Model predictions based on a fitted "ncvsurv" object.
Similar to other predict methods, this function returns predictions from
a fitted "ncvsurv" object.
## S3 method for class 'ncvsurv'
predict(object, X, type=c("link", "response", "survival",
"median", "coefficients", "vars", "nvars"), lambda,
which=1:length(object$lambda), ...)object |
Fitted |
X |
Matrix of values at which predictions are to be made. Not
used for |
lambda |
Values of the regularization parameter |
which |
Indices of the penalty parameter |
type |
Type of prediction: |
... |
Not used. |
Estimation of baseline survival function conditional on the estimated
values of beta is carried out according to the method described in
Chapter 4.3 of Kalbfleish and Prentice. In particular, it agrees exactly
the results returned by survfit.coxph(..., type='kalbfleisch-prentice')
in the survival package.
The object returned depends on type.
Patrick Breheny <patrick-breheny@uiowa.edu>
Breheny P and Huang J. (2011) Coordinate descentalgorithms for nonconvex penalized regression, with applications to biological feature selection. Annals of Applied Statistics, 5: 232-253. doi: 10.1214/10-AOAS388
Kalbfleish JD and Prentice RL (2002). The Statistical Analysis of Failure Time Data, 2nd edition. Wiley.
data(Lung) X <- Lung$X y <- Lung$y fit <- ncvsurv(X,y) coef(fit, lambda=0.05) head(predict(fit, X, type="link", lambda=0.05)) head(predict(fit, X, type="response", lambda=0.05)) # Survival function S <- predict(fit, X[1,], type="survival", lambda=0.05) S(100) S <- predict(fit, X, type="survival", lambda=0.05) plot(S, xlim=c(0,200)) # Medians predict(fit, X[1,], type="median", lambda=0.05) M <- predict(fit, X, type="median") M[1:10, 1:10] # Nonzero coefficients predict(fit, type="vars", lambda=c(0.1, 0.01)) predict(fit, type="nvars", lambda=c(0.1, 0.01))
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