# ROCwoGS-package: Non-parametric estimation of ROC curves without Gold Standard In ROCwoGS: Non-parametric estimation of ROC curves without Gold Standard Test

## 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.

## Details

 Package: ROCwoGS Type: Package Version: 1.0 Date: 2010-09-13 License: GPL (>= 2) LazyLoad: no

This package contains one function.NPROCwoGS estimates the ROC Curve of a continuous-scaled diagnostic test with the help of a second imperfect diagnostic test with binary responses

## Author(s)

Chong Wang <[email protected]>

Maintainer: Chong Wang <[email protected]>

## 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.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22``` ```data(score) score\$r <- (score\$r >= 3) ncutoff<- 20 ROC.est<-NPROCwoGS (score, ncutoff, niter=2000, CIlevel=0.95) #Print results on R screen ROC.est #Calculate area under the curve 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 #Youden Index opt.cut<- ROC.est\$cutoff[which.max(ROC.est\$T.Se[1,]+ROC.est\$T.Sp[1,])-1] # Plot ROC curve 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 #".csv" files, saved in the R library path #write.csv(ROC.est\$T.Se, #paste(.Library,"/ROCwoGS/data/T_Se.csv",sep='')) #write.csv(ROC.est\$T.Sp, #paste(.Library,"/ROCwoGS/data/T_Sp.csv",sep='')) ```

ROCwoGS documentation built on May 29, 2017, 9:20 p.m.