surv2ts {TwoTimeScales} | R Documentation |
Survival function with two time scales
Description
Computes the survival matrix, that contains the probability of not
experiencing an event (of any cause) by time s
and fixed entry time u
.
The survival function can be obtained from one fitted model with only one
event type, or combining information from several cause-specific hazard
in a competing risks model. In the first case, a fitted object of class 'haz2ts'
or 'haz2tsLMM'
can be passed directly as argument to the function. In the
competing risks framework, the user should provide a list of cause-specific
cumulative hazard matrices. The function is also called internally from plot()
if the user wants to plot the cumulative hazard from a fitted model.
Usage
surv2ts(
cumhaz = NULL,
fitted_model = NULL,
plot_grid = NULL,
cause = NULL,
midpoints = FALSE,
where_slices = NULL,
direction = c("u", "s", NULL),
tmax = NULL
)
Arguments
cumhaz |
(optional) a list with all the cause-specific cumulated hazard matrices (minimum one element needs to be supplied). If more than one cause-specific cumulated hazard is provided, then they should all be matrices of the same dimension. |
fitted_model |
(optional) The output of the function |
plot_grid |
(optional) A list containing the parameters to build a new
finer grid of intervals over
|
cause |
a character string with a short name for the cause (optional). |
midpoints |
A Boolean. Default is |
where_slices |
A vector of values for the cutting points of the desired
slices of the surface. This option is included mostly for the plotting function.
When using |
direction |
If cross-sectional one-dimensional curves are plotted, this
indicates whether the cutting points are located on the |
tmax |
The maximum value of |
Value
a matrix containing the values of the survival function over s
and u
.
Examples
# Create some fake data - the bare minimum
id <- 1:20
u <- c(5.43, 3.25, 8.15, 5.53, 7.28, 6.61, 5.91, 4.94, 4.25, 3.86, 4.05, 6.86,
4.94, 4.46, 2.14, 7.56, 5.55, 7.60, 6.46, 4.96)
s <- c(0.44, 4.89, 0.92, 1.81, 2.02, 1.55, 3.16, 6.36, 0.66, 2.02, 1.22, 3.96,
7.07, 2.91, 3.38, 2.36, 1.74, 0.06, 5.76, 3.00)
ev <- c(1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1)
fakedata <- as.data.frame(cbind(id, u, s, ev))
fakedata2ts <- prepare_data(u = fakedata$u,
s_out = fakedata$s,
ev = fakedata$ev,
ds = .5)
# Fit a fake model - not optimal smoothing
fakemod <- fit2ts(fakedata2ts,
optim_method = "grid_search",
lrho = list(seq(1 , 1.5, .5),
seq(1 , 1.5, .5)))
# Obtain the fake cumulated hazard
fakecumhaz2ts <- cumhaz2ts(fakemod)
# Fake survival curve
fakesurv2ts <- surv2ts(fitted_model = fakemod)