Data from 442 diabetes patients used in Section 7.3. The response is a quantitative measure of disease progression one year after baseline. There are ten baseline predictors: age, sex, body-mass index, average blood pressure, and six blood serum measurements.
Format
A data frame with 442 rows and 12 variables:
- X
Row index
- age
Age of patient
- sex
Sex of patient
- bmi
Body mass index
- map
Average blood pressure (mean arterial pressure)
- tc
Total cholesterol (serum measurement)
- ldl
Low-density lipoproteins (serum measurement)
- hdl
High-density lipoproteins (serum measurement)
- tch
Total cholesterol / HDL (serum measurement)
- ltg
Log of triglycerides (serum measurement)
- glu
Blood sugar level (serum measurement)
- prog
Response: quantitative measure of disease progression
Details
First used in the LARS paper (Efron et al., 2004).
Note: In Table 7.2, the centered predictor variables were standardized to unit L2 norm. In Table 20.1 they were standardized to unit variance.
References
Efron, B., Hastie, T., Johnstone, I. and Tibshirani, R. (2004). Least Angle Regression. Annals of Statistics, 32(2), 407-499.
Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference. Cambridge University Press, Section 7.3.
Examples
data(diabetes)
str(diabetes)
#> 'data.frame': 442 obs. of 12 variables:
#> $ X : int 1 2 3 4 5 6 7 8 9 10 ...
#> $ age : int 59 48 72 24 50 23 36 66 60 29 ...
#> $ sex : int 1 0 1 0 0 0 1 1 1 0 ...
#> $ bmi : num 32.1 21.6 30.5 25.3 23 22.6 22 26.2 32.1 30 ...
#> $ map : num 101 87 93 84 101 89 90 114 83 85 ...
#> $ tc : int 157 183 156 198 192 139 160 255 179 180 ...
#> $ ldl : num 93.2 103.2 93.6 131.4 125.4 ...
#> $ hdl : num 38 70 41 40 52 61 50 56 42 43 ...
#> $ tch : num 4 3 4 5 4 2 3 4.55 4 4 ...
#> $ ltg : num 2.11 1.69 2.03 2.12 1.86 ...
#> $ glu : int 87 69 85 89 80 68 82 92 94 88 ...
#> $ prog: int 151 75 141 206 135 97 138 63 110 310 ...