bsroc | R Documentation |
This function plots the observed data in the ROC (Receiving Operating Charachteristics) space with the Bayesian SROC (Summary ROC) curve. The predictive curves are approximated using a parametric model.
bsroc( m, level = c(0.05, 0.5, 0.95), title = "Bayesian SROC Curve", fpr.x = seq(0.01, 0.95, 0.01), partial.AUC = TRUE, xlim.bsroc = c(0, 1), ylim.bsroc = c(0, 1), lower.auc = 0, upper.auc = 0.95, col.fill.points = "blue", results.bauc = TRUE, results.bsroc = FALSE, plot.post.bauc = FALSE, bins = 30, scale.size.area = 10 )
m |
The object generated by metadiag. |
level |
Credibility levels of the predictive curve |
title |
Optional parameter for setting a title in the plot. |
fpr.x |
Grid of values where the conditionl distribution is calculated. |
partial.AUC |
Automatically calculate the AUC for the observed range of FPRs, default is TRUE. |
xlim.bsroc |
Graphical limits of the x-axis for the BSROC curve plot. |
ylim.bsroc |
Graphical limits of the y-axis for the BSROC curve plot. |
lower.auc |
Lower limit of the AUC. |
upper.auc |
Upper limit of the AUC. |
col.fill.points |
Color used to fill points, default is blue. |
results.bauc |
Print results of the Bayesian Area Under the Curve, default value is TRUE. |
results.bsroc |
Print results of the Bayesian SROC curve, default value is FALSE. |
plot.post.bauc |
The BSROC and the posterior of the BAUC are ploted in the same page, default is FALSE. |
bins |
Histograms' bins. |
scale.size.area |
Scale area for the ploted points, default = 10. |
Verde P. E. (2010). Meta-analysis of diagnostic test data: A bivariate Bayesian modeling approach. Statistics in Medicine. 29(30):3088-102. doi: 10.1002/sim.4055.
Verde P. E. (2018). bamdit: An R Package for Bayesian Meta-Analysis of Diagnostic Test Data. Journal of Statisticsl Software. Volume 86, issue 10, pages 1–32.
metadiag
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## execute analysis ## Not run: # Example: data from Glas et al. (2003)..................................... data(glas) glas.t <- glas[glas$marker == "Telomerase", 1:4] glas.m1 <- metadiag(glas.t, re = "normal", link = "logit") bsroc(glas.m1) bsroc(glas.m1, plot.post.bauc = TRUE) # Example: data from Scheidler et al. (1997) # In this example the range of the observed FPR is less than 20%. # Calculating the BSROC curve makes no sense! You will get a warning message! data(mri) mri.m <- metadiag(mri) bsroc(mri.m) ## End(Not run)
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