oxboys.saemix: Heights of Boys in Oxford

In iame-researchCenter/saemix: Stochastic Approximation Expectation Maximization (SAEM) Algorithm

Heights of Boys in Oxford

Description

The oxboys.saemix data collects the height and age of students in Oxford measured over time. The data frame has 234 rows and 4 columns.

Usage

oxboys.saemix

Format

This data frame contains the following columns:

Subject

an ordered factor giving a unique identifier for each boy in the experiment

age

a numeric vector giving the standardized age (dimensionless)

height

a numeric vector giving the height of the boy (cm)

Occasion

an ordered factor - the result of converting 'age' from a continuous variable to a count so these slightly unbalanced data can be analyzed as balanced

Details

These data are described in Goldstein (1987) as data on the height of a selection of boys from Oxford, England versus a standardized age. The dataset can be found in the package nlme. We use an linear model for this data: y_ij = Base_i + slope_i x_ij +epsilon_ij

References

JC Pinheiro, DM Bates (2000) Mixed-effects Models in S and S-PLUS, Springer, New York (Appendix A.19)

Examples

data(oxboys.saemix)
saemix.data<-saemixData(name.data=oxboys.saemix,header=TRUE,
      name.group=c("Subject"),name.predictors=c("age"),name.response=c("height"),
      units=list(x="yr",y="cm"))

# plot the data
plot(saemix.data)

growth.linear<-function(psi,id,xidep) {
  x<-xidep[,1]
  base<-psi[id,1]
  slope<-psi[id,2]
  f<-base+slope*x
  return(f)
}
saemix.model<-saemixModel(model=growth.linear,description="Linear model",
      psi0=matrix(c(140,1),ncol=2,byrow=TRUE,dimnames=list(NULL,c("base","slope"))),
      transform.par=c(1,0),covariance.model=matrix(c(1,1,1,1),ncol=2,byrow=TRUE), 
      error.model="constant")

saemix.options<-list(algorithms=c(1,1,1),nb.chains=1,seed=201004,
      save=FALSE,save.graphs=FALSE,displayProgress=FALSE)

saemix.fit<-saemix(saemix.model,saemix.data,saemix.options)

iame-researchCenter/saemix documentation built on March 26, 2022, 10:33 p.m.