This function provides a framework to evaluate various measures of distance between an empirical distribution (induced by the dataset provided) and a theoretical probability distribution.
a numeric vector containing a data sample.
a character string indicating which measure of distance is computed.
a character string determining the (theoretical) probability distribution that is used as a reference.
method = "ks" gives the Kolmogorov-Smirnov distance.
method = "cvm" yields the Cramér-von-Mises criterion (scaled with the sample size).
A positive real number giving the distance measure.
At the moment,
dist.to = "uniform" (the uniform distribution on the unit interval) is the
only valid option for the theoretical distribution, and hence the members of
x have to lie in
the unit interval.
ks.test for the Kolmogorov-Smirnov test.
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# A sample from the standard uniform distribution x <- runif(100, 0, 1) # Distance to uniformity should be small pvaldistance(x, "ks") pvaldistance(x, "cvm") # A sample from the Beta(2, 7) distribution y <- rbeta(100, 2, 7) # Distance to uniformity should be much larger here pvaldistance(y, "ks") pvaldistance(y, "cvm")
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