epilepsy.saemix: Epilepsy count data

In saemix: Stochastic Approximation Expectation Maximization (SAEM) Algorithm

Epilepsy count data

Description

The epilepsy data from Thall and Vail (1990), available from the MASS package, records two-week seizure counts for 59 epileptics. The number of seizures was recorded for a baseline period of 8 weeks, and then patients were randomly assigned to a treatment group or a control group. Counts were then recorded for four successive two-week periods. The subject's age is the only covariate. See the documentation for epil in the MASS package for details on the dataset.

Source

MASS package in R

References

P Thall, S Vail (1990). Some covariance models for longitudinal count data with overdispersion. Biometrics 46(3):657-71.

Examples

# You need to have MASS installed to successfully run this example
if (requireNamespace("MASS")) {
  
  epilepsy<-MASS::epil
  saemix.data<-saemixData(name.data=epilepsy, name.group=c("subject"),
     name.predictors=c("period","y"),name.response=c("y"),
     name.covariates=c("trt","base", "age"), units=list(x="2-week",y="",covariates=c("","","yr")))
  ## Poisson model with one parameter
  countPoi<-function(psi,id,xidep) { 
    y<-xidep[,2]
    lambda<-psi[id,1]
    logp <- -lambda + y*log(lambda) - log(factorial(y))
    return(logp)
    }
 saemix.model<-saemixModel(model=countPoi,description="Count model Poisson",modeltype="likelihood", 
   psi0=matrix(c(0.5),ncol=1,byrow=TRUE,dimnames=list(NULL, c("lambda"))), transform.par=c(1))
 
  saemix.options<-list(seed=632545,save=FALSE,save.graphs=FALSE, displayProgress=FALSE)
  poisson.fit<-saemix(saemix.model,saemix.data,saemix.options)
  
}
  

saemix documentation built on May 29, 2024, 10:43 a.m.