survConcordance(survival)
survConcordance()所属R语言包：survival
Compute a concordance measure.
计算出一个和谐的措施。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function computes the concordance between a right-censored survival time and a single continuous covariate
此函数计算审查的权利，生存时间和一个单一连续的协变量之间的一致性
用法----------Usage----------
survConcordance(formula, data, weights, subset, na.action)
参数----------Arguments----------
参数：formula
a formula with a survival time on the left and a single covariate on the right, along with an optional strata() term.
与存活时间在左侧和右侧的单个协变量的公式，随着可选strata()任期。
参数：data
a data frame
一个数据框
参数：weights,subset,na.action
as for coxph
coxph
Details
详情----------Details----------
Concordance is defined as Pr(agreement) for any two randomly chosen observations, where in this case agreement means that the observation with the shorter survival time of the two also has the larger risk score. The predictor (or risk score) will often be the result of a Cox model or other regression.
PR（协议）被定义为两个随机选择的任何意见，在这种情况下，协议意味着两个存活时间较短的观察也有较大的风险评分的一致性。 （或风险评分）预测，往往会是一个Cox模型或其他回归的结果。
For continuous covariates concordance is equivalent to Kendall's tau, and for logistic regression is is equivalent to the area under the ROC curve. A value of 1 signifies perfect agreement, .6-.7 is a common result for survival data, .5 is an agreement that is no better than chance, and .3-.4 is the performace of some stock market analysts.
对于连续变项的一致性是相当于肯德尔的头，logistic回归是相当于ROC曲线下的面积。值为1意味着完全一致，.6 - 0.7是一个共同的结果生存数据，0.5是一个协议，是没有比的机会，和.3 - .4是一些股市分析家的服务表现。
The computation involves all n(n-1)/2 pairs of data points in the sample. For survival data, however, some of the pairs are incomparable. For instance a pair of times (5+, 8), the first being a censored value. We do not know whether the first survival time is greater than or less than the second. Among observations that are comparable, pairs may also be tied on survival time (but only if both are uncensored) or on the predictor. The final concondance is (agree + tied/2)/(agree + disagree + tied).
计算涉及所有N（N-1）/ 2对样本中的数据点。生存数据，但是，一些对所无法比拟的。例如一双的次数（5 + 8），首先是截断值。我们不知道是否存活时间大于或小于第二。之间相媲美的意见，对可能还可以绑生存时间（但只有两个是未经审查）上的预测。最后concondance（同意/ 2 +并列）/（同意+同意+并列）。
There is, unfortunately, one aspect of the formula above that is unclear. Should the count of ties include observations that are tied on survival time y, tied on the predictor x, or both? By default the concordance only counts ties in x, treating tied survival times as incomparable; this agrees with the AUC calculation used in logistic regression. The Goodman-Kruskal Gamma statistic is (agree-disagree)/(agree + disagree), ignoring ties. It ranges from -1 to +1 similar to a correlation coefficient. Kendall's tau uses ties of both types. All of the components are returned in the result, however, so people can compute other combinations if interested. (If two observations have the same survival and the same x, they are counted in the tied survival time category).
有不幸的是，目前还不清楚上述公式的一个方面。计数的关系应该包括观察生存时间y并列，并列预测x，或两者兼而有之？默认情况下，一致只计算在x的关系，治疗并列为无与伦比的生存时间，这与logistic回归的AUC计算同意。古德曼克鲁斯卡尔伽玛统计（同意，不同意）/（同意+同意），忽略关系。它的范围从-1到+1的相关系数。肯德尔的头使用两种类型的关系。然而，所有的部件都在返回的结果，所以人们可以计算出其他组合，如果有兴趣。 （如果有两个意见相同的生存和相同的x，他们在绑的存活时间类别计算）。
值----------Value----------
an object containing the concordance, followed by the number of pairs that agree, disagree, are tied, and are not comparable.
一个对象，它包含的一致同意，不同意对数，其次，捆绑，并没有可比性。
参见----------See Also----------
summary.coxph
summary.coxph
举例----------Examples----------
survConcordance(Surv(time, status) ~age, data=lung)
options(na.action=na.exclude)
fit <- coxph(Surv(time, status) ~ ph.ecog + age + sex, lung)
survConcordance(Surv(time, status) ~predict(fit), lung)
## Not run: [＃无法运行：]
n=227 (1 observations deleted due to missing values)
Concordance= 0.6371102 , Gamma= 0.2759638
concordant discordant tied risk tied time
12544 7117 126 28
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。
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发表于 2012-2-17 09:52:40
