| Title: | Modified Lilliefors Goodness-of-Fit Normality Test |
|---|---|
| Description: | Presentation of a new goodness-of-fit normality test based on the Lilliefors method. For details on this method see: Sulewski (2019) <doi:10.1080/03610918.2019.1664580>. |
| Authors: | Piotr Sulewski [aut, cre]
|
| Maintainer: | Piotr Sulewski <[email protected]> |
| License: | GPL-3 |
| Version: | 0.0.1 |
| Built: | 2024-12-25 06:32:26 UTC |
| Source: | CRAN |
The data set from AbouRizk, S.M., Halpin, D.W., Wilson, J. R. (1994). Fitting beta distributions based on sample data. Journal of Construction Engineering and Management 120(2), 288–305. consist of 72 observations for Dozer Cycle Times
data1data1
A data frame with 72 observations
The data set presents the height of 99 five-year-old British boys in cm downloaded from http://www.mas.ncl.ac.uk/njnsm/medfac/docs/intro.pdf.
data2data2
A data frame with 99 observations
Calculates the p-value of the modified Lilliefors goodness-of-fit normality test.
MLF.pvalue(x)MLF.pvalue(x)
x |
a numeric vector of data values, the number of which must be greater than 4. |
The modified Lilliefors goodness-of-fit p-value.
The function returns the p-value of the modified Lilliefors goodness-of-fit normality test.
Piotr Sulewski, [email protected], Pomeranian University in Slupsk.
Sulewski, P. (2019). Modified Lilliefors Goodness-of-fit Test for Normality. Communications in Statistics - Simulation and Computation 51(3), 1199-1219.
MLF.pvalue(rnorm(33, mean = 0, sd = 2)) MLF.pvalue(data1)MLF.pvalue(rnorm(33, mean = 0, sd = 2)) MLF.pvalue(data1)
Calculates the value of the modified Lilliefors goodness-of-fit normality test statistic.
MLF.stat(x)MLF.stat(x)
x |
a numeric vector of data values, the number of which must be greater than 4. |
The modified Lilliefors goodness-of-fit normality test statistic, see formula (5) in the article.
The function returns the value of the modified Lilliefors goodness-of-fit normality test statistic.
Piotr Sulewski, [email protected], Pomeranian University in Slupsk.
Sulewski, P. (2019). Modified Lilliefors Goodness-of-fit Test for Normality. Communications in Statistics - Simulation and Computation 51(3), 1199-1219.
MLF.stat(rnorm(33, mean = 0, sd = 2)) MLF.stat(data1)MLF.stat(rnorm(33, mean = 0, sd = 2)) MLF.stat(data1)
Performs the modified Lilliefors goodness-of-fit normality test.
MLF.test(x)MLF.test(x)
x |
a numeric vector of data values, the number of which must be greater than 4. |
The modified Lilliefors goodness-of-fit normality test statistic, see formula (5) in the article.
A list with class “htest” containing the following components:
statistic - the value of the modified Lilliefors statistic.
p.value - the p-value for the test.
method - the character string “Modified Lilliefors goodness-of-fit normality test”.
data.name - a character string giving the name(s) of the data.
Piotr Sulewski, [email protected], Pomeranian University in Slupsk.
Sulewski, P. (2019). Modified Lilliefors Goodness-of-fit Test for Normality. Communications in Statistics - Simulation and Computation 51(3), 1199-1219.
MLF.test(rnorm(33, mean = 0, sd = 2)) MLF.test(data1)MLF.test(rnorm(33, mean = 0, sd = 2)) MLF.test(data1)
The PSGoft package puts into practice the modified Lilliefors goodness-of-fit normality test. This modification consists in varying a formula of calculating the empirical distribution function. Values of constants a, b in the formula depend on values of sample skewness and excess kurtosis, which is recommended in order to increase the power of the LF test.