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

Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/predict.glmmNPML.R

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

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

Details

The predicted values are obtained by

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.

Value

A vector of predicted values.

Note

The results of the generic fitted() method correspond to predict(object, type="response"). Note that, as we are working with random effects, fitted values are never really ‘fitted’ but rather ‘predicted’.

Author(s)

Jochen Einbeck and John Hinde (2007).

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

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 # Toxoplasmosis data:
    data(rainfall, package="forward")
    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. 

npmlreg documentation built on May 29, 2017, 2:20 p.m.