Description Usage Arguments Value Examples
survival (or hazard) function based on e and n.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  sf(x, ...)
## Default S3 method:
sf(x, ..., what = c("S", "H"), SCV = FALSE,
times = NULL)
## S3 method for class 'ten'
sf(x, ..., what = c("S", "H"), SCV = FALSE, times = NULL,
reCalc = FALSE)
## S3 method for class 'stratTen'
sf(x, ..., what = c("S", "H"), SCV = FALSE,
times = NULL, reCalc = FALSE)
## S3 method for class 'numeric'
sf(x, ..., n = NULL, what = c("all", "S", "Sv", "H",
"Hv"), SCV = FALSE, times = NULL)

x 
One of the following:

... 
Additional arguments (not implemented). 
n 
Number at risk. 
what 
See return, below. 
SCV 
Include the Squared Coefficient of Variation, which is calcluated using the mean mean(x) and the variance var(x): SCV[x] = var(x) / mean(x)^2 This measure of dispersion is also referred to as the 'standardized variance' or the 'noise'. 
times 
Times for which to calculate the function.

reCalc 
Recalcuate the values?

A data.table which is stored as an attribute of
the ten
object.
If what="s"
, the survival is returned, based on the
KaplanMeier or productlimit estimator.
This is 1 at t=0 and thereafter is given by:
S[t] = prod (1  e[t]) / n[t]
If what="sv"
, the survival variance is returned.
Greenwoods estimtor of the variance of the
KaplanMeier (productlimit) estimator is:
Var(S[t]) = S[t]^2 sum e[t] / (n[t] * (n[t]  e[t]))
If what="h"
, the hazard is returned,
based on the the NelsonAalen estimator.
This has a value of H=0 at t=0
and thereafter is given by:
H[t] = sum(e[t] / n[t])
If what="hv"
, the hazard variance is returned.
The variance of the NelsonAalen estimator is given by:
Var(H[t]) = sum(e / n^2)
If what="all"
(the default), all of the above
are returned in a data.table
, along with:
Survival, based on the NelsonAalen hazard estimator H,
which is:
S[t] = exp(H[t])
Hazard, based on the KaplanMeier survival estimator S, which is:
H[t] = log(S[t])
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  data("kidney", package="KMsurv")
k1 < ten(Surv(time=time, event=delta) ~ type, data=kidney)
sf(k1)
sf(k1, times=1:10, reCalc=TRUE)
k2 < ten(with(kidney, Surv(time=time, event=delta)))
sf(k2)
## K&M. Table 4.1A, pg 93.
## 6MP patients
data("drug6mp", package="KMsurv")
d1 < with(drug6mp, Surv(time=t2, event=relapse))
(d1 < ten(d1))
sf(x=d1$e, n=d1$n, what="S")
data("pbc", package="survival")
t1 < ten(Surv(time, status==2) ~ log(bili) + age + strata(edema), data=pbc)
sf(t1)
## K&M. Table 4.2, pg 94.
data("bmt", package="KMsurv")
b1 < bmt[bmt$group==1, ] # ALL patients
t2 < ten(Surv(time=b1$t2, event=b1$d3))
with(t2, sf(x=e, n=n, what="Hv"))
## K&M. Table 4.3, pg 97.
sf(x=t2$e, n=t2$n, what="all")

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