| GlaucomaMVF {ipred} | R Documentation |
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.
data(GlaucomaMVF)
This data frame contains the following predictors describing the morphology of the optic nerve head, the visual field, the intra occular pressure and a membership variable:
glaucoma and normal.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.
GLaucomMVF overlaps in some parts with GlaucomaM.
Andrea Peters, Berthold Lausen, Georg Michelson and Olaf Gefeller (2002), Diagnosis of glaucoma by indirect classifiers, Methods of Information in Medicine, to appear.
## Not run:
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"))
}
errorest(Class~clv+lora+cs~., data = GlaucomaMVF, model=inclass,
estimator="cv", pFUN = list(list(model = rpart)), cFUN = response)
## End(Not run)