Description Usage Arguments Details Value Author(s) References See Also Examples
Assuming that a weighted Youden index is maximized in all primary studies, the RueckerSchumacher approach estimates individual ROC curves and then averages them.
1 2 3 4 5 6 
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 <[email protected]> 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 metaanalysis of diagnostic accuracy.” Statistics in Medicine, 29, 3069–3078.
reitsmaclass
, talpha
, SummaryPts
1 2 3 4 5 6 7 8 9 10 11 12  ## First Example
data(Dementia)
ROCellipse(Dementia)
rsSROC(Dementia, add = TRUE) # Add the RSSROC 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

Loading required package: mvtnorm
Loading required package: ellipse
Loading required package: mvmeta
This is mvmeta 0.4.7. For an overview type: help('mvmetapackage').
[1] 0.3770685
[1] 8.7649143 5.8190776 2.1014775 9.3366989 3.4783383 3.6483526
[7] 1.5717822 3.2711866 4.6120053 7.9643203 5.7057843 2.9273260
[13] 2.2176739 86.8562992 2.1043268 1.4576141 2.4430091 1.7452135
[19] 5.6100637 1.8658430 2.2204301 2.8826903 2.4285117 4.7890059
[25] 4.7044995 3.1884834 1.6181089 3.8613743 2.7364387 0.1196850
[31] 1.2096786 0.1375928 2.4160947
[1] 4.720177798 1.516753988 0.454079545 3.920202488 1.142589181
[6] 1.920266756 0.064879443 0.680303336 1.532634378 3.064469272
[11] 2.595144286 2.978740968 19.388051202 64.372216706 0.717030992
[16] 0.601713286 0.207802344 0.273179416 1.780216765 0.585180001
[21] 1.380700938 0.815958653 0.774166196 1.531239398 2.421073658
[26] 1.660541133 0.361349823 0.903442735 1.525601885 0.174979018
[31] 0.084350032 0.007550627 0.762123608
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