iiPlot | R Documentation |
The function iiPlot
compares the absolute information (AI) of
RMX estimators with AI of ML estimators.
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, ...)
x |
object of S3 class |
param.digits |
number of digits used for the estimated parameter values, if
default |
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 |
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. |
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.
Invisible object of class ggplot
.
Matthias Kohl Matthias.Kohl@stamats.de
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.
rmx
, optIF
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")))
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