result: Posterior inference results from the object of S5

In BayesS5: Bayesian Variable Selection Using Simplified Shotgun Stochastic Search with Screening (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

Author(s)

Shin Minsuk and Ruoxuan Tian

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

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)) 

BayesS5 documentation built on March 26, 2020, 7:14 p.m.