# Response Variables Represent a possibly censored or truncated response variable ```r R(object, ...) ## S3 method for class 'numeric' R(object = NA, cleft = NA, cright = NA, tleft = NA, tright = NA, tol = sqrt(.Machine$double.eps), as.R.ordered = FALSE, as.R.interval = FALSE, ...) ## S3 method for class 'ordered' R(object, cleft = NA, cright = NA, ...) ## S3 method for class 'integer' R(object, cleft = NA, cright = NA, bounds = c(min(object, na.rm = TRUE), Inf), ...) ## S3 method for class 'factor' R(object, ...) ## S3 method for class 'Surv' R(object, as.R.ordered = FALSE, as.R.interval = FALSE, ...) as.Surv(object) ## S3 method for class 'response' as.Surv(object) ## S3 method for class 'response' as.double(x, ...) ``` ## Arguments - `object`: A vector of (conceptually) exact measurements or an object of class `response` (for `as.Surv`) or a list. - `x`: same as `object`. - `cleft`: A vector of left borders of censored measurements - `cright`: A vector of right borders of censored measurements - `tleft`: A vector of left truncations - `tright`: A vector of right truncations - `tol`: Tolerance for checking if `cleft` < `cright` - `bounds`: Range of possible values for integers - `as.R.ordered`: logical, should numeric responses or right-censored (and possible left-truncated survival) times be coded as ordered factor? This leads to a parameterisation allowing to maximise the nonparametric maximum likelihood - `as.R.interval`: logical, should numeric responses be coded for the nonparametric maximum likelihood - ``...``: other arguments, ignored except for `tleft` and `tright` to `R.ordered` and `R.integer` ## Details `R` is basically an extention of `Surv` for the representation of arbitrarily censored or truncated measurements at any scale. The `storage.mode` of `object` determines if models are fitted by the discrete likelihood (integers or factors) or the continuous likelihood (log-density for numeric `object`s). Interval-censoring is given by intervals (`cleft`, `cright`], also for integers and factors (see example below). Left- and right-truncation can be defined by the `tleft` and `tright` arguments. Existing `Surv` objects can be converted using `R(Surv(...))`$ 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")`. ## References Torsten Hothorn (2020), Most Likely Transformations: The mlt Package, **Journal of Statistical Software**, 92 (1), 1--68, tools:::Rd_expr_doi("10.18637/jss.v092.i01") ## Examples ```r 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) ```

R function