View source: R/BayesRobustProbitSummary.r
BayesRobustProbitSummary | R Documentation |
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.
BayesRobustProbitSummary(object, digits = max(1L, getOption("digits") - 4L))
object |
output from the function |
digits |
rounds the values in its first argument to the specified number of significant digits. |
a list of posterior summary statistics and corresponding model information
## 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)
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