ppPlot | R Documentation |
The function ppPlot
generates a pp-plot for fitted models.
ppPlot(x, ...)
## S3 method for class 'rmx'
ppPlot(x, param.digits = 3,
ggplot.xlab = "Theoretical Cumulative Probabilities",
ggplot.ylab = "Empirical Cumulative Probabilities",
ggplot.ggtitle = NULL,
point.col = "#0072B5", point.alpha = 1, ...)
x |
object of S3 class |
param.digits |
number of digits used for the estimated parameter values, if
default |
ggplot.xlab |
label of x-axis. |
ggplot.ylab |
label of y-axis. |
ggplot.ggtitle |
plot title. If |
point.col |
single color used for colouring the data points. |
point.alpha |
alpha used for color shading. |
... |
further arguments passed through. |
The function is based on functions provided by packages ggplot2 and qqplotr.
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(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)
ppPlot(res)
## plot-method
plot(res, which = 3)
## setting and passing arguments
ppPlot(res, ggplot.xlab = "Cumulative probabilities of estimated model",
point.col = "black")
plot(res, which = 3,
control = list(ppPlot = list(ggplot.xlab = "Cumulative probabilities of estimated model",
point.col = "black")))
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