NPROCwoGS(ROCwoGS)
NPROCwoGS()所属R语言包：ROCwoGS
Non-parametric estimation of ROC curves without Gold Standard
非参数估计的ROC曲线没有黄金标准
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Function to estimate the ROC Curve of a continuous-scaled diagnostic test with the help of a second imperfect diagnostic test with binary responses.
函数来估算一个规模连续诊断测试的ROC曲线与第二不完善的诊断测试的帮助下，用二进制响应。
用法----------Usage----------
NPROCwoGS(score, ncutoff, niter, CIlevel)
参数----------Arguments----------
参数：score
A data frame with the first column of the continuous-scaled diagnostic test scores; the second column of the binary reference scores; the third column of a factor vector to indicate the population that the observations come from.
一个数据框的第一列的连续缩放的诊断测试的分数;二进制基准评分的第二列中;一个因子矢量来表示的人口，观测来自第三列。
参数：ncutoff
Desired number of cutoff values. This determines the number of estimated pairs of (sensitivity, specificity) along the ROC curve.
期望数量的临界值。这就决定了估计对（敏感性，特异性）沿曲线。
参数：niter
Desired number of iterations for the Markov Chain Monte Carlo method.
所需数量的迭代马尔可夫链蒙特卡罗方法。
参数：CIlevel
Desired level of credible intervals. A numeric value between 0 and 1.
可信区间的期望水平。 0和1之间的数值。
Details
详细信息----------Details----------
The method is based on a multinomial model for the joint distribution of test-positive and test-negative observations. The estimated ROC curve of the continuous-scaled test is in the form of pairs of sensitivities and specificities. Also estimated are the sensitivity and specificity of the binary reference test and the population disease prevelances.
该方法是基于在一个多项式模型试验阳性的观察和试验阴性的联合分布。估计ROC曲线的连续缩放的测试是在对形式的敏感性和特异性。也估计的敏感性和特异性的二进制基准测试和人口疾病prevelances的。
值----------Value----------
NPROCwoGS produces an ordinary list with the following components. <table summary="R valueblock"> <tr valign="top"><td>cutoff</td> <td> Cutoff values for the continuous-scaled diagnostic test. The length of cutoff is the same as the input ncutoff</td></tr> <tr valign="top"><td>T.Se</td> <td> Point estimates (first row) and credible intervals (second and third rows) of the sensitivities for the continuous-scaled diagnostic test</td></tr> <tr valign="top"><td>T.Sp</td> <td> Point estimates (first row) and credible intervals (second and third rows) of the specificities for the continuous-scaled diagnostic test</td></tr> <tr valign="top"><td>R.Se</td> <td> Point estimate and the lower bound and upper bound of the credible interval of the sensitivity for the binary reference test</td></tr> <tr valign="top"><td>R.Sp</td> <td> Point estimate and the lower bound and upper bound of the credible interval of the specificity for the binary reference test</td></tr> <tr valign="top"><td>Prev</td> <td> Point estimates (first row) and credible intervals (second and third rows) of the population disease prevelances. Each column represents a population</td></tr> <tr valign="top"><td>flag</td> <td> Flag for potential nonidentifiable configurations. flag=0: no flag is raised, flag=1: Flag 1 is raised; flag=2: Flag 2 is raised; flag=3: both flags are raised</td></tr> </table>
NPROCwoGS产生一个普通的列表与以下组件。 <table summary="R valueblock"> <tr valign="top"> <TD> cutoff</ TD> <TD>临界值的连续缩放的诊断测试。的截止长度是相同的输入ncutoff </ TD> </ TR> <tr valign="top"> <TD>T.Se </ TD> <TD>点估计（第一行）和可信的的敏感性规模连续诊断测试的时间间隔（第二排和第三排）</ TD> </ TR> <tr valign="top"> <TD>T.Sp</ TD> <TD>点估计（第一行）和可信区间（第二排和第三排）的具体规模连续诊断测试</ TD> </ TR> <tr valign="top"> <TD>R.Se</ TD > <TD>点估计的下限和上限的可信区间为二进制的基准测试的敏感性</ TD> </ TR> <tr valign="top"> <TD>R.Sp / TD> <TD>点估计的下限和上限的特殊性的可信区间为二进制的基准测试</ TD> </ TR> <tr valign="top"> <TD>Prev </ TD> <TD>点估计（第一行）的人口疾病prevelances的可信区间（第二排和第三排）。每一列代表一个人口</ TD> </ TR> <tr valign="top"> <TD> flag</ TD> <TD>标志潜在的nonidentifiable配置。标志= 0：没有国旗升起，标志= 1：标志1提高;引发标志= 2：标志2，标志= 3：两个标志都提出了</ TD> </ TR> </ TABLE>
（作者）----------Author(s)----------
Chong Wang <<a href="mailto:chwang@iastate.edu">chwang@iastate.edu</a>>
参考文献----------References----------
Wang, C., Turnbull, B. W., Grohn, Y. T. and Nielsen, S. S. (2007). Nonparametric Estimation of ROC Curves Based on Bayesian Models When the True Disease State Is Unknown. Journal of Agricultural, Biological and Environmental Statistics 12, 128-146.
实例----------Examples----------
data(score)
score$r <- (score$r >= 3)
ncutoff<- 20
ROC.est<-NPROCwoGS (score, ncutoff, niter=2000, CIlevel=0.95)
#Print results on R screen[R屏幕上的打印结果]
ROC.est
#Calculate area under the curve[计算曲线下的面积]
AUC<- sum((ROC.est$T.Se[1,-1]+ROC.est$T.Se[1,-(ncutoff+2)])*(ROC.est$T.Sp[1,-1]-ROC.est$T.Sp[1,-(ncutoff+2)])/2)
#Find the optimal cutoff to maximize[找到最佳的截止最大化]
#Youden Index[Youden指数]
opt.cut<- ROC.est$cutoff[which.max(ROC.est$T.Se[1,]+ROC.est$T.Sp[1,])-1]
# Plot ROC curve[绘制ROC曲线]
plot(1-ROC.est$T.Sp[1,],ROC.est$T.Se[1,],"l", xlab="1-Specificities",ylab="Sensitivities", main=paste("AUC=", format(AUC, digits=4),
", Optimal Cutoff=",opt.cut))
data.frame(1-ROC.est$T.Sp)[c(3,2),]->ci.tsp
data.frame(ROC.est$T.Se)[c(2,3),]->ci.tse
#Write Sensitivities and Specificities to [写的敏感性和特异性，以]
#".csv" files, saved in the R library path[“csv”文件，保存在R库的路径]
#write.csv(ROC.est$T.Se,[write.csv（ROC.est $ T.Se，]
#paste(.Library,"/ROCwoGS/data/T_Se.csv",sep=''))[粘贴（。图书馆，“/ ROCwoGS /数据/ T_Se.csv”，SEP =））]
#write.csv(ROC.est$T.Sp,[write.csv（ROC.est $ T.Sp，]
#paste(.Library,"/ROCwoGS/data/T_Sp.csv",sep=''))[粘贴（。图书馆，“/ ROCwoGS /数据/ T_Sp.csv”，SEP =））]
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 2012-9-27 22:47:43
