plotDiagnostics.perc {rriskDistributions}

Graphical tools for choosing distribution by given quantiles

Description

Diagnostic plot for choosing a most appropriate continuous probability for known quantiles

Usage

plotDiagnostics.perc(fit.results, tolPlot = 0.1)

Arguments

fit.results

a list containing fitting results as an output of the function rriskFitdist.perc.

tolPlot

numerical value, if the sums of the differences between the distribution percentiles and the given percentiles are smaller than this value, the distribution will be plotted.

Details

This function plots distribution whose percentiles go through the given percentiles q. The argument tolPlot controls this match.

Value

Only graphical output.

Author(s)

Matthias Greiner [e-mail] (BfR),
Kristin Tolksdorf [e-mail] (BfR),
Katharina Schueller [e-mail] (STAT-UP Statistical Consulting),
Natalia Belgorodski [e-mail] (STAT-UP Statistical Consulting)

Examples

p <- c(0.025, 0.5, 0.975)
q <- c(9.68, 29.20, 50.98)
fit.results1 <- rriskFitdist.perc(p = p, q = q, show.output = FALSE, tolConv = 0.5)
old.par <- graphics::par(mfrow = c(1, 2))
plotDiagnostics.perc(fit.results1)
plotDiagnostics.perc(fit.results1, tolPlot = 5)
graphics::par(old.par)

p <- c(0.2, 0.7)
q <- c(2.6, 19.1)
fit.results2 <- rriskFitdist.perc(p = p, q = q, show.output = FALSE)
plotDiagnostics.perc(fit.results2)

p <- c(0.3, 0.8, 0.9)
q <- c(10, 20, 40)
fit.results3 <- rriskFitdist.perc(p = p, q = q, show.output = FALSE)
plotDiagnostics.perc(fit.results3)

p <- c(0.3, 0.8, 0.9)
q <- c(10, 30, 40)
fit.results4 <- rriskFitdist.perc(p = p, q = q, show.output = FALSE)
plotDiagnostics.perc(fit.results4)

## Example with fitted beta pert distribution
p <- c(0.025, 0.5, 0.6, 0.975)
q <- mc2d::qpert(p = p, min = 0, mode = 3, max = 10, shape = 5)
fit.results5 <- rriskFitdist.perc(p = p, q = q, show.output = FALSE)
plotDiagnostics.perc(fit.results5)