npmlreg (version 0.46-5)

predict.glmmNPML: Prediction from objects of class glmmNPML or glmmGQ

Description

The functions alldist and allvc produce objects of type glmmGQ, if Gaussian quadrature (Hinde, 1982, random.distribution="gq" ) was applied for computation, and objects of class glmmNPML, if parameter estimation was carried out by nonparametric maximum likelihood (Aitkin, 1996a, random.distribution="np" ). The functions presented here give predictions from those objects.

Usage

# S3 method for glmmNPML
predict(object, newdata, type = "link", ...)
# S3 method for glmmGQ
predict(object, newdata, type = "link", ...)

Arguments

object

a fitted object of class glmmNPML or glmmGQ.

newdata

a data frame with covariates from which prediction is desired. If omitted, empirical Bayes predictions for the original data will be given.

type

if set to link, the prediction is given on the linear predictor scale. If set to response, prediction is given on the scale of the responses.

…

further arguments which will mostly not have any effect (and are included only to ensure compatibility with the generic predict()- function.)

Value

A vector of predicted values.

Details

The predicted values are obtained by

• Empirical Bayes (Aitkin, 1996b), if newdata has not been specified. That is, the prediction on the linear predictor scale is given by \(\sum{\eta_{ik}w_{ik}} \), whereby \(\eta_{ik}\) are the fitted linear predictors, \(w_{ik}\) are the weights in the final iteration of the EM algorithm (corresponding to the posterior probability for observation \(i\) to come from component \(k\) ), and the sum is taken over the number of components \(k\) for fixed \(i\).

• the marginal model, if object is of class glmmNPML and newdata has been specified. That is, computation is identical as above, but with \(w_{ik}\) replaced by the masses \(\pi_k\) of the fitted model.

• the analytical expression for the marginal mean of the responses, if object is of class glmmGQ and newdata has been specified. See Aitkin et al. (2009), p. 481, for the formula. This method is only supported for the logarithmic link function, as otherwise no analytical expression for the marginal mean of the responses exists.

It is sufficient to call predict instead of predict.glmmNPML or predict.glmmGQ, since the generic predict function provided in R automatically selects the right model class.

References

Aitkin, M. (1996a). A general maximum likelihood analysis of overdispersion in generalized linear models. Statistics and Computing 6, 251-262.

Aitkin, M. (1996b). Empirical Bayes shrinkage using posterior random effect means from nonparametric maximum likelihood estimation in general random effect models. Statistical Modelling: Proceedings of the 11th IWSM 1996, 87-94.

Aitkin, M., Francis, B. and Hinde, J. (2009). Statistical Modelling in R. Oxford Statistical Science Series, Oxford, UK.

Hinde, J. (1982). Compound Poisson regression models. Lecture Notes in Statistics 14, 109-121.

See Also

alldist, allvc, predict

Examples

# NOT RUN {
 # Toxoplasmosis data:
    data(rainfall)
    rainfall$x<-rainfall$Rain/1000
    toxo.0.3x<- alldist(cbind(Cases,Total-Cases)~1, random=~x,
          data=rainfall, k=3, family=binomial(link=logit))
    toxo.1.3x<- alldist(cbind(Cases,Total-Cases)~x, random=~x, 
          data=rainfall, k=3, family=binomial(link=logit))
    predict(toxo.0.3x, type="response", newdata=data.frame(x=2))
    # [1] 0.4608
    predict(toxo.1.3x, type="response", newdata=data.frame(x=2))
    # [1] 0.4608
    # gives the same result, as both models are equivalent and only differ
    # by a  parameter transformation.

# Fabric faults data:
    data(fabric)
    names(fabric) 
    # [1] "leng" "y"    "x"    
    faults.g2<- alldist(y ~ x, family=poisson(link=log), random=~1, 
        data= fabric,k=2, random.distribution="gq") 
    predict(faults.g2, type="response",newdata=fabric[1:6,])
    # [1]  8.715805 10.354556 13.341242  5.856821 11.407828 13.938013
    # is not the same as
    predict(faults.g2, type="response")[1:6]
    # [1]  6.557786  7.046213 17.020242  7.288989 13.992591  9.533823
    # since in the first case prediction is done using the analytical 
    # mean of the marginal distribution, and in the second case  using the
    # individual posterior probabilities in an  empirical Bayes approach. 


# }