Dogoftest (version 0.3)

Wgof: Watson goodness-of-fit test Performs the Watson test for goodness-of-fit to a specified distribution.

Description

Watson goodness-of-fit test Performs the Watson test for goodness-of-fit to a specified distribution.

Usage

Wgof(x, dist = c("norm", "exp", "unif", "lnorm", "gamma"), ..., eps = 1e-15)

Value

An object of class "htest" containing the test statistic, p-value, method description, data name, and any distribution parameters used.

Arguments

x

Numeric vector of observations.

dist

Character string specifying the distribution to test against. One of "norm", "exp", "unif", "lnorm", or "gamma".

...

Additional parameters passed to the distribution's cumulative distribution function (CDF). For example, mean and sd for the normal distribution.

eps

Numeric tolerance for probability bounds to avoid extremes (default: 1e-15).

Details

The Watson test is a modification of the Cramér–von Mises test, adjusting for mean deviations. It measures the squared distance between the empirical distribution function of the data and the specified theoretical cumulative distribution function, with a correction for location.

Examples

set.seed(123)
x_norm <- rnorm(1000, mean = 5, sd = 2)
Wgof(x_norm, dist = "norm", mean = 5, sd = 2)

x_exp <- rexp(500, rate = 0.5)
Wgof(x_exp, dist = "exp", rate = 0.5)

x_unif <- runif(300, min = 0, max = 10)
Wgof(x_unif, dist = "unif", min = 0, max = 10)

x_lnorm <- rlnorm(200, meanlog = 0, sdlog = 1)
Wgof(x_lnorm, dist = "lnorm", meanlog = 0, sdlog = 1)

x_gamma <- rgamma(400, shape = 1, rate = 1)
Wgof(x_gamma, dist = "gamma", shape = 1, rate = 1)