ppcc.test: Test for Normality

In USGS-R/smwrStats: R functions to support statistical methods in water resources

Test for Normality

Description

Computes the probability plot correlation coefficient test for departures from normality.

Usage

ppcc.test(x)

Arguments

x	a vector of numeric values. Missing values are allowed, but are ignored in the calculation.

Value

An object of class "htest" having the following components:

statistic	the value of the test statistic.
p.value	the attained p-value for the test.
data.name	a character string describing the name of the data used in the test.
method	a description of the method.

Note

The PPCC test is attractive because it has a simple, graphical interpretation: it is a measure of the correlation in a Q-normal plot of the data. As such, it is related to the Shapiro-Wilk test (Shapiro and Wilk, 1965) for normality.

The distribution function of the test statistic is empirical. This application uses the "pocket calculator" approximation for computing the p-value of the observed statistic (Royston, 1992).

References

Filliben, 1975, The PPCC test for normality: Technometrics, v. 17, no. 1, p. 111–117.

Looney, S.W., and Gulledge, T.R., 1985, Use of the correlation coefficient with normal probability plots: The American Statistician, v. 39, p. 75–79.

Royston, J.P., 1992, A pocket-calculator algorithm for the Shapiro-Francia test of non-normality–an application to medicine: Statistics in Medicine, v. 12, p. 181–184.

Shapiro, S.S., and Wilk, M.B., 1965, An analysis of variance test for normality (complete samples): Biometrika, v. 52, p. 591–611.

See Also

shapiro.test

Examples

## These data should produce an attained p-value less than 0.001
set.seed(45)
ppcc.test.data <- rnorm(32)
qqnorm(ppcc.test.data)
abline(mean(ppcc.test.data), sd(ppcc.test.data))
ppcc.test(ppcc.test.data)

USGS-R/smwrStats documentation built on Oct. 11, 2022, 6:15 a.m.