neg.normal: Negligible Effect Test for Normality of a Univariate...

In negligible: A Collection of Functions for Negligible Effect/Equivalence Testing

Negligible Effect Test for Normality of a Univariate Distribution

Description

This function allows researchers to test whether the distribution of scores in a distribution has a Shapiro-Wilk W statistic that is negligibly different from 1.

Usage

neg.normal(x, eiL = 0.95, nboot = 1000, plot = TRUE, alpha = 0.05, data = NULL)

## S3 method for class 'neg.normal'
print(x, ...)

Arguments

x	object of class neg.normal
eiL	Lower Bound of the Negligible Effect Interval for W
nboot	Number of Bootstrap Samples for computing the CIs
plot	If the user prefers plots to be generated
alpha	Nominal Type I Error Rate
data	Dataset containing x
...	Extra arguments

Details

#' This function allows researchers to test whether the distribution of scores in a distribution has a Shapiro-Wilk W statistic that is negligibly different from 1. I.e., we are testing the null hypothesis that W is less than or equal to some prespecified lower bound for W (i.e., the least extreme value of W that is non-negligibly different from 1). We recommend .95 and .975 as liberal and conservative bounds, respectively

Value

A list including the following:

• sw Sample Shapiro-Wilk W statistic

• sskew Sample skewness

• skurt Sample kurtosis

• sddiff_mn_mdn Standardized difference between the sample mean and median

• sddiff_mn_trmn Standardized difference between the sample mean and trimmed mean

• lb Lower bound of 1-alpha CI for W

• eiL Maximum W for which the degree of nonnormality is considered extreme

Author(s)

Rob Cribbie [e-mail] and Linda Farmus [e-mail]

Examples

#Normal Distribution
xx<-stats::rnorm(200)
neg.normal(xx)
#Positive Skewed Distribution
xx<-stats::rchisq(200, df=3)
neg.normal(xx)

negligible documentation built on Sept. 11, 2024, 9:24 p.m.