Description Slots Methods Author(s) See Also

Stores the result of a call to `penalized`

.

`penalized`

:Object of class "vector". Regression coefficients for the penalized covariates.

`unpenalized`

:Object of class "vector". Regression coefficients for the unpenalized covariates.

`residuals`

:Object of class "vector". Unstandardized residuals for each subject in the fitted model. Martingale residuals are given for the cox model.

`fitted`

:Object of class "vector". Fitted values (means) for each subject in the fitted model. In the cox model, this slot holds the relative risks.

`lin.pred`

:Object of class "vector". Linear predictors for each subject in the fitted model.

`loglik`

:Object of class "numeric". Log likelihood of the fitted model. For the Cox model, reports the full likelihood rather than the partial likelihood.

`penalty`

:Object of class "vector". L1 and L2 penalties of the fitted model.

`iterations`

:Object of class "numeric". Number of iterations used in the fitting process.

`converged`

:Object of class "logical". Whether the fitting process was judged to be converged.

`model`

:Object of class "character". The name of the generalized linear model used.

`lambda1`

:Object of class "vector". The lambda1 parameter(s) used.

`lambda2`

:Object of class "vector". The lambda2 parameter(s) used.

`nuisance`

:Object of class "list". The maximum likelihood estimates of any nuisance parameters in the model.

`weights`

:Object of class "vector". The weights of the covariates used for standardization.

`formula`

:Object of class "list". A named list containing the unpenalized and penalized formula objects, if present.

- basehaz
(penfit): Returns the baseline hazard (a

`data.frame`

) if a cox model was fitted,`NULL`

otherwise. An additional argument`center`

(default (`TRUE`

) can be used to give the survival curve at the covariate mean (`center = TRUE`

) rather than at zero.- basesurv
(penfit): Returns the baseline survival curve (a

`breslow`

object) if a cox model was fitted,`NULL`

otherwise. An additional argument`center`

(default (`TRUE`

) can be used to give the survival curve at the covariate mean (`center = TRUE`

) rather than at zero.- coef
(penfit): Returns the regression coefficients. Accepts a second argument "which", that takes values "nonzero" (the default), "all", "penalized" or "unpenalized" for extracting only the non-zero, the penalized or the unpenalized regression coefficients. A third argument "standardize" (default FALSE) can be used to let the method return the regression coefficients for the standardized covariates.

- coefficients
(penfit): synonym for

`coef`

above.- fitted
(penfit): Returns the fitted values for each subject (i.e. the predicted means). In the Cox model, this method returns the relative risks for each individual.

- fitted.values
(penfit): synonym for

`fitted`

above.- linear.predictors
(penfit): Returns the linear predictors for each subject.

- loglik
(penfit): Returns the log likelihood of the fitted model.

- penalty
(penfit): Returns the L1 and L2 penalties of the fitted model.

- residuals
(penfit): Returns the residuals.

- show
(penfit): Summarizes the fitted model.

- weights
(penfit): Returns the weights used for standardization.

- predict
(penfit): Calculates predictions for new subjects. See

`predict`

.

Jelle Goeman: [email protected]

luederm/penalizedcpp documentation built on May 21, 2019, 8:55 a.m.

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