Probability Plots Using Maximum Likelihood EstimatesSource:
Make quantile-quantile plots and probability-probability plots using maximum likelihood estimation.
ppmlplot(y, obj, plot.it = TRUE, datax = FALSE, ...) ppmlline(...) ppmlpoints(y, obj, plot.it = TRUE, datax = TRUE, ...) qqmlplot(y, obj, plot.it = TRUE, datax = FALSE, ...) qqmlline(y, obj, datax = FALSE, probs = c(0.25, 0.75), qtype = 7, ...) qqmlpoints(y, obj, plot.it = TRUE, datax = TRUE, ...)
Numeric vector; The data to plot on the
univariateMLobject or a function that returns a
univariateMLobject when called with
yas its only argument.
Logical; should the result be plotted?
ybe plotted on the
x-axis? Defaults to
Numeric vector of length two, representing probabilities. Corresponding quantile pairs define the line drawn.
typeof quantile computation used in
ppmlpoints, a list with components
x (plotted on the x axis)
y (plotted on the y axis).
qqmlplot produces a quantile-quantile plot (Q-Q plot) of the values in
y with respect to the distribution defined by
obj, which is
univariateML object or a function returning a
univariateML object when called with
qqmlline adds a
line to a “theoretical”, quantile-quantile plot which passes through
probs quantiles, by default the first and third quartiles.
stats::points and adds a Q-Q plot to
an existing plot.
probability-probability plots (or P-P plots). They behave similarly to the
quantile-quantile plot functions.
This function is modeled after qqnorm.
Graphical parameters may be given as arguments to all the functions below.
M. B. Wilk, R. Gnadadesikan, Probability plotting methods for the analysis for the analysis of data, Biometrika, Volume 55, Issue 1, March 1968, Pages 1–17, https://doi.org/10.1093/biomet/55.1.1
## Make a single probability plot with a line. obj <- mlgamma(Nile) qqmlplot(Nile, obj) qqmlline(Nile, obj) ## Make multiple probability plots. datax = TRUE must be used to make this ## look good. ppmlplot(airquality$Wind, mlgamma, main = "Many P-P plots") ppmlpoints(airquality$Wind, mlexp, col = "red") ppmlpoints(airquality$Wind, mlweibull, col = "purple") ppmlpoints(airquality$Wind, mllnorm, col = "blue")