Err.sgl

object

a design matrix (the $X$ matrix).

data

a matrix of the true responses (the $Y$ matrix).

response

Compute and return the root-mean-square error for each model.
This method is only intended for testing.
The root-mean-square error (RMSE) is
$$\frac{1}{K}\sum_{i = 1}^K \sqrt{\frac{1}{N}\sum_{j=1}^N Y_{ji}-(X\hat \beta)_{ji}}$$
RMSE is the default error.

Fast generic solver for sparse group lasso optimization
problems. The loss (objective) function must be defined in a
C++ module. The optimization problem is solved using a
coordinate gradient descent algorithm. Convergence of the
algorithm is established (see reference) and the algorithm is
applicable to a broad class of loss functions. Use of parallel
computing for cross validation and subsampling is supported
through the 'foreach' and 'doParallel' packages. Development
version is on GitHub, please report package issues on GitHub.

Err.sgl: Error Rates

Description

Usage

Arguments

Value