bamdit (version 3.4.4)

bsroc: bsroc

Description

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.

Usage

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
)

Arguments

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.

References

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.

See Also

metadiag.

Examples

## execute analysis
if (FALSE) {
# 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)
}