diabetes {rrcov}

Reaven and Miller diabetes data

Description

The data set contains five measurements made on 145 non-obese adult patients classified into three groups.

The three primary variables are glucose intolerance (area under the straight line connecting glucose levels), insulin response to oral glucose (area under the straight line connecting insulin levels) and insulin resistance (measured by the steady state plasma glucose (SSPG) determined after chemical suppression of endogenous insulin secretion). Two additional variables, the relative weight and fasting plasma glucose, are also included.

Reaven and Miller, following Friedman and Rubin (1967), applied cluster analysis to the three primary variables and identified three clusters: "normal", "chemical diabetic", and "overt diabetic" subjects. The column group contains the classifications of the subjects into these three groups, obtained by current medical criteria.

Usage

data(diabetes)

Format

A data frame with the following variables:

rw

relative weight, expressed as the ratio of actual weight to expected weight, given the person's height.

fpg

fasting plasma glucose level.

glucose

area under plasma glucose curve after a three hour oral glucose tolerance test (OGTT).

insulin

area under plasma insulin curve after a three hour oral glucose tolerance test (OGTT).

sspg

Steady state plasma glucose, a measure of insulin resistance.

group

the type of diabetes: a factor with levels normal, chemical and overt.

Source

Reaven, G. M. and Miller, R. G. (1979). An attempt to define the nature of chemical diabetes using a multidimensional analysis. Diabetologia 16, 17–24. Andrews, D. F. and Herzberg, A. M. (1985). Data: A Collection of Problems from Many Fields for the Student and Research Worker, Springer-Verlag, Ch. 36.

References

Reaven, G. M. and Miller, R. G. (1979). An attempt to define the nature of chemical diabetes using a multidimensional analysis. Diabetologia 16, 17–24.

Friedman, H. P. and Rubin, J. (1967). On some invariant criteria for grouping data. Journal of the American Statistical Association 62, 1159–1178.

Hawkins, D. M. and McLachlan, G. J., 1997. High-breakdown linear discriminant analysis. Journal of the American Statistical Association 92 (437), 136–143.

Examples

data(diabetes)
(cc <- Linda(group~insulin+glucose+sspg, data=diabetes))
(pr <- predict(cc))