bdpv (version 1.3)

plotnPV: Plot experimental design for different setting in a single figure.

Description

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

Usage

plotnPV(x, NPVpar = NULL, PPVpar = NULL, legpar = NULL, ...)

Arguments

x

an object of class "nPV" as can be obtained by calling function nPV

NPVpar

a named list which specifies plot parameters for the negative predictive values, possible are lty, lwd, col, pch

PPVpar

a named list which specifies plot parameters for the positive predictive values, possible are lty, lwd, col, pch

legpar

a named list to pass arguments to the legend. See ?legend for the possible arguments.

…

further arguments to be passed to plot

Value

A 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 positive on x and the total sample size on y, combining all parameter settings in one plot. Parameter settings my be distinguished bylty, lwd, col, pch in NPVpar and PPVpar. By default a legend is drawn which can be further modified in legpar.

References

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

See Also

plotnPV2 for a plot with separate subplots for each parameter setting

Examples

# NOT RUN {

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 = 100, alpha = 0.05)

plotnPV(TEST)

# plot parameters maybe introduced via ...
# the legend maybe modified via legpar:

plotnPV(TEST, log="y", legpar=list(x=0.6))

# own colour definitions
plotnPV(TEST, NPVpar=list(col=1:6, lwd=2, lty=1),
 PPVpar=list(col=1:6, lwd=2, lty=3))


# }