plotnPV2: Plot experimental design for different settings in a set of...

In bdpv: Inference and Design for Predictive Values in Diagnostic Tests

Plot experimental design for different settings in a set of sub figure.

Description

The function creates a plot from the results of the function nPV.

Usage

plotnPV2(x, NPVlty = 1, PPVlty = 3, ...)

Arguments

x	an object of class "nPV" as can be obtained by calling function nPV
NPVlty	single integer value, the linetype for NPV sample size, see par for the options
PPVlty	single integer value, the linetype for PPV sample size, see par for the options
...	further arguments to be passed to plot

Details

Required sample sizes for different experimental settings and prevalences, needed to achieve a prespecified power can be calculated in dependence of the proportion of true negative and true positive compounds in the validation set, using function nPV. This function draws a plot with the proportion of true positives on x and the total sample size on y, combining all parameter settings in one plot.

Note that for huge numbers of setting this should not work.

Value

A plot.

Author(s)

Frank Schaarschmidt

References

Steinberg DM, Fine J, Chappell R (2009). Sample size for positive and negaitive predictiove value in diagnostic research using case-control designs. Biostatoistics 10, 1, 94-105.

See Also

plotnPV, for sample sizes for several settings in one figure

Examples

TEST<-nPV(se=c(0.9, 0.92, 0.94, 0.96, 0.98), sp=c(0.98, 0.96, 0.94, 0.92, 0.90),
 pr=0.12, NPV0=0.98, PPV0=0.4, NPVpower = 0.8, PPVpower = 0.8,
 rangeP = c(0.05, 0.95), nsteps = 20, alpha = 0.05)

plotnPV2(TEST, log="x")

bdpv documentation built on June 8, 2025, 11:50 a.m.