result: Posterior inference results from the object of S5

Description

Using the object of S5, the maximum a posteriori (MAP) model, its posterior probability, and the marginal inclusion probabilities are provided.

Usage

result(fit)

Arguments

fit

an object of the 'S5' function.

Value

hppm

the MAP model

hppm.prob

the posterior probability of the MAP model

marg.prob

the marginal inclusion probabilities

gam

the binary vaiables of searched models by S5

obj

the corresponding log (unnormalized) posterior model probabilities

post

the corresponding (normalized) posterior model probabilities

tuning

the tuning parameter used in the model selection

References

Shin, M., Bhattacharya, A., Johnson V. E. (2018) A Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings, Statistica Sinica.

Hans, C., Dobra, A., and West, M. (2007). Shotgun stochastic search for large p regression. Journal of the American Statistical Association, 102, 507-516.

Nikooienejad,A., Wang, W., and Johnson V.E. (2016). Bayesian variable selection for binary outcomes in high dimensional genomic studies using non-local priors. Bioinformatics, 32(9), 1338-45.

Examples

Run this code

# NOT RUN {
p=5000
n = 200

indx.beta = 1:5
xd0 = rep(0,p);xd0[indx.beta]=1
bt0 = rep(0,p); 
bt0[1:5]=c(1,1.25,1.5,1.75,2)*sample(c(1,-1),5,replace=TRUE)
xd=xd0
bt=bt0
X = matrix(rnorm(n*p),n,p)
y = X%*%bt0 + rnorm(n)*sqrt(1.5)
X = scale(X)
y = y-mean(y)
y = as.vector(y)

### piMoM  
#C0 = 2 # the number of repetitions of S5 algorithms to explore the model space
#tuning = 10 # tuning parameter
#tuning = hyper_par(type="pimom",X,y,thre = p^-0.5)
#print(tuning)
#ind_fun = ind_fun_pimom # choose the prior on the regression coefficients (pimom in this case)
#model = Bernoulli_Uniform # choose the model prior 
#tem =  seq(0.4,1,length.out=20)^2 # the sequence of the temperatures

#fit_pimom = S5(X,y,ind_fun=ind_fun,model = model,tuning=tuning,tem=tem,C0=C0)
#fit_pimom$GAM # the searched models by S5
#fit_pimom$OBJ # the corresponding log (unnormalized) posterior probability

#res_pimom = result(fit_pimom)
#str(res_pimom)
#print(res_pimom$hppm) 
#print(res_pimom$hppm.prob)  
#plot(res_pimom$marg.prob,ylim=c(0,1)) 
# }