blrsim: Simulated binary logistic regression data

In AndrewLawrence/dCVnet: doubly cross-validated elastic-net regularised generalised linear models

Simulated binary logistic regression data

Description

blrsim is a single realisation of data under the following scheme: x contains 40 random, correlated gaussian predictors (see mvrnorm); the binary outcome (y) is set to 1 if the sum of x1:x10 (+ error) is positive, and zero otherwise. In effect variables 11-40 are not-relevant to the outcome.

Usage

blrsim

Format

list

x

40 Gaussian predictors. Training data, 50 observations

y

Class membership for x. Training data, 50 observations

x.test

20 Gaussian predictors. Test data, 1000 observations

y.test

Class membership for x. Test data, 1000 observations

bayes.rate

Classification accuracy in the complete sample given the rule used to generate the data (see: raw-data/blrsim.R).

Note: calling this the bayes rate is a bit of a liberty as bayes rate is an error/loss while classification accuracy is the complement of such a error/loss.

Details

The data is split into train and test sets. For reference, the classification accuracy of a model (using the generative rule) in the complete data is provided.

AndrewLawrence/dCVnet documentation built on Sept. 24, 2024, 5:24 a.m.