481 0.7752 0.5254 0.9992
#> Prob Time
#> 0.9541 0.5433
summary(crime.bic)
#> P(B != 0 | Y) model 1 model 2 model 3 model 4
#> Intercept 1.0000000 1.00000 1.000000e+00 1.0000000 1.0000000
#> M 0.9335117 1.00000 1.000000e+00 1.0000000 1.0000000
#> So 0.3276563 0.00000 1.000000e+00 0.0000000 0.0000000
#> Ed 0.9910219 1.00000 1.000000e+00 1.0000000 1.0000000
#> Po1 0.7246635 1.00000 1.000000e+00 1.0000000 1.0000000
#> Po2 0.4602481 0.00000 1.000000e+00 0.0000000 0.0000000
#> LF 0.2935326 0.00000 1.000000e+00 0.0000000 0.0000000
#> M.F 0.3298168 0.00000 1.000000e+00 0.0000000 0.0000000
#> Pop 0.4962869 0.00000 1.000000e+00 0.0000000 0.0000000
#> NW 0.8346412 1.00000 1.000000e+00 1.0000000 1.0000000
#> U1 0.3481266 0.00000 1.000000e+00 0.0000000 0.0000000
#> U2 0.7752102 1.00000 1.000000e+00 1.0000000 1.0000000
#> GDP 0.5253694 0.00000 1.000000e+00 0.0000000 1.0000000
#> Ineq 0.9992058 1.00000 1.000000e+00 1.0000000 1.0000000
#> Prob 0.9541470 1.00000 1.000000e+00 1.0000000 1.0000000
#> Time 0.5432686 1.00000 1.000000e+00 0.0000000 1.0000000
#> BF NA 1.00000 1.267935e-04 0.7609295 0.5431578
#> PostProbs NA 0.01910 1.560000e-02 0.0145000 0.0133000
#> R2 NA 0.84200 8.695000e-01 0.8265000 0.8506000
#> dim NA 9.00000 1.600000e+01 8.0000000 10.0000000
#> logmarg NA -22.15855 -3.113150e+01 -22.4317627 -22.7689035
#> model 5
#> Intercept 1.0000000
#> M 1.0000000
#> So 0.0000000
#> Ed 1.0000000
#> Po1 1.0000000
#> Po2 0.0000000
#> LF 0.0000000
#> M.F 0.0000000
#> Pop 1.0000000
#> NW 1.0000000
#> U1 0.0000000
#> U2 1.0000000
#> GDP 0.0000000
#> Ineq 1.0000000
#> Prob 1.0000000
#> Time 0.0000000
#> BF 0.5203179
#> PostProbs 0.0099000
#> R2 0.8375000
#> dim 9.0000000
#> logmarg -22.8118635