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#' compileCDF.R
#'
#' Wrappers for estimating CDF from right-censored data
#'
#' Copyright (c) 1991-2023 Adrian Baddeley, Rolf Turner and Ege Rubak
#' GNU Public Licence (>= 2.0)
#'
#' $Revision: 1.1 $ $Date: 2023/11/04 04:45:32 $
censtimeCDFest <- function(o, cc, d, breaks, ...,
KM=TRUE, RS=TRUE, HAN=TRUE, RAW=TRUE,
han.denom=NULL, tt=NULL, pmax=0.9,
fname="CDF", fexpr=quote(CDF(r))) {
# Histogram-based estimation of cumulative distribution function
# of lifetimes subject to censoring.
# o: censored lifetimes min(T_i,C_i)
# cc: censoring times C_i
# d: censoring indicators 1(T_i <= C_i)
# breaks: histogram breakpoints (vector or 'breakpts' object)
# han.denom: denominator (eroded area) for each value of r
# tt: uncensored lifetimes T_i, if known
breaks <- as.breakpts(breaks)
bval <- breaks$val
rval <- breaks$r
rmax <- breaks$max
# Kaplan-Meier and/or Reduced Sample
out <- km.rs.opt(o, cc, d, breaks, KM=KM, RS=RS)
# convert to data frame
out$breaks <- NULL
df <- as.data.frame(out)
# Raw ecdf of observed lifetimes if available
if(RAW && !is.null(tt)) {
h <- whist(tt[tt <= rmax], breaks=bval)
df <- cbind(df, data.frame(raw=cumsum(h)/length(tt)))
}
# Hanisch
if(HAN) {
if(is.null(han.denom))
stop("Internal error: missing denominator for Hanisch estimator")
if(length(han.denom) != length(rval))
stop(paste("Internal error:",
"length(han.denom) =", length(han.denom),
"!=", length(rval), "= length(rvals)"))
# uncensored distances
x <- o[d]
# calculate Hanisch estimator
h <- whist(x[x <= rmax], breaks=bval)
H <- cumsum(h/han.denom)
df <- cbind(df, data.frame(han=H/max(H[is.finite(H)])))
}
# determine appropriate plotting range
bestest <- if(KM) "km" else if(HAN) "han" else if(RS) "rs" else "raw"
alim <- range(df$r[df[[bestest]] <= pmax])
# convert to fv object
nama <- c("r", "km", "hazard", "han", "rs", "raw")
avail <- c(TRUE, KM, KM, HAN, RS, RAW)
iscdf <- c(FALSE, TRUE, FALSE, TRUE, TRUE, TRUE)
labl <- c("r",
makefvlabel(NULL, "hat", fname, "km"),
"hat(lambda)(r)",
makefvlabel(NULL, "hat", fname, "han"),
makefvlabel(NULL, "hat", fname, "bord"),
makefvlabel(NULL, "hat", fname, "raw")
)[avail]
desc <- c("distance argument r",
"Kaplan-Meier estimate of %s",
"Kaplan-Meier estimate of hazard function lambda(r)",
"Hanisch estimate of %s",
"border corrected estimate of %s",
"uncorrected estimate of %s")[avail]
df <- df[, nama[avail]]
Z <- fv(df, "r", fexpr, bestest, . ~ r, alim, labl, desc,
fname=fname)
fvnames(Z, ".") <- nama[iscdf & avail]
return(Z)
}
# simple interface for students and code development
compileCDF <- function(D, B, r, ..., han.denom=NULL, check=TRUE) {
han <- !is.null(han.denom)
breaks <- breakpts.from.r(r)
if(check) {
stopifnot(length(D) == length(B) && all(D >= 0) && all(B >= 0))
if(han)
stopifnot(length(han.denom) == length(r))
}
D <- as.vector(D)
B <- as.vector(B)
# observed (censored) lifetimes
o <- pmin.int(D, B)
# censoring indicators
d <- (D <= B)
# go
result <- censtimeCDFest(o, B, d, breaks,
HAN=han,
han.denom=han.denom,
RAW=TRUE, tt=D)
result <- rebadge.fv(result, new.fname="compileCDF")
}
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