| 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,
data, model = c("cox", "logistic", "linear"), 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. 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 plotpath. |
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) |
The penalized function fits regression models for a given
combination of L1 and L2 penalty parameters.
penalized returns a link{penfit} object when steps = 1
or a list of such objects if steps > 1.
The functions also accept formula input as in lm. The input
penalized(y~x) is equivalent to penalized(y, ~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 tunung 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 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)