R Dataset / Package robustbase / vaso

On this R-data statistics page, you will find information about the vaso data set which pertains to Vaso Constriction Skin Data Set. The vaso data set is found in the robustbase R package. You can load the vaso data set in R by issuing the following command at the console data("vaso"). This will load the data into a variable called vaso. If R says the vaso data set is not found, you can try installing the package by issuing this command install.packages("robustbase") and then attempt to reload the data with the library() command. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the vaso R data set. The size of this file is about 440 bytes.

Vaso Constriction Skin Data Set


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




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


Inhaled volume of air


Rate of inhalation


vector of 0 or 1 values.


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).


Finney, D.J. (1947) The estimation from individual records of the relationship between dose and quantal response. Biometrika 34, 320–334


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.


pairs(vaso)glmV <- glm(Y ~ log(Volume) + log(Rate), family=binomial, data=vaso)
## -->example(glmrob)showing classical & robust GLM

Dataset imported from https://www.r-project.org.

Attachments: csv, json

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