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#'@title Variable Coefficients for Theta
#'@aliases summary_mean
#'@name summary_mean
#'@description A function that uses the beta values of the posterior distribution of the model and calculates the estimates for each theta covariate.
#'@usage summary_mean(x,prob=0.95)
#'@param x an object of the class \emph{bayesbr}, containing the list returned from the \code{\link{bayesbr}} function.
#'@param prob a probability containing the credibility index for the HPD interval for the coefficients of the covariates.
#'@return A list containing the estimates for the covariables of theta, this list contains the following items:
#'\describe{
#'\item{table}{a table with the means, medians, standard deviations and the Highest Posterior Density (HPD) Interval,}
#'\item{coeff}{a vector containing the estimated coefficients for the variables.}}
#'@seealso \code{\link{summary_precision}},\code{\link{values}},\code{\link{summary.bayesbr}}
summary_mean = function(x,prob=0.95){
beta = x$info$samples$beta
names_x = x$info$names$names_x
warmup = x$info$warmup
iter = x$info$iter
table = NULL
coeff = numeric()
if(!is.null(beta)){
table = c()
tam = length(beta)
for (i in 1:tam) {
if(i==1 && tam==1){
betas = beta$'betas'
}
else{
aux = paste0('betas[',i,']')
betas = beta[[aux]]
}
mean_t = round(mean(betas),5)
coeff = c(coeff,mean_t)
median_t = round(median(betas),5)
sd_t = round(sd(betas),5)
beta_mcmc = as.mcmc( c(betas) )
hpd = HPDinterval(beta_mcmc, prob=prob)
vec = c(mean_t,median_t,sd_t,round(hpd[1:2],5))
table = rbind(table,vec)
}
colnames(table) = c("Mean","Median", "Std. Dev.","HPD_inf","HPD_sup")
rownames(table) = names_x
names(coeff) = names_x
}
list = list(table = table,betas = coeff)
return(list)
}
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