Takes a meta4diag object produced by meta4diag() and makes the standard summary receiver operating chracteristic plot.
SROC(x,...)
# S3 method for meta4diag
SROC(x, sroc.type=1, est.type="mean", sp.cex=1.5,sp.pch=8,sp.col="red",
dataShow="o", data.col="#FF0000", data.cex="scaled", data.pch=1,
lineShow=T, line.lty=1, line.lwd=2, line.col="black",
crShow=T, cr.lty=2, cr.lwd=1.5, cr.col="blue",
prShow=T, pr.lty=3, pr.lwd=1, pr.col="darkgray",
dataFit = T, add=FALSE, main="", xlim, ylim,
legend=F,legend.cex = 0.7,...)A meta4diag object.
A numerical value specifying the function used to make SROC line. Options are 1, 2, 3, 4, 5. When sroc.type=1, the SROC line is plotted as " The regression line 1" according to Arends et al.(2008),
$$y = \mu + \rho\sqrt{\frac{\sigma_{\mu}^2}{\sigma_{\nu}^2}}(x-\nu)$$
When sroc.type=2, the SROC line is plotted as "The major axis method",
$$y = \frac{\sigma_{\mu}^2-\sigma_{\nu}^2\pm\sqrt{(\sigma_{\mu}^2-\sigma_{\nu}^2)^2+4\rho^2\sigma_{\mu}^2\sigma_{\nu}^2}}{2\rho\sqrt{\sigma_{\mu}^2\sigma_{\nu}^2}}(x-\nu)+\mu$$
When sroc.type=3, the SROC line is plotted as "The Moses and Littenberg's regression line",
$$y = \frac{\sigma_{\mu}^2 + \rho\sqrt{\sigma_{\mu}^2\sigma_{\nu}^2}}{\sigma_{\nu}^2+\rho\sqrt{\sigma_{\mu}^2\sigma_{\nu}^2}}(x-\nu)+\mu$$
When sroc.type=4, the SROC line is plotted as "The regression line 2",
$$y = \mu + \frac{1}{\rho}\sqrt(\frac{\sigma_{\mu}^2}{\sigma_{\nu}^2})(x-\nu)$$
When sroc.type=5, the SROC line is plotted as "The Rutter and Gatsonis's SROC curve",
$$y = \mu + \sqrt{\frac{\sigma_{\mu}^2}{\sigma_{\nu}^2}}(x-\nu)$$
The estimates type used to make SROC plot. Options are "mean" and "median".
Summary points size. The summary points are mean or median of sensitivities and specificities of all sudies.
Point symbols of summary points.
Color of summary points.
A character indicating whether the original dataset or the fitted dataset is shown or not. If is "o", the original data will be plotted. If is "f", the fitted data will be plotted. No dataset will be plotted if dataShow is not "o" or "f".
Color of orignal data bubbles.
A string or a numerical value indicating the size of the plotted dataset points. If is "bubble" or "scaled", the size of the data points are proportional to the total number of individuals in each study.
A string or a numerical value indicating the symbol of the plotted dataset points.
Boolean indicating whether the SROC line is shown or not.
SROC line type.
SROC line width.
Color for the SROC line.
Boolean indicating whether the confidence region is shown or not.
Confidence region line width.
Confidence region line width.
Color for the confidence region line.
Boolean indicating whether the prediction region is shown or not.
Prediction region line type.
Prediciton region line width.
Color for the prediction region line.
Boolean indicating the length SROC line. Either plotted from -1 to 1, or fit the data.
If add is TRUE, the plots are added to an existing plot, otherwise a new plot is created.
A overall title for the plot.
A numeric value, giving the x coordinates ranges.
A numeric value, giving the y coordinates ranges.
Boolean indicating whether the legend is shown or not. Can also be a string indicating the position to show the legend. Options are "left", "bottom" and "right".
Lengend size.
Further arguments passed to or from other methods.
The return value is a list of the files created (if any).
The SROC() returns a plot of the true positive rate against the false positive rate (1-specificity) (ROC space) for the different possible cutpoints of a diagnostic test. It shows the tradeoff between sensitivity and specificity (any increase in sensitivity will be accompanied by a decrease in specificity). The closer the curve follows the left-hand border and then the top border of the ROC space, the more accurate the test. The closer the curve comes to the 45-degree diagonal of the ROC space, the less accurate the test. The area under the curve is a measure of text accuracy. Summary points, summary receiver operating characteristic line, confidence region and prediction region of the summary points are plotted.
The function SROC() gives the user flexibility to control the plots.
Chappell F, Raab G, Wardlaw J (2009). "When are Summary ROC Curves Appropriate for Diagnostic Meta-analyses?" Statistics in Medicine, 28(21), 2653-2668.
Arends et al.(2008) "Bivariate random effects meta-analysis of ROC curves." Medical Decision Making, 28(5), 621-638.
meta4diag
# NOT RUN {
if(requireNamespace("INLA", quietly = TRUE)){
require("INLA", quietly = TRUE)
data(Catheter)
res <- meta4diag(data = Catheter)
SROC(res, est.type="mean")
}
# }
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