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tab.paired
and tab.1test
now allow for data where all subjects are either diseased or nondiseasedTest 1
is consistently used as the reference test.pv.gs
and pv.wgs
, it is now diff.ppv <- ppv.2-ppv.1
(instead of diff.ppv <- abs(ppv.1-ppv.2)
), and accordingly for negative predictive values.pv.prev
) to allow computation of positive and negative predictive values for different theoretical prevalences.sesp.gen.mcnemar
) implementing a generalized McNemar’s test for a joint comparison of sensitivity and specificity.pv.rpv()
now returns the full variance-covariance matrix (Sigma
).ellipse.pv.rpv()
generates the data to plot a joint confidence region for rPPV and rNPV (depends on the ellipse
package) (as in Moskowitz and Pepe, 2006).sesp.rel()
calculates relative sensitivity and relative specificity (with Wald CIs and p-value).tpffpf.rel()
calculates relative sensitivity (rTPF) and relative ‘one minus specificity’ (rFPF) (with Wald CIs and p-value), but it does not calculate their individual components (ie, TPFs and FPFs); this function is meant to be used with paired screen-positive designs, where only rTPF and rFPF are estimable form the data (see Cheng and Macaluso, 1997 or Alonzo, Pepe, Moskowitz, 2002).sesp.diff.ci
(detected by F. Gimenez - many thanks!).sesp.exactbinom
(detected by J. Swiecicki - many thanks!).These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.