+--------+-------------------+
| vaso   | R Documentation   |
+--------+-------------------+

Vaso Constriction Data Set
--------------------------

Description
~~~~~~~~~~~

Finney's data on vaso constriction in the skin of the digits.

Usage
~~~~~

::

    data(vaso)

Format
~~~~~~

A data frame with 39 observations on the following 3 variables.

``Volume``
    Inhaled volume of air

``Rate``
    Rate of inhalation

``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 “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. *Biometrika* **34**,
320–334

References
~~~~~~~~~~

Atkinson, A.C. and Riani, M. (2000) *Robust Diagnostic Regression
Analysis*, First Edition. New York: Springer, Table A.23.

Fahrmeir, L. and Tutz, G. (2001) *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, *JASA* **84**, 460–466.

Pregibon, D. (1981) Logistic regression diagnostics, *Annals of
Statistics* **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

