coxph(survival)
coxph()所属R语言包：survival
Fit Proportional Hazards Regression Model
适合比例风险回归模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Fits a Cox proportional hazards regression model. Time dependent variables, time dependent strata, multiple events per subject, and other extensions are incorporated using the counting process formulation of Andersen and Gill.
适合Cox比例风险回归模型。随时间变化的变量，随时间变化的阶层，每个主题的多个事件，和其他扩展纳入使用安德森和吉尔的点票过程制定。
用法----------Usage----------
coxph(formula, data=, weights, subset,
na.action, init, control,
method=c("efron","breslow","exact"),
singular.ok=TRUE, robust=FALSE,
model=FALSE, x=FALSE, y=TRUE, tt, ...)
参数----------Arguments----------
参数：formula
a formula object, with the response on the left of a ~ operator, and the terms on the right. The response must be a survival object as returned by the Surv function.
一个公式对象，响应~运营商，并在右侧的条款左侧。 Surv函数返回的响应必须是一个生存的对象。
参数：data
a data.frame in which to interpret the variables named in the formula, or in the subset and the weights argument.
数据框，在其中解释变量名为或formula和subset参数weights。
参数：weights
vector of case weights. If weights is a vector of integers, then the estimated coefficients are equivalent to estimating the model from data with the individual cases replicated as many times as indicated by weights.
向量的情况下重量。如果weights是一个整数向量，然后估计系数相当于多次复制的个别情况的数据估计模型weights。
参数：subset
expression indicating which subset of the rows of data should be used in the fit. All observations are included by default.
表达式，表示应在适合使用的数据行的一个子集。所有的意见，包括默认情况下。
参数：na.action
a missing-data filter function. This is applied to the model.frame after any subset argument has been used. Default is options()\$na.action.
丢失数据的过滤功能。这是适用于任意子集参数已用于model.frame后。默认options()\$na.action。
参数：init
vector of initial values of the iteration. Default initial value is zero for all variables.
矢量迭代的初始值。默认所有变量的初始值是零。
参数：control
Object of class coxph.control specifying iteration limit and other control options. Default is coxph.control(...).
对象的类coxph.control指定迭代的限制和其他控制选项。默认coxph.control(...)。
参数：method
a character string specifying the method for tie handling. If there are no tied death times all the methods are equivalent. Nearly all Cox regression programs use the Breslow method by default, but not this one. The Efron approximation is used as the default here, as it is much more accurate when dealing with tied death times, and is as efficient computationally. The exact method computes the exact partial likelihood, which is equivalent to a conditional logistic model. If there are a large number of ties the computational time will be excessive.
一个字符串，指定领带处理方法。如果有没有绑的死亡时间，所有的方法都是相同的。默认情况下，几乎所有的Cox回归计划使用布瑞斯罗夫方法，但不是这一个。埃弗龙近似这里作为默认，因为它是更准确的处理绑死亡时间时，作为高效计算。确切的方法计算的确切部分的可能性，这相当于一个条件Logistic模型。如果有大量的计算时间的关系将是过度。
参数：singular.ok
logical value indicating how to handle collinearity in the model matrix. If TRUE, the program will automatically skip over columns of the X matrix that are linear combinations of earlier columns. In this case the coefficients for such columns will be NA, and the variance matrix will contain zeros. For ancillary calculations, such as the linear predictor, the missing coefficients are treated as zeros.
逻辑值，该值指示如何处理共线性模型矩阵。如果TRUE，程序会自动跳过X矩阵列列早期的线性组合。在这种情况下，这样的列系数将不适用，方差矩阵将包含零。为辅助计算，如线性预测，失踪的系数被视为零。
参数：robust
if TRUE a robust variance estimate is returned. Default is TRUE if the model includes a cluster operative, FALSE otherwise.
如果TRUE返回一个强大的方差估计。默认是TRUE如果该模型包括一个cluster手术，FALSE否则。
参数：model
logical value: if TRUE, the model frame is returned in component model.
逻辑值：如果TRUE，模型框架在组件model返回。
参数：x
logical value: if TRUE, the x matrix is returned in component x.
逻辑值：如果TRUE，X矩阵在组件x返回。
参数：y
logical value: if TRUE, the response vector is returned in component y.
逻辑值：如果TRUE，响应向量在组件y返回。
参数：tt
optional list of time-transform functions.
时间转换功能的可选列表。
参数：...
Other arguments will be passed to coxph.control
其他参数将被传递coxph.control的
Details
详情----------Details----------
The proportional hazards model is usually expressed in terms of a single survival time value for each person, with possible censoring. Andersen and Gill reformulated the same problem as a counting process; as time marches onward we observe the events for a subject, rather like watching a Geiger counter. The data for a subject is presented as multiple rows or "observations", each of which applies to an interval of observation (start, stop].
比例风险模型通常表示在一个单一的每个人的生存时间值，可能审查。安德森和吉尔重新点票过程同样的问题，随着时间的游行前进，我们观察为主题的活动，而喜欢看盖革计数器，。为主题的数据，提出了多个行或“意见”，其中每个适用于观察间隔（启动，停止]。
值----------Value----------
an object of class coxph representing the fit. See coxph.object for details.
类coxph代表适合的对象。看到coxph.object详情。
副作用----------Side Effects----------
Depending on the call, the predict, residuals, and survfit routines may need to reconstruct the x matrix created by coxph. It is possible for this to fail, as in the example below in which the predict function is unable to find tform.
根据呼叫，predict，residuals，survfit例程可能需要重建的X矩阵coxph。这是可能的失败在下面的例子中的预测功能是无法找到tform。
特别条款----------Special terms----------
There are three special terms that may be used in the model equation. A strata term identifies a stratified Cox model; separate baseline hazard functions are fit for each strata. The cluster term is used to compute a robust variance for the model. The term + cluster(id) where each value of id is unique is equivalent to specifying the robust=T argument, and produces an approximate jackknife estimate of the variance. If the id variable were not unique, but instead identifies clusters of correlated observations, then the variance estimate is based on a grouped jackknife.
在模型方程，可用于有三个特殊条款。一个strata长期标识分层Cox模型;独立基线危险功能适合每个阶层。 cluster长期使用计算模型的鲁棒方差。一词+ cluster(id)每id价值是独一无二的，相当于指定robust=T参数，并产生近似刀切估计的方差。如果id变量是不是唯一的，而是确定集群的相关意见，然后方差的估计是基于分组的折刀。
A time-transform term allows variables to vary dynamically in time. In this case the tt argument will be a function or a list of functions (if there are more than one tt() term in the model) giving the appropriate transform. See the examples below.
一时间变换的长期允许变量随时间变化的动态。在这种情况下tt参数是一个函数或函数列表（如果有一个以上的TT（）长期在模型），给予适当的变换。见下面的例子。
收敛----------Convergence----------
In certain data cases the actual MLE estimate of a coefficient is infinity, e.g., a dichotomous variable where one of the groups has no events. When this happens the associated coefficient grows at a steady pace and a race condition will exist in the fitting routine: either the log likelihood converges, the information matrix becomes effectively singular, an argument to exp becomes too large for the computer hardware, or the maximum number of interactions is exceeded. (Nearly always the first occurs.) The routine attempts to detect when this has happened, not always successfully. The primary consequence for he user is that the Wald statistic = coefficient/se(coefficient) is not valid in this case and should be ignored; the likelihood ratio and score tests remain valid however.
MLE的估计系数的实际数据在某些情况下是无穷大，例如，一个二分变量的群体之一没有活动。当这种情况发生在稳步增长的相关系数和比赛条件，存在于装修的程序：无论是日志的可能性收敛，信息矩阵变得有效奇异，为exp的参数过大，成为计算机硬件，或最大相互作用的数量超出。 （几乎都是第一次出现。）例程尝试检测当发生这种情况，并不总是成功。用户对他的主要后果是，Wald统计量=系数/ SE（系数）是无效在这种情况下，应该被忽略的可能性比和评分测试但仍然有效。
惩罚的回归----------Penalized regression----------
coxph can now maximise a penalised partial likelihood with arbitrary user-defined penalty. Supplied penalty functions include ridge regression (ridge), smoothing splines (pspline), and frailty models (frailty).
coxph现在可以最大限度地与任意用户定义的刑罚部分受罚的可能性。提供的刑罚功能包括岭回归（脊），平滑样条（pspline），脆弱性模型（脆弱）。
参考文献----------References----------
Cox's regression model for counting processes, a large sample study. Annals of Statistics 10, 1100-1120.
Springer-Verlag, 2000.
参见----------See Also----------
cluster, strata, Surv, survfit, pspline, frailty, ridge.
cluster，strata，Surv，survfit，pspline，frailty，ridge。
举例----------Examples----------
# Create the simplest test data set [创建简单的测试数据集]
test1 <- list(time=c(4,3,1,1,2,2,3),
status=c(1,1,1,0,1,1,0),
x=c(0,2,1,1,1,0,0),
sex=c(0,0,0,0,1,1,1))
# Fit a stratified model [适合分层模型]
coxph(Surv(time, status) ~ x + strata(sex), test1)
# Create a simple data set for a time-dependent model [一个随时间变化的模型，创建一个简单的数据集]
test2 <- list(start=c(1,2,5,2,1,7,3,4,8,8),
stop=c(2,3,6,7,8,9,9,9,14,17),
event=c(1,1,1,1,1,1,1,0,0,0),
x=c(1,0,0,1,0,1,1,1,0,0))
summary(coxph(Surv(start, stop, event) ~ x, test2))
#[]
# Create a simple data set for a time-dependent model[一个随时间变化的模型，创建一个简单的数据集]
#[]
test2 <- list(start=c(1, 2, 5, 2, 1, 7, 3, 4, 8, 8),
stop =c(2, 3, 6, 7, 8, 9, 9, 9,14,17),
event=c(1, 1, 1, 1, 1, 1, 1, 0, 0, 0),
x =c(1, 0, 0, 1, 0, 1, 1, 1, 0, 0) )
summary( coxph( Surv(start, stop, event) ~ x, test2))
# Fit a stratified model, clustered on patients [适合分层模型，对患者聚集]
bladder1 <- bladder[bladder$enum < 5, ]
coxph(Surv(stop, event) ~ (rx + size + number) * strata(enum) +
cluster(id), bladder1)
# Fit a time transform model using current age[使用目前的年龄适合的时间变换模型]
coxph(Surv(time, status) ~ ph.ecog + tt(age), data=lung,
tt=function(x,t,...) pspline(x + t/365.25))
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。
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发表于 2012-2-17 09:54:40
