CVMgof2 {Dogoftest} | R Documentation |
One-Sample Cramér–von Mises Goodness-of-Fit Test
Description
Performs the one-sample Cramér–von Mises goodness-of-fit (GoF) test to assess whether a sample comes from a specified distribution using asymptotic p-value approximations.
Usage
CVMgof2(
x,
dist = c("norm", "exp", "unif", "lnorm", "weibull", "gamma", "t", "chisq"),
...,
eps = 1e-15
)
Arguments
x |
A numeric vector of observations. |
dist |
A character string specifying the theoretical distribution. Must be one of
|
... |
Distribution parameters passed to the corresponding |
eps |
A small value to truncate extreme p-values (default is |
Details
The test uses the Cramér–von Mises statistic to assess how well the empirical distribution function (EDF) of the sample agrees with the cumulative distribution function (CDF) of the specified theoretical distribution. The p-value is computed using approximation formulas derived from the asymptotic distribution of the test statistic.
Value
An object of class "htest"
with the following components:
- statistic
The computed Cramér–von Mises test statistic.
- p.value
The asymptotic p-value.
- method
A description of the test and distribution.
- data.name
The name of the data vector.
Examples
set.seed(123)
x1 <- rnorm(500, mean = 0, sd = 1)
CVMgof2(x1, dist = "norm", mean = 0, sd = 1)
x2 <- rexp(500, rate = 2)
CVMgof2(x2, dist = "exp", rate = 2)
x3 <- runif(200, min = -1, max = 3)
CVMgof2(x3, dist = "unif", min = -1, max = 3)