search for: sigmas2

...dfor (i in 1:n){time[i] ~ dnorm( mu[i], tau ) # stochastic componenent# link and linear predictormu[i] <- beta0 + beta1 * cases[i] + beta2 * distance[i]}# prior distributionstau ~ dgamma( 0.01, 0.01 )beta0 ~ dnorm( 0.0, 1.0E-4)beta1 ~ dnorm( 0.0, 1.0E-4)beta2 ~ dnorm( 0.0, 1.0E-4)# definition of sigmas2<-1/taus <-sqrt(s2)# calculation of the sample variancefor (i in 1:n){ c.time[i]<-time[i]-mean(time[]) }sy2 <- inprod( c.time[], c.time[] )/(n-1)# calculation of Bayesian version R squaredR2B <- 1 - s2/sy2# Expected y for a typical delivery timetypical.y <- beta0 + beta1 * mean(cas...