rsSROC | R Documentation |
Assuming that a weighted Youden index is maximized in all primary studies, the Ruecker-Schumacher approach estimates individual ROC curves and then averages them.
rsSROC(data = NULL, subset=NULL, TP="TP", FN="FN", FP="FP", TN="TN", lambda = "from_bivariate", fpr = NULL, extrapolate = FALSE, plotstudies = FALSE, correction = 0.5, correction.control = "all", add = FALSE, lty = 1, lwd = 1, col = 1, ...)
data |
any object that can be converted to a data frame with integer variables for observed frequencies of true positives, false negatives, false positives and true negatives. The names of the variables are provided by the arguments |
TP |
character or integer: name for vector of integers that is a variable of |
FN |
character or integer: name for vector of integers that is a variable of |
FP |
character or integer: name for vector of integers that is a variable of |
TN |
character or integer: name for vector of integers that is a variable of |
subset |
the rows of |
lambda |
numeric or |
fpr |
Points between 0 and 1 on which to draw the SROC curve. Should be tightly spaced. If set to |
extrapolate |
logical, should the SROC curve be extrapolated beyond the region where false positive rates are observed? |
plotstudies |
logical, should the ROC curves for the individual studies be added to the plot? The plot will become crowded if set to |
correction |
numeric, continuity correction applied if zero cells |
correction.control |
character, if set to |
add |
logical, should the SROC curve be added to an existing plot? |
lty |
line type, see |
lwd |
line width, see |
col |
color of SROC, see |
... |
arguments to be passed on to plotting functions. |
Details are found in the paper of Ruecker and Schumacher (2010).
Besides plotting the SROC, an invisible
list is returned which contains the parameters of the SROC.
Philipp Doebler <philipp.doebler@googlemail.com> Original code kindly supplied by G. Ruecker.
Ruecker G., & Schumacher M. (2010) “Summary ROC curve based on a weighted Youden index for selecting an optimal cutpoint in meta-analysis of diagnostic accuracy.” Statistics in Medicine, 29, 3069–3078.
reitsma-class
, talpha
, SummaryPts
## First Example data(Dementia) ROCellipse(Dementia) rsSROC(Dementia, add = TRUE) # Add the RS-SROC to this plot ## Second Example # Make a crowded plot and look at the coefficients rs_Dementia <- rsSROC(Dementia, col = 3, lwd = 3, lty = 3, plotstudies = TRUE) rs_Dementia$lambda rs_Dementia$aa # intercepts of primary studies on logit ROC space rs_Dementia$bb # slopes
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