iiPlot: Compare Absolute Information of Estimators

View source: R/iiPlotRmx.R

iiPlotR Documentation

Compare Absolute Information of Estimators

Description

The function iiPlot compares the absolute information (AI) of RMX estimators with AI of ML estimators.

Usage

iiPlot(x, ...)

## S3 method for class 'rmx'
iiPlot(x, param.digits = 2, ggplot.ylim = NULL,
        ggplot.xlab = "Absolute Information of RMX", 
        ggplot.ylab = "Absolute Information of ML",
        ggplot.ggtitle = NULL, color.line = "#E18727",
        point.col = "#0072B5", point.alpha = 0.4, ...)

Arguments

x

object of S3 class rmx.

param.digits

number of digits used for the estimated parameter values, if default ggplot.ggtitle is used.

ggplot.ylim

numeric vector of length two defining the limits of the y-axis.

ggplot.xlab

label(s) of x-axis, recycled if length is equal to 1 and more than 1 parameter has been estimated.

ggplot.ylab

label(s) of y-axis, recycled if length is equal to 1 and more than 1 parameter has been estimated.

ggplot.ggtitle

if NULL a default plot title is. If it is specified, it should have length 1.

color.line

single color used for colouring the line that represents the maximum absolute information in case of RMX.

point.col

single color used for colouring the data points.

point.alpha

alpha used for color shading.

...

further arguments passed through.

Details

The function is inspired by the plot-methods and function PlotIC of the RobASt-family of packages.

In case of optimally-robust RMX estimators computed with function rmx (S3 class rmx), the absolute information is plotted versus the absolute information of the ML estimator.

Value

Invisible object of class ggplot.

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de

References

Kohl, M. (2005) Numerical Contributions to the Asymptotic Theory of Robustness. Bayreuth: Dissertation.

Rieder, H. (1994) Robust Asymptotic Statistics. New York: Springer.

Rieder, H., Kohl, M. and Ruckdeschel, P. (2008) The Costs of not Knowing the Radius. Statistical Methods and Applications 17(1) 13-40. Extended version: http://r-kurs.de/RRlong.pdf

M. Kohl, P. Ruckdeschel, and H. Rieder (2010). Infinitesimally Robust Estimation in General Smoothly Parametrized Models. Statistical Methods and Application, 19(3):333-354.

See Also

rmx, optIF

Examples

ind <- rbinom(50, size=1, prob=0.05) 
x <- rnorm(50, mean=0, sd=(1-ind) + ind*2)
res <- rmx(x, eps.lower = 0.01, eps.upper = 0.1)
gg <- iiPlot(res)
gg
gg$data

## plot-method
plot(res, which = 7)

## setting and passing argument
iiPlot(res, ggplot.ylim = c(0.5, 10), point.col = "darkred")
plot(res, which = 7, 
     control = list(iiPlot = list(ggplot.ylim = c(0.5, 10), 
                                  point.col = "#0072B5")))

stamats/rmx documentation built on Sept. 29, 2023, 7:13 p.m.