clogit(survival)
clogit()所属R语言包：survival
Conditional logistic regression
条件Logistic回归分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimates a logistic regression model by maximising the conditional likelihood. Uses a model formula of the form case.status~exposure+strata(matched.set). The default is to use the exact conditional likelihood, a commonly used approximate conditional likelihood is provided for compatibility with older software.
logistic回归模型估计的条件的可能性最大化。使用模型公式的形式case.status~exposure+strata(matched.set)。默认情况下使用的确切条件的可能性，一种常用的近似条件的可能性是与旧版本软件的兼容性。
用法----------Usage----------
clogit(formula, data, weights, subset, na.action,
method=c("exact", "approximate", "efron", "breslow"),
...)
参数----------Arguments----------
参数：formula
Model formula
模型公式
参数：data
data frame
数据框
参数：weights
optional, names the variable containing case weights
可选的，命名变量的收纳箱重量
参数：subset
optional, subset the data
可选的子集的数据
参数：na.action
optional na.action argument. By default the global option na.action is used.
可选na.action的说法。默认情况下，全局选项“na.action使用。
参数：method
use the correct (exact) calculation in the conditional likelihood or one of the approximations
使用正确的在有条件的似然率计算（精确的）或1的近似值
参数：...
optional arguments, which will be passed to coxph.control
可选的参数，这些参数将被传递给coxph.control
Details
详细信息----------Details----------
A stratified Cox model with time=constant, status of 1=case 0=control, and using the exact partial likelihood has the same likelihood formula as a conditional logistic regression. The clogit routine creates the necessary dummy variable of times (all 1) and then calls coxph.
随着时间的推移一个分层的Cox比例风险模型=常数，状态= 1的情况下，0 =控制，并采用精确的局部似然作为条件Logistic回归公式具有相同的可能性。的clogit程序创建所需的虚拟变量倍（所有1），，然后调用coxph。
The computation of the exact partial likelihood can be very slow, however. If a particular strata had say 10 events out of 20 subjects we have to add up a denominator that involves all possible ways of choosing 10 out of 20, which is 20!/(10! 10!) = 184756 terms. Gail et al describe a fast recursion method, which largely ameleorates this; it was incorporated into version 2.36-11 of the survival package. (Most of the time conditional logistic modeling is applied data with 1 case + k controls per set, however, which involves choosing 1 out of k and the computational issue above does not arise.) The 'appoximate' option maps to the Breslow approximation for historical reasons.
计算精确的局部似然可以是非常缓慢的，但是。如果一个特定的阶层已经说了10个项目的20个科目，我们添加了一个分母，涉及所有可能的方式选择10 20，20 /（10 10）= 184756条款。盖尔等人描述了一种快速递归的方法，这在很大程度上ameleorates此，它被纳入的生存包的版本2.36-11。 （大部分的时间条件Logistic模型应用的数据有1例+ K控制每套，但是，这涉及到选择1 k和上面的计算问题不会出现。）appoximate的“选项映射到布瑞斯罗夫近似由于历史的原因。
It is not clear how case weights should be handled. For instance if there are two deaths in a strata, one with weight=1 and one with weight=2, should the likelihood calculation consider all subsets of size 2 or all subsets of size 3? Consequently, case weights are ignored by the routine.
目前尚不清楚的情况下，权重应该如何处理。例如，如果有两人死亡，一个阶层，一个带权重= 1，一个体重= 2，计算的可能性应该考虑大小为2或所有子集的所有子集的大小为3？因此，情况权重将被忽略的常规。
值----------Value----------
An object of class "clogit", which is a wrapper for a "coxph" object.
类的一个对象"clogit"，这是一个包装的"coxph"对象。
参考文献----------References----------
calculations for matched case-control studies and survival studies with tied death times. Biometrika 68:703-707, 1980.
（作者）----------Author(s)----------
Thomas Lumley
参见----------See Also----------
strata,coxph,glm
strata，coxph，glm
实例----------Examples----------
## Not run: clogit(case ~ spontaneous + induced + strata(stratum), data=infert)[＃未运行：clogit（的情况下自发+引起的地层（层），数据infert）]
# A multinomial response recoded to use clogit[多项式响应重新编码，使用clogit]
# The revised data set has one copy per possible outcome level, with new[修订后的数据集有一个副本每个可能的结果水平，与新]
# variable tocc = target occupation for this copy, and case = whether[变量TOCC这个副本的目标职业和案例=是否]
# that is the actual outcome for each subject.[这是每个科目的实际结果。]
# See the catspec package for more details.[有关详细信息，请参阅catspec包。]
resp <- levels(logan$occupation)
n <- nrow(logan)
indx <- rep(1:n, length(resp))
logan2 <- data.frame(logan[indx,],
id = indx,
tocc = factor(rep(resp, each=n)))
logan2$case <- (logan2$occupation == logan2$tocc)
clogit(case ~ tocc + tocc:education + strata(id), logan2)
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。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
|
发表于 2012-9-30 20:44:30
