Prediction from penalized models {penalized} | R Documentation |
Predicting a response for new subjects based on a fitted penalized regression model.
## S4 method for signature 'penfit': predict(object, penalized, unpenalized, data)
object |
The fitted model (a penfit object). |
penalized |
The penalized covariates for the new subjects. These may be specified either as a matrix or as a (one-sided) formula object. In the latter case, the formula is interpreted in terms of the data argument. |
unpenalized |
The unpenalized covariates for the new subjects. These may be specified either as a matrix or as a (one-sided) formula object. In the latter case, the formula is interpreted in terms of the data argument. |
data |
A data.frame used to evaluate the terms of penalized or unpenalized when these have been specified as a formula object. |
The terms or columns of the penalized and unpenalized arguments must be exactly the same as in the original call that produced the penfit
object. Any factors in data
must have the same levels.
The predictions, either as a vector
(logistic and Poisson models), a matrix
(linear model), or a breslow
object (Cox model).
data(nki70) pen <- penalized(Surv(time, event), penalized = nki70[1:50,8:77], unpenalized = ~ER+Age+Diam+N+Grade, data = nki70[1:50,], lambda1 = 10) predict(pen, nki70[51:52,8:77], ~ER+Age+Diam+N+Grade, data = nki70[51:52,])