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 CramÃ©r-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")
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.