predict.ncvsurv  R Documentation 
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", "hazard", "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

type 
Type of prediction:

lambda 
Values of the regularization parameter 
which 
Indices of the penalty parameter 
... 
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='kalbfleischprentice')
in the survival
package.
The object returned depends on type.
Patrick Breheny patrickbreheny@uiowa.edu
Breheny P and Huang J. (2011) Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection. Annals of Applied Statistics, 5: 232253. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/10AOAS388")}
Kalbfleish JD and Prentice RL (2002). The Statistical Analysis of Failure Time Data, 2nd edition. Wiley.
ncvsurv()
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))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.