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\name{vaso}
\alias{vaso}
\docType{data}
\title{Vaso Constriction Skin Data Set}
\description{
Finney's data on vaso constriction in the skin of the digits.
}
\usage{data(vaso, package="robustbase")}
\format{
A data frame with 39 observations on the following 3 variables.
\describe{
\item{\code{Volume}}{Inhaled volume of air}
\item{\code{Rate}}{Rate of inhalation}
\item{\code{Y}}{vector of 0 or 1 values.}
}
}
\details{The data taken from Finney (1947) were obtained in a carefully
controlled study in human physiology where a reflex
\dQuote{vaso constriction} may occur in the skin of the digits after taking a
single deep breath. The response y is the occurence (y = 1) or
non-occurence (y = 0) of vaso constriction in the skin of the digits
of a subject after he or she inhaled a certain volume of air at a certain
rate. The responses of three subjects are available. The first
contributed 9 responses, the second contributed 8 responses, and the
third contributed 22 responses.
Although the data represent repeated measurements, an analysis that
assumes independent observations may be applied, as claimed by Pregibon
(1981).
}
\source{
Finney, D.J. (1947)
The estimation from individual records of the relationship between
dose and quantal response.
\emph{Biometrika} \bold{34}, 320--334
}
\references{
Atkinson, A.C. and Riani, M. (2000)
\emph{Robust Diagnostic Regression Analysis},
First Edition. New York: Springer, Table A.23.
Fahrmeir, L. and Tutz, G. (2001)
\emph{Multivariate Statistical Modelling Based on Generalized Linear Models},
Springer, Table 4.2.
Kuensch, H.R., Stefanski, A. and Carrol, R.J. (1989)
Conditionally unbiased bounded influence estimation in general
regression models, with applications to generalized linear models,
\emph{JASA} \bold{84}, 460--466.
Pregibon, D. (1981)
Logistic regression diagnostics,
\emph{Annals of Statistics} \bold{9}, 705--724.
}
\examples{
data(vaso)
str(vaso)
pairs(vaso)
glmV <- glm(Y ~ log(Volume) + log(Rate), family=binomial, data=vaso)
summary(glmV)
## --> example(glmrob) showing classical & robust GLM
}
\keyword{datasets}
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