Penalized generalized linear models {penalized} | R Documentation |
Fitting generalized linear models with L1 (lasso) and/or L2 (ridge) penalties, or a combination of the two.
penalized (response, penalized, unpenalized, lambda1 = 0, lambda2 = 0, positive = FALSE, data, model = c("cox", "logistic", "linear", "poisson"), startbeta, startgamma, steps = 1, epsilon = 1e-10, maxiter, standardize = FALSE, trace = TRUE)
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. |
positive |
If TRUE , constrains the estimated regression coefficients of all penalized covariates to be positive. |
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 plotpath . With steps = "Park" it is possible to choose the steps in such a way that they are at the approximate value at which the active set changes, following Park and Haste (2007). |
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 only an L2 penalty is present, 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) |
The penalized
function fits regression models for a given combination of L1 and L2 penalty parameters.
penalized
returns a penfit
object when steps = 1
or a list of such objects if steps > 1
.
The response
argument of the function also accepts formula input as in lm
and related functions. In that case, the right hand side of the response
formula is used as the penalized
argument or, if that is already given, as the unpenalized
argument. For example, the input penalized(y~x)
is equivalent to penalized(y, ~x)
and penalized(y~x, ~z)
to penalized(y, ~z, ~x)
.
In case of tied survival times, the function uses Breslow's version of the partial likelihood.
Jelle Goeman: j.j.goeman@lumc.nl
penfit
for the penfit
object returned, plotpath
for plotting the solution path, and cvl
for cross-validation and
optimizing the tuning parameters.
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 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)