| result {BayesS5} | R Documentation |
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 |
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))