# Prediction of new observations Computing a prediction for new observations ```r Prediction(object, data) ``` ## Arguments - `object`: an object inheriting from class ZIHR - `data`: dataset of observed variables with the same format as the data in the object ## Returns Estimation, standard errors and 95% credible intervals for predictions ## Details It provides a summary of the output of the ZIHR function, including parameter estimations. ## Examples ```r # Example 1 data(dataD) index <- 1:(dim(dataD)[1]) IND_new <- sample(index, .5 * length(index)) datat <- dataD[IND_new, ] datav <- dataD[-IND_new, ] modelY <- y~x1 + x2 modelZ <- z~x1 D1 <- ZIHR(modelY, modelZ, data = datat, n.chains = 2, n.iter = 1000, n.burnin = 500, n.thin = 1, family = "Poisson" ) SummaryZIHR(D1) Prediction(D1, data = datav) D2 <- ZIHR(modelY, modelZ, data = datat, n.chains = 2, n.iter = 1000, n.burnin = 500, n.thin = 1, family = "Bell" ) SummaryZIHR(D2) # Example 2 data(dataC) modelY <- y~x1 + x2 modelZ <- z~x1 C <- ZIHR(modelY, modelZ, data = dataC, n.chains = 2, n.iter = 1000, n.burnin = 500, n.thin = 1, family = "Gaussian" ) SummaryZIHR(C) Prediction(C, data = datav) # Example 3 data(dataP) modelY <- y~x1 + x2 modelZ <- z~x1 P1 <- ZIHR(modelY, modelZ, data = dataP, n.chains = 2, n.iter = 1000, n.burnin = 500, n.thin = 1, family = "Exponential" ) SummaryZIHR(P1) P2 <- ZIHR(modelY, modelZ, data = dataP, n.chains = 2, n.iter = 1000, n.burnin = 500, n.thin = 1, family = "Gamma" ) SummaryZIHR(P2) P3 <- ZIHR(modelY, modelZ, data = dataP, n.chains = 2, n.iter = 1000, n.burnin = 500, n.thin = 1, family = "Weibull" ) SummaryZIHR(P3) # Example B data(dataB) modelY <- y~x1 + x2 modelZ <- z~x1 P <- ZIHR(modelY, modelZ, data = dataB, n.chains = 2, n.iter = 1000, n.burnin = 500, n.thin = 1, family = "Beta" ) SummaryZIHR(P) # Example C data(dataI) modelY <- y~x1 + x2 modelZ <- z~x1 P4 <- ZIHR(modelY, modelZ, data = dataI, n.chains = 2, n.iter = 1000, n.burnin = 500, n.thin = 1, family = "inverse.gaussian" ) SummaryZIHR(P4) ``` ## See Also `ZIHR` ## Author(s) Taban Baghfalaki t.baghfalaki@gmail.com , Mojtaba Ganjali m-ganjali@sbu.ac.ir

Prediction function