Prediction {UHM}

Prediction of new observations

Description

Computing a prediction for new observations

Usage

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

Details

It provides a summary of the output of the ZIHR function, including parameter estimations.

Value

Estimation, standard errors and 95% credible intervals for predictions

Author(s)

Taban Baghfalaki [e-mail], Mojtaba Ganjali [e-mail]

See Also

ZIHR

Examples

# 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)