neg.normal {negligible} | R Documentation |
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 |
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 cribbie@yorku.ca and Linda Farmus lifarm@yorku.ca
Examples
#Normal Distribution
xx<-stats::rnorm(200)
neg.normal(xx)
#Positive Skewed Distribution
xx<-stats::rchisq(200, df=3)
neg.normal(xx)