cum_haz is the Nelson-Aalen estimate of the cumulative hazard: at
each event time the number of events is divided by the number at risk, and
the ratios are summed. The surv column, its standard error and its
confidence limits are the Kaplan-Meier estimates, as kaplan
returns them, and hazard, density, life and
proplife are derived from that surv. The two functions
therefore differ only in cum_haz, where kaplan reports
\(-\log S(t)\). The two agree closely while the risk set is large and
diverge in the tail; when the last observation is an event, \(-\log S(t)\)
is infinite there and the Nelson-Aalen sum is not.
Arguments
- interval
name of the interval variable in the training dataset.
- censor
name of the censoring variable in the training dataset.
- data
name of the survival training data.frame
- by
stratifying variable in the training dataset, defaults to NULL
- weight
optional numeric vector of event weights, one per row of
data(defaultNULL, every event counts once). The weights apply to events only: each increment ofcum_hazis the summed weight of the events at that time over the unweighted number at risk, so a censored observation's weight has no effect. Use it for severity-weighted events. The Kaplan-Meier columns are not weighted.- ...
arguments passed to the
survfitfunction
Value
gg_survival object
See also
gg_survival nelson
plot.gg_survival
Examples
# These get run through the gg_survival examples.
data(pbc, package = "randomForestSRC")
pbc$time <- pbc$days / 364.25
# This is the same as gg_survival
gg_dta <- nelson(
interval = "time", censor = "status",
data = pbc
)
plot(gg_dta, error = "none")
plot(gg_dta)
# Stratified on treatment variable.
gg_dta <- gg_survival(
interval = "time", censor = "status",
data = pbc, by = "treatment"
)
plot(gg_dta, error = "none")
plot(gg_dta, error = "lines")
plot(gg_dta)
gg_dta <- gg_survival(
interval = "time", censor = "status",
data = pbc, by = "treatment",
type = "nelson"
)
plot(gg_dta, error = "bars")
plot(gg_dta)
