kaplan.meier(spatstat)
kaplan.meier()所属R语言包：spatstat
Kaplan-Meier Estimator using Histogram Data
Kaplan-Meier法估计使用直方图数据
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Compute the Kaplan-Meier estimator of a survival time distribution function, from histogram data
从直方图数据计算的Kaplan-Meier的生存时间分布函数的估计，
用法----------Usage----------
kaplan.meier(obs, nco, breaks, upperobs=0)
参数----------Arguments----------
参数：obs
vector of n integers giving the histogram of all observations (censored or uncensored survival times)
向量，n给所有观测数据的直方图（审查或未经审查的生存时间整数）
参数：nco
vector of n integers giving the histogram of uncensored observations (those survival times that are less than or equal to the censoring time)
矢量n给直方图未经审查的观测（那些存活时间是小于或等于的审查时间的整数）
参数：breaks
Vector of n+1 breakpoints which were used to form both histograms.
向量的n+1被用来形成两个直方图断点。
参数：upperobs
Number of observations beyond the rightmost breakpoint, if any.
超出最右边的断点的观测数，如果有的话。
Details
详细信息----------Details----------
This function is needed mainly for internal use in spatstat, but may be useful in other applications where you want to form the Kaplan-Meier estimator from a huge dataset.
此功能主要用于需要内部使用中spatstat的，但在其他的应用中，你想从一个巨大的数据集形成的Kaplan-Meier估计可能是有用的。
Suppose T[i] are the survival times of individuals i=1,…,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 T*[i] = min(T[i],C[i]) and non-censoring indicators D[i] = 1(T[i] <= C[i]) for each i.
假设T[i]是个人的存活时间i=1,…,M未知分布的功能F(t)我们希望估计。假设这些时间右删失随机审查倍C[i]。因此，观察由右删失的生存时间T*[i] = min(T[i],C[i])和非设限指标D[i] = 1(T[i] <= C[i])每个i。
If the number of observations M is large, it is efficient to use histograms. Form the histogram obs of all observed times T*[i]. That is, obs[k] counts the number of values T*[i] in the interval (breaks[k],breaks[k+1]] for k > 1 and [breaks[1],breaks[2]] for k = 1. Also form the histogram nco of all uncensored times, i.e. those T*[i] such that D[i]=1. These two histograms are the arguments passed to kaplan.meier.
如果该号码的观察M是大的，它是有效利用直方图。形成直方图obs所有观察到的时间T*[i]。也就是说，obs[k]计值的数量T*[i]在区间(breaks[k],breaks[k+1]]的k > 1和[breaks[1],breaks[2]]k = 1。另外，形成的直方图的nco所有未经审查的时间，即T*[i]D[i]=1。这两个直方图的参数传递给kaplan.meier。
The vectors km and lambda returned by kaplan.meier are (histogram approximations to) the Kaplan-Meier estimator of F(t) and its hazard rate lambda(t). Specifically, km[k] is an estimate of F(breaks[k+1]), and lambda[k] is an estimate of the average of lambda(t) over the interval (breaks[k],breaks[k+1]).
的矢量km和lambda返回kaplan.meier（直方图近似）的Kaplan-Meier估计F(t)和它的危险率lambda(t)。具体来说，km[k]是估计F(breaks[k+1])，lambda[k]是一个估计的平均lambda(t)在区间(breaks[k],breaks[k+1])。
The histogram breaks must include 0. If the histogram breaks do not span the range of the observations, it is important to count how many survival times T*[i] exceed the rightmost breakpoint, and give this as the value upperobs.
直方图截断必须包括：0。如果直方图的场所不跨越的观测范围内，重要的是算多少的存活时间T*[i]超过最右边的断点，并且给这个作为值upperobs。
值----------Value----------
A list with two elements:
有两个元素的列表：
参数：km
Kaplan-Meier estimate of the survival time c.d.f. F(t)
Kaplan-Meier估计的存活时间c.d.f. F(t)
参数：lambda
corresponding Nelson-Aalen estimate of the hazard rate lambda(t)
相应的纳尔逊 - 阿伦估计的危险率lambda(t)
These are numeric vectors of length n.
这些数字向量的长度n。
（作者）----------Author(s)----------
Adrian Baddeley
<a href="mailto:Adrian.Baddeley@csiro.au">Adrian.Baddeley@csiro.au</a>
<a href="http://www.maths.uwa.edu.au/~adrian/">http://www.maths.uwa.edu.au/~adrian/</a>
and Rolf Turner
<a href="mailto:r.turner@auckland.ac.nz">r.turner@auckland.ac.nz</a>
参见----------See Also----------
reduced.sample, km.rs
reduced.sample，km.rs
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
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发表于 2012-9-30 13:38:49
