lasso: lasso

In lol: Lots Of Lasso

Description

Lasso penalized linear regression with different optimizers

Usage

lasso(y, ...)

Arguments

y	A list object of one of the four classes: 'cv', 'stability', 'multiSplit', and 'simultaneous'. If x is NULL then y should a list of two components y and x, y is a vector of expression and x is a matrix containing copy number variables
...	other parameters

Details

The function contains various optimization methods for Lasso inference, such as cross-validation, randomised lasso, simultaneous lasso etc. It is specifically designed for multicollinear predictor variables.

Value

Varied depending on the optimizer used. Generally it contains

beta	coefficients
residuals	residuals of regression model
fit	the corresponding fit of regression

Author(s)

Yinyin Yuan

References

Goeman, J. J. (2009), L1 penalized estimation in the cox proportional hazards model, Biometrical Journal. N. Meinshausen and P. Buehlmann (2010), Stability Selection (with discussion), Journal of the Royal Statistical Society, Series B, 72, 417-473. Nicolai Meinshausen, Lukas Meier and Peter Buehlmann (2009), P-values for high-dimensional regression. Journal of the American Statistical Association, 104, 1671-1681.

See Also

matrixLasso

Examples

data(chin07)
data <- list(y=chin07$ge[1,], x=t(chin07$cn))
class(data) <- 'cv'
res <- lasso(data)

Example output

Loading required package: penalized
Loading required package: survival
Welcome to penalized. For extended examples, see vignette("penalized").
Loading required package: Matrix

lol documentation built on Oct. 31, 2019, 2:21 a.m.