predict.coxpath {glmpath} | R Documentation |
Makes predictions at particular points along the fitted coxpath
Description
This function makes predictions at particular points along the fitted
coxpath.
The coefficients, log-partial-likelihood, linear
predictor or the risk can be computed. A coxph
object can be
returned at one particular value of λ.
Usage
## S3 method for class 'coxpath'
predict(object, data, s, type = c("coefficients", "loglik",
"lp", "risk", "coxph"), mode = c("step",
"norm.fraction", "norm", "lambda.fraction", "lambda"),
eps = .Machine$double.eps, ...)
Arguments
object |
a coxpath object
|
data |
a list containing x, time, and status, with
which the predictions are made. If type=lp or
type=risk, then x is required. If type=loglik
or type=coxph, then x, time, and status
are required.
|
s |
the values of mode at which the predictions are made. If
type=coxph, only the first element of s is used. If
s is missing, then the steps at which the active set changed
are used, and thus, mode is automatically switched to
step.
|
type |
If type=coefficients, the coefficients are returned; if
type=loglik, log-partial-likelihoods are returned; if
type=lp, linear predictors (x′β ) are returned; if
type=risk, risks (ex′β ) are returned; and if
type=coxph, a coxph object (as in survival
package) at the first element of s is returned. (i.e. the
components of a coxph object such as coefficients, variance,
and the test statistics are adjusted to the shrinkage corresponding
to s. A coxph object can be further used as an
argument to the functions in survival package.) Default is
coefficients. The coefficients for the initial input
variables are returned (rather than the standardized coefficients).
|
mode |
what mode=s refers to. If mode=step, s is the
number of steps taken; if mode=norm.fraction, s is the
fraction of the L1 norm of the standardized coefficients (with
respect to the largest norm); if mode=norm, s is the
L1 norm of the standardized coefficients; if
mode=lambda.fraction, s is the fraction of
log(λ ); and if mode=lambda, s is
λ . Default is step.
|
eps |
an effective zero
|
... |
other options for the prediction
|
Author(s)
Mee Young Park and Trevor Hastie
References
Mee Young Park and Trevor Hastie (2007) L1 regularization path
algorithm for generalized linear models. J. R. Statist. Soc. B,
69, 659-677.
See Also
cv.coxpath, coxpath, plot.coxpath
Examples
data(lung.data)
attach(lung.data)
fit <- coxpath(lung.data)
pred.a <- predict(fit, x, s = seq(0, 1, length=10),
mode = "norm.fraction")
library(survival)
pred.b <- predict(fit, lung.data, s = 0.5, type = "coxph",
mode = "lambda.fraction")
pred.s <- survfit(pred.b)
plot(pred.s)
detach(lung.data)
[Package
glmpath version 0.98
Index]