aiPlot: Plot Absolute Information of Estimators

In stamats/rmx: Radius-Minimax Estimators

Plot Absolute Information of Estimators

Description

The function aiPlot plots the absolute information (AI) of estimators.

Usage

aiPlot(x, ...)

## S3 method for class 'rmx'
aiPlot(x, range.alpha = 1e-6, range.n = 501, 
        param.digits = 2, ggplot.xlab = "x", 
        ggplot.ylab = expression(paste(abs(IF(x))^2)),
        ggplot.ggtitle = NULL,
        point.col = "#0072B5", point.alpha = 0.4, ...)

Arguments

x	object of S3 class rmx.
range.alpha	alpha-quantile used to compute plot range, which is from range.alpha-quantile to 1-range.alpha-quantile of the fitted model.
range.n	number of points in the alpha-quantile range, generated by function seq.
param.digits	number of digits used for the estimated parameter values, if default ggplot.ggtitle is used.
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.
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 which corresponds to the squared length of the influence function; see Kohl (2005).

Value

Object of class ggplot.

Author(s)

Matthias Kohl [e-mail]

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(100, size=1, prob=0.05) 
x <- rnorm(100, mean=ind*3, sd=(1-ind) + ind*9)
res <- rmx(x, eps.lower = 0.01, eps.upper = 0.1)
aiPlot(res)

## plot-method
plot(res, which = 5)

## setting and passing argument
aiPlot(res, ggplot.xlab = "data", ggplot.ylab = "absolute information")
plot(res, which = 5, 
     control = list(aiPlot = list(ggplot.xlab = "data", 
                                  ggplot.ylab = "influence")))

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