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acc.1test()
and acc.paired()
now allow the user (via the argument method.ci
)to choose
from a range of differnt types of confidence intervals.tab.paired()
and tab.1test()
now allow for data where all subjects are either diseased or
nondiseased.Test 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.man/dtcompair-package.rd
was
deleted.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.