Description Usage Arguments Details Value Note See Also Examples
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.
1 | pvaldistance(x, method = c("ks", "cvm"), dist.to = c("uniform"))
|
x |
a numeric vector containing a data sample. |
method |
a character string indicating which measure of distance is computed. |
dist.to |
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.
See ks.test
for the Kolmogorov-Smirnov test.
1 2 3 4 5 6 7 8 9 10 11 12 13 | # 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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