BayesRobustProbitSummary {BayesRGMM}

To summarizes model estimation outcomes

Description

It provides basic posterior summary statistics such as the posterior point and confidence interval estimates of parameters and the values of information criterion statistics for model comparison.

Usage

BayesRobustProbitSummary(object, digits = max(1L, getOption("digits") - 4L))

Arguments

object

output from the function BayesRobustProbit.

digits

rounds the values in its first argument to the specified number of significant digits.

Value

a list of posterior summary statistics and corresponding model information

Examples

## Not run: 
library(BayesRGMM)
rm(list=ls(all=TRUE))
Fixed.Effs = c(-0.2, -0.3, 0.8, -0.4) #c(-0.2,-0.8, 1.0, -1.2)
P = length(Fixed.Effs) 
q = 1 #number of random effects
T = 5 #time points
N = 100 #number of subjects
num.of.iter = 100 #number of iterations
HSD.para = c(-0.5,  -0.3) #the parameters in HSD model
a = length(HSD.para)
w = array(runif(T*T*a), c(T, T, a)) #design matrix in HSD model

for(time.diff in 1:a)
	w[, , time.diff] = 1*(as.matrix(dist(1:T, 1:T, method="manhattan")) 
 == time.diff)

#Generate a data with HSD model
HSD.sim.data = SimulatedDataGenerator(Num.of.Obs = N, Num.of.TimePoints = T, 
Fixed.Effs = Fixed.Effs, Random.Effs = list(Sigma = 0.5*diag(1), df=3), 
Cor.in.DesignMat = 0., Missing = list(Missing.Mechanism = 2, 
 RegCoefs = c(-1.5, 1.2)), Cor.Str = "HSD", 
HSD.DesignMat.para = list(HSD.para = HSD.para, DesignMat = w))

hyper.params = list(
        sigma2.beta = 1,
        sigma2.delta = 1,
        v.gamma = 5,
        InvWishart.df = 5,
        InvWishart.Lambda = diag(q) )

HSD.output = BayesRobustProbit(
 fixed = as.formula(paste("y~-1+", paste0("x", 1:P, collapse="+"))), 
	data=HSD.sim.data$sim.data, random = ~ 1, Robustness=TRUE, 
 HS.model = ~IndTime1+IndTime2, subset = NULL, na.action='na.exclude', 
 hyper.params = hyper.params, num.of.iter = num.of.iter, Interactive =0)

BayesRobustProbitSummary(HSD.output)

## End(Not run)