spatstat (version 1.3-2)

# reduced.sample: Reduced Sample Estimator using Histogram Data

## Description

Compute the Reduced Sample estimator of a survival time distribution function, from histogram data

## Usage

reduced.sample(nco, cen, ncc, show=FALSE)

## Arguments

nco
vector of counts giving the histogram of uncensored observations (those survival times that are less than or equal to the censoring time)
cen
vector of counts giving the histogram of censoring times
ncc
vector of counts giving the histogram of censoring times for the uncensored observations only
show
Logical value controlling the amount of detail returned by the function value (see below)

## Value

• If show = FALSE, a numeric vector giving the values of the reduced sample estimator. If show=TRUE, a list with three components which are vectors of equal length,
• rsReduced sample estimate of the survival time c.d.f. $F(t)$
• numeratornumerator of the reduced sample estimator
• denominatordenominator of the reduced sample estimator

## Details

This function is needed mainly for internal use in spatstat, but may be useful in other applications where you want to form the reduced sample estimator from a huge dataset.

Suppose $T_i$ are the survival times of individuals $i=1,\ldots,M$ with unknown distribution function $F(t)$ which we wish to estimate. Suppose these times are right-censored by random censoring times $C_i$. Thus the observations consist of right-censored survival times $\tilde T_i = \min(T_i,C_i)$ and non-censoring indicators $D_i = 1{T_i \le C_i}$ for each $i$.

If the number of observations $M$ is large, it is efficient to use histograms. Form the histogram cen of all censoring times $C_i$. That is, obs[k] counts the number of values $C_i$ in the interval (breaks[k],breaks[k+1]] for $k > 1$ and [breaks,breaks] for $k = 1$. Also form the histogram nco of all uncensored times, i.e. those $\tilde T_i$ such that $D_i=1$, and the histogram of all censoring times for which the survival time is uncensored, i.e. those $C_i$ such that $D_i=1$. These three histograms are the arguments passed to kaplan.meier.

The return value rs is the reduced-sample estimator of the distribution function $F(t)$. Specifically, rs[k] is the reduced sample estimate of F(breaks[k+1]). The value is exact, i.e. the use of histograms does not introduce any approximation error.

## See Also

kaplan.meier, kmrs