Response Variables
Represent a possibly censored or truncated response variable
R(object, ...) ## S3 method for class 'numeric' R(object = NA, cleft = NA, cright = NA, tleft = NA, tright = NA, tol = sqrt(.Machine$double.eps), as.R.ordered = FALSE, ...) ## S3 method for class 'ordered' R(object, cleft = NA, cright = NA, ...) ## S3 method for class 'integer' R(object, cleft = NA, cright = NA, bounds = c(min(object), Inf), ...) ## S3 method for class 'factor' R(object, ...) ## S3 method for class 'Surv' R(object, as.R.ordered = FALSE, ...) as.Surv(object) ## S3 method for class 'response' as.Surv(object)
object |
A vector of (conceptually) exact measurements or an object of class
|
cleft |
A vector of left borders of censored measurements |
cright |
A vector of right borders of censored measurements |
tleft |
A vector of left truncations |
tright |
A vector of right truncations |
tol |
Tolerance for checking if |
bounds |
Range of possible values for integers |
as.R.ordered |
logical, should numeric responses or right-censored (and possible left-truncated survival) times be coded as ordered factor? This leads to a parameterisation allowing to maximise the nonparametric maximum likelihood. |
... |
other arguments, ignored except for |
R is basically an extention of Surv for the
representation of arbitrarily censored or truncated measurements at any scale.
The storage.mode of object determines if models are fitted
by the discrete likelihood (integers or factors) or the continuous
likelihood (log-density for numeric objects). Interval-censoring
is given by intervals (cleft, cright], also for integers and
factors (see example below). Left- and right-truncation can be defined
by the tleft and tright arguments. Existing Surv
objects can be converted using R(Surv(...))$ and, in some cases, an
as.Surv() method exists for representing response objects as
Surv objects.
R applied to a list calls R for each of the list elements
and returns a joint object.
More examples can be found in Hothorn (2018) and in
vignette("tram", package = "tram").
Torsten Hothorn (2020), Most Likely Transformations: The mlt Package, Journal of Statistical Software, 92(1), 1–68, doi: 10.18637/jss.v092.i01
library("survival")
### randomly right-censored continuous observations
time <- as.double(1:9)
event <- rep(c(FALSE, TRUE), length = length(time))
Surv(time, event)
R(Surv(time, event))
### right-censoring, left-truncation
ltm <- 1:9 / 10
Surv(ltm, time, event)
R(Surv(ltm, time, event))
### interval-censoring
Surv(ltm, time, type = "interval2")
R(Surv(ltm, time, type = "interval2"))
### interval-censoring, left/right-truncation
lc <- as.double(1:4)
lt <- c(NA, NA, 7, 8)
rt <- c(NA, 9, NA, 10)
x <- c(3, NA, NA, NA)
rc <- as.double(11:14)
R(x, cleft = lt, cright = rt)
as.Surv(R(x, cleft = lt, cright = rt))
R(x, tleft = 1, cleft = lt, cright = rt)
R(x, tleft = 1, cleft = lt, cright = rt, tright = 15)
R(x, tleft = lc, cleft = lt, cright = rt, tright = rc)
### discrete observations: counts
x <- 0:9
R(x)
### partially interval-censored counts
rx <- c(rep(NA, 6), rep(15L, 4))
R(x, cright = rx)
### ordered factor
x <- gl(5, 2, labels = LETTERS[1:5], ordered = TRUE)
R(x)
### interval-censoring (ie, observations can have multiple levels)
lx <- ordered(c("A", "A", "B", "C", "D", "E"),
levels = LETTERS[1:5], labels = LETTERS[1:5])
rx <- ordered(c("B", "D", "E", "D", "D", "E"),
levels = LETTERS[1:5], labels = LETTERS[1:5])
R(rx, cleft = lx, cright = rx)
### facilitate nonparametric maximum likelihood
(y <- round(runif(10), 1))
R(y, as.R.ordered = TRUE)
R(Surv(time, event), as.R.ordered = TRUE)
R(Surv(ltm, time, event), as.R.ordered = TRUE)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.