rms_gof: Simulated root-mean-square goodness-of-fit test

In josh-mc/discretefit: Simulated Goodness-of-Fit Tests for Discrete Distributions

Description

The rms_gof() function implements Monte Carlo simulations to calculate p-values based on the root-mean-square statistic for goodness-of-fit tests for discrete distributions.

Usage

rms_gof(x, p, reps = 10000, tolerance = 64 * .Machine$double.eps)

Arguments

x	a numeric vector that contains observed counts for each bin/category.
p	a vector of probabilities of the same length of x. An error is given if any entry of p is negative or if the sum of p does not equal one.
reps	an integer specifying the number of Monte Carlo simulations. The default is set to 10,000 which may be appropriate for exploratory analysis. A higher number of simulation should be selected for more precise results.
tolerance	sets an upper bound for rounding errors when evaluating whether a statistic for a simulation is greater than or equal to the statistic for the observed data. The default is identical to the tolerance set for simulations in the chisq.test function from the stats package in base R.

Value

A list with class "htest" containing the following components:

statistic	the value of the root-mean-square test statistic
p.value	the simulated p-value for the test
method	a character string describing the test
data.name	a character string give the name of the data

Examples

x <- c(15, 36, 17)
p <- c(0.25, 0.5, 0.25)

rms_gof(x, p)

josh-mc/discretefit documentation built on Jan. 28, 2022, 5:39 p.m.