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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.
Health stats visible at Monitor.