penalized (version 0.9-6)

Penalized generalized linear models: Penalized regression

Description

Fitting generalized linear models with L1 (lasso) and/or L2 (ridge) penalties, or a combination of the two.

Usage

penalized (response, penalized, unpenalized, lambda1 = 0, lambda2 = 0, 
    data, model = c("cox", "logistic", "linear"), startbeta, startgamma, 
    steps = 1, epsilon = 1e-10, maxiter, standardize = FALSE, 
    trace = TRUE)

Arguments

response

The response variable (vector). This should be a numeric vector for linear regression, a Surv object for Cox regression and a vector of 0/1 values for logistic regression.

penalized

The penalized covariates. These may be specified either as a matrix or as a (one-sided) formula object. See also under data.

unpenalized

Additional unpenalized covariates. Specified as under penalized. Note that an unpenalized intercept is included in the model by default (except in the cox model). This can be suppressed by specifying unpenalized = ~0.

lambda1, lambda2

The tuning parameters for L1 and L2 penalization. May be vectors if different cavariates are to be penalized differently.

data

A data.frame used to evaluate response, and the terms of penalized or unpenalized when these have been specified as a formula object.

model

The model to be used. If missing, the model will be guessed from the response input.

startbeta

Starting values for the regression coefficients of the penalized covariates.

startgamma

Starting values for the regression coefficients of the unpenalized covariates.

steps

If greater than 1, the algorithm will fit the model for a range of steps lambda1-values, starting from the maximal value down to the value of lambda1 specified. This is useful for making plots as in

epsilon

The convergence criterion. As in glm. Convergence is judged separately on the likelihood and on the penalty.

maxiter

The maximum number of iterations allowed. Set by default at 25 when lambda1 = 0, infinite otherwise.

standardize

If TRUE, standardizes all penalized covariates to unit central L2-norm before applying penalization.

trace

If TRUE, prints progress information. Note that setting trace=TRUE may slow down the algorithm up to 30 percent (but it often feels quicker)

Value

• penalized returns a link{penfit} object when steps = 1 or a list of such objects if steps > 1.

Details

The penalized function fits regression models for a given combination of L1 and L2 penalty parameters.

See Also

penfit for the penfit object returned, plotpath for plotting the solution path, and cvl for cross-validation and optimizing the tunung parameters.

Examples

data(nki70)

# A single lasso fit predicting survival
pen <- penalized(Surv(time, event), penalized = nki70[,8:77], 
    unpenalized = ~ER+Age+Diam+N+Grade, data = nki70, lambda1 = 10)
show(pen)
coefficients(pen)
coefficients(pen, "penalized")
basehaz(pen)

# A single lasso fit using using the clinical risk factors 
pen <- penalized(Surv(time, event), penalized = ~ER+Age+Diam+N+Grade, 
    data = nki70, lambda1=10, standardize=TRUE)

# using steps
pen <- penalized(Surv(time, event), penalized = nki70[,8:77], 
    data = nki70, lambda1 = 1, steps = 20)
plotpath(pen)