predict.ncvsurv | R Documentation |
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$lambda),
...
)
object |
Fitted |
X |
Matrix of values at which predictions are to be made. Not used for
|
type |
Type of prediction: |
lambda |
Values of the regularization parameter |
which |
Indices of the penalty parameter |
... |
Not used. |
Estimation of baseline survival function conditional on the estimated values
of beta
is carried out according to the method described in Chapter
4.3 of Kalbfleish and Prentice. In particular, it agrees exactly the
results returned by survfit.coxph(..., type='kalbfleisch-prentice')
in the survival
package.
The object returned depends on type.
Patrick Breheny patrick-breheny@uiowa.edu
Breheny P and Huang J. (2011) Coordinate descentalgorithms for nonconvex penalized regression, with applications to biological feature selection. Annals of Applied Statistics, 5: 232-253. c("\Sexpr[results=rd]tools:::Rd_expr_doi(\"#1\")", "10.1214/10-AOAS388")\Sexpr{tools:::Rd_expr_doi("10.1214/10-AOAS388")}
Kalbfleish JD and Prentice RL (2002). The Statistical Analysis of Failure Time Data, 2nd edition. Wiley.
ncvsurv
data(Lung)
X <- Lung$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))
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