ipred (version 0.6-14)

GlaucomaMVF: Glaucoma Database

Description

The GlaucomaMVF data has 170 observations in two classes. 66 predictors are derived from a confocal laser scanning image of the optic nerve head, from a visual field test, a fundus photography and a measurement of the intra occular pressure.

Usage

data(GlaucomaMVF)

source

Andrea Peters, Berthold Lausen, Georg Michelson and Olaf Gefeller (2002), Diagnosis of glaucoma by indirect classifiers, Methods of Information in Medicine, to appear.

Details

Confocal laser images of the eye background are taken with the Heidelberg Retina Tomograph and variables 1-62 are derived. Most of these variables describe either the area or volume in certain parts of the papilla and are measured in four sectors (temporal, superior, nasal and inferior) as well as for the whole papilla (global). The global measurement is, roughly, the sum of the measurements taken in the four sector.

The perimeter `Octopus' measures the visual field variables clv and cs, stereo optic disks photographs were taken with a telecentric fundus camera and lora is derived.

Observations of both groups are matched by age and sex, to prevent for possible confounding.

Examples

data(GlaucomaMVF)

response <- function (data) {
  attach(data) 
  res <- ifelse((!is.na(clv) & !is.na(lora) & clv >= 5.1 & lora >= 
        49.23372) | (!is.na(clv) & !is.na(lora) & !is.na(cs) & 
        clv < 5.1 & lora >= 58.55409 & cs < 1.405) | (is.na(clv) & 
        !is.na(lora) & !is.na(cs) & lora >= 58.55409 & cs < 1.405) | 
        (!is.na(clv) & is.na(lora) & cs < 1.405), 0, 1)
  detach(data)
  factor (res, labels = c("glaucoma", "normal"))
}

mypredict.inclass <- function(object, newdata){
  res <- predict.inclass(object = object, cFUN = response, newdata = newdata)
  return(res)
}

errorest(clv+lora+cs~., data = GlaucomaMVF, model=inclass, 
         predict=mypredict.inclass, iclass="Class", estimator="cv", 
         pFUN = rpart)