diet: Simulated diet data to predict weight loss

In xtune: Regularized Regression with Feature-Specific Penalties Integrating External Information

Simulated diet data to predict weight loss

Description

The simulated diet data contains 100 observations, 14 predictors, and an binary outcome, weightloss. The external information Z is the nutrition fact about the dietary items. Z contains three external information variables: Calories, protein and carbohydrates.

Usage

data(diet)

Format

The diet object is a list containing three elements:

• DietItems: Matrix of predictors.

• weightloss: 0: no weight loss; 1: weight loss

• nutritionFact: External information of the predictors

References

S. Witte, John & Greenland, Sander & W. Haile, Robert & L. Bird, Cristy. (1994). Hierarchical Regression Analysis Applied to a Study of Multiple Dietary Exposures and Breast Cancer. Epidemiology (Cambridge, Mass.). 5. 612-21. 10.1097/00001648-199411000-00009.

See Also

example

Examples

data(diet)
X <- diet$DietItems
Y <- diet$weightloss
Z <- diet$nutritionFact
fit <- xtune(X,Y,Z, family = "binary")
fit$penalty.vector

xtune documentation built on July 9, 2023, 5:22 p.m.