Survival Curves for a Cox Proportional Hazards Model
Computes the predicted survivor function for a Cox proportional hazards model.
## S3 method for class 'mboost' survFit(object, newdata = NULL, ...) ## S3 method for class 'survFit' plot(x, xlab = "Time", ylab = "Probability", ...)
object |
an object of class |
newdata |
an optional data frame in which to look for variables with which to predict the survivor function. |
x |
an object of class |
xlab |
the label of the x axis. |
ylab |
the label of the y axis. |
... |
additional arguments passed to callies. |
If newdata = NULL, the survivor function of the Cox proportional
hazards model is computed for the mean of the covariates used in the
blackboost, gamboost, or glmboost
call. The Breslow estimator is used for computing the baseline survivor
function. If newdata is a data frame, the predict method
of object, along with the Breslow estimator, is used for computing the
predicted survivor function for each row in newdata.
An object of class survFit containing the following components:
surv |
the estimated survival probabilities at the time points
given in |
time |
the time points at which the survivor functions are evaluated. |
n.event |
the number of events observed at each time point given
in |
gamboost, glmboost and
blackboost for model fitting.
library("survival")
data("cancer", package = "survival")
fm <- Surv(futime,fustat) ~ age + resid.ds + rx + ecog.ps
fit <- glmboost(fm, data = ovarian, family = CoxPH(),
control=boost_control(mstop = 500))
S1 <- survFit(fit)
S1
newdata <- ovarian[c(1,3,12),]
S2 <- survFit(fit, newdata = newdata)
S2
plot(S1)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.