coxph.detail(survival)
coxph.detail()所属R语言包：survival
Details of a Cox Model Fit
详细的Cox模型拟合
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Returns the individual contributions to the first and second derivative matrix, at each unique event time.
返回到第一和第二的衍生矩阵的个人捐款，在每一个独特的事件时间。
用法----------Usage----------
coxph.detail(object, riskmat=FALSE)
参数----------Arguments----------
参数：object
a Cox model object, i.e., the result of coxph.
Cox模型对象，即coxph结果。
参数：riskmat
include the at-risk indicator matrix in the output?
在输出中包括在风险指标矩阵？
Details
详情----------Details----------
This function may be useful for those who wish to investigate new methods or extensions to the Cox model. The example below shows one way to calculate the Schoenfeld residuals.
此功能可能是有用的为那些希望Cox模型研究新的方法或扩展。下面的例子显示了一个方法来计算的Schoenfeld残差。
值----------Value----------
a list with components
一个组件的列表
参数：time
the vector of unique event times
独特的事件时间的向量
参数：nevent
the number of events at each of these time points.
在这些时间点的事件数量。
参数：means
a matrix with one row for each event time and one column for each variable in the Cox model, containing the weighted mean of the variable at that time, over all subjects still at risk at that time. The weights are the risk weights exp(x %*% fit$coef).
与每个事件的时间和一列，一列Cox模型中的每个变量的矩阵，包含当时的变量的加权平均，超过当时所有科目仍处于风险。权重的风险权重exp(x %*% fit$coef)。
参数：nrisk
number of subjects at risk.
风险科目的数量。
参数：score
the contribution to the score vector (first derivative of the log partial likelihood) at each time point.
在每个时间点贡献得分矢量（一阶导数的日志部分的可能性）。
参数：imat
the contribution to the information matrix (second derivative of the log partial likelihood) at each time point.
在每个时间点的信息矩阵（二阶导数的日志部分的可能性）的贡献。
参数：hazard
the hazard increment. Note that the hazard and variance of the hazard are always for some particular future subject. This routine uses object$mean as the future subject.
危险增量。注意的危害和危险的变异总是为一些特定的未来主题。这个例程使用object$mean作为未来的主题。
参数：varhaz
the variance of the hazard increment.
方差危险增量。
参数：x,y
copies of the input data.
输入数据的副本。
参数：strata
only present for a stratified Cox model, this is a table giving the number of time points of component time that were contributed by each of the strata.
目前只有一个分层的Cox模型，这是一个表，让组件time各阶层贡献的时间点。
参数：riskmat
a matrix with one row for each time and one column for each observation containing a 0/1 value to indicate whether that observation was (1) or was not (0) at risk at the given time point.
每次一行和一列包含0/1值的观察表明，观察是否是（1），或者是不是在给定的时间点的风险（0）每个矩阵。
参见----------See Also----------
coxph, residuals.coxph
coxph，residuals.coxph
举例----------Examples----------
fit <- coxph(Surv(futime,fustat) ~ age + rx + ecog.ps, ovarian, x=TRUE)
fitd <- coxph.detail(fit)
# There is one Schoenfeld residual for each unique death. It is a[有1舍恩每一个独特的死亡的残余。这是一个]
# vector (covariates for the subject who died) - (weighted mean covariate[向量（协变量的问题，谁死） - （加权平均协]
# vector at that time). The weighted mean is defined over the subjects[当时的向量）。定义过的科目的加权平均]
# still at risk, with exp(X beta) as the weight.[仍然处于危险之中，EXP（xβ）为重。]
events <- fit$y[,2]==1
etime <- fit$y[events,1] #the event times --- may have duplicates[事件的时间---可能有重复]
indx <- match(etime, fitd$time)
schoen <- fit$x[events,] - fitd$means[indx,]
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 2012-2-16 18:58:11
