survivROC(RCASPAR)
survivROC()所属R语言包：RCASPAR
Generates the ROC curve at a given time point given the observed and predicted survival data in the presence of censored subjects.
在审查科目存在的观察和预测的生存数据，在特定的时间点产生的ROC曲线。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function generates the Receiver-Operator Curve (ROC) at the specified time point using predicted and observed data in the presence of censored subjects. It so plots (1 - specificity) against the (specificity) at the designated cut off points. It is based on Patrick Heagerty's survivalROC function in the survivalROC package.
函数生成在指定的时间点在审查主体的存在，预测和观测数据的接收器操作曲线（ROC）。它使图（1 - 特异性）反对（特异性）在指定切客点。它是基于对帕特里克Heagerty的在survivalROC包survivalROC功能。
用法----------Usage----------
survivROC(Stime, status, marker, entry = NULL, predict.time, cut.values = NULL, plot = TRUE)
参数----------Arguments----------
参数：Stime
The observed survival times of the patients.
观察病人的生存时间。
参数：status
The censoring status of the patient. 1 for a censored patient, and 0 for a patient who has an event.
审查病人的状态。 1截断病人，病人事件0。
参数：marker
The predicted survival time of the patients.
预测病人的存活时间。
参数：entry
The time of entry of the patients, set to NULL by default.
默认情况下设置为NULL的患者，进入的时间。
参数：predict.time
The time point for which the ROC curve is to be plotted.
ROC曲线是时间点要绘制。
参数：cut.values
The cut off values for which the ROC curves are to be constructed.
关闭值ROC曲线是将建造的切割。
参数：plot
A logical argument that specifies whether a plot is to be generated (TRUE) or not (FALSE). The argument is set to TRUE by default.
一个逻辑参数，指定是否有一个图是要生成（TRUE）或否（false）。参数设置默认为true。
Details
详情----------Details----------
This function is basically the survivROC function in Patrick Heagerty's survivROC package, with slight modifications to it to better suit our purpose. Unlike Heagerty's function it only performs the calculations using the KM estimator and does not provide any other methods as options.
此功能是基本的在帕特里克Heagerty survivROC包survivROC功能，稍作修改，以更好地满足我们的目的。 Heagerty的功能不同，它仅执行使用KM估计计算，并没有提供任何其他方法为选项。
值----------Value----------
参数：cut.values
the cut off values for which the sensitivity and (1-specificity) were calculated
关闭削减的敏感性和（1 - 特异性）的计算值
参数：Comp.Specificity
(1 - Specificity) as was calculated for every cut off value.
（1 - 特异性）的计算，每切断价值。
参数：Sensitivity
The Sensitivity as was calculated for every cut off value
灵敏度为每切断价值计算
参数：predict.time
The time point for which the ROC curve is calculated
ROC曲线计算的时间点
参数：Survival
The value of the estimated survivor function (using the KM estimator) at "predict.time"
估计生还的功能（使用KM估计在“predict.time”的价值）
参数：AUC
The value of the area under the estimated ROC curve
估计ROC曲线下的面积值
注意----------Note----------
It is important to note that using the Kaplan-Meier estimator as the method for estimating the survival function results in non-monotonous curves in some instances.
重要的是要注意使用的Kaplan-Meier法估计生存函数的结果在某些情况下，非单调曲线估计。
作者（S）----------Author(s)----------
Douaa Mugahid
参考文献----------References----------
参见----------See Also----------
survivAURC
survivAURC
举例----------Examples----------
True_STs <- c(1.416667,2.75,2.416667,2.583333,2.166667,2.5,2.5,1.833333,1.25,0.6666667,1,6.583333,6.5,6.666667,2.75,1.666667,1.166667,2.833333,3.583333,6.166667,6.166667,
3.416667,6.083333,1.833333,5.583333,0.75,5.75,5.5,0.5833333,7.666667,5,2.833333,1.333333,5.083333,0.8333333,1.5,4.75,3.416667,4.666667,1.916667,4.666667,7.416667,0.9166667,
1.083333,3.75,3.25,3,2.416667,2.75,2.5,2.666667,4.5,4.416667,1.5,0.8333333,3.166667,3.833333,3.833333,0.4166667,3.333333,2.75,3.083333,0.3333333,0.25,0.6666667,1.833333,
2.333333,3.416667,3.416667,3,0.6666667,0.75,2.166667,1,1.416667,1.333333,1.166667,1.166667,0.4166667,1.25,1.166667,1.083333)
Predicted_STs <- c(6.030591,6.014457,3.545584,5.414229,6.41576,9.393992,5.542331,6.890859,8.090213,4.98545,2.77357,6.275699,9.163978,7.511511,9.531218,7.63715,10.08977,
11.12364,3.982502,5.441881,12.61404,12.21851,17.05850,12.78141,16.22795,21.48544,6.281354,13.83925,8.859929,6.104142,8.255909,2.335526,6.564962,2.335761,9.33772,12.62540,
10.97276,15.63089,8.01967,5.817267,5.59897,4.340784,32.40319,33.74123,27.45024,26.31024,26.88833,24.34707,32.06541,38.90473,17.37102,15.11059,8.772035,14.24816,7.852889,
7.79996,5.601459,2.802408,35.77047,24.34717,30.65796,25.93927,20.64544,22.04807,19.15037,23.83430,1.876557,3.937208,6.526354,5.886377,9.301074,12.4657,14.49783,15.41502,
2.860931,2.541947,4.543111,4.525553,4.148272,3.986912,6.246755,6.89523)
censored <- c(0,1,1,1,1,0,1,1,0,1,0,1,1,1,1,0,0,0,0,1,1,1,1,0,1,0,1,1,0,1,1,0,0,1,0,0,1,0,1,0,1,1,0,0,1,1,1,0,1,1,1,1,1,0,1,1,1,1,0,1,1,1,1,0,0,0,1,1,1,1,0,0,1,0,1,1,1,1,1,1,1,
1)
survivROC(Stime=True_STs,status=censored, marker=Predicted_STs,predict.time=5)
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 2012-2-26 12:08:04
