normfit {LambertW}R Documentation

Graphical and statistical Gaussianity check

Description

Graphical and statistical check if data is Gaussian (4 Normality tests, QQ-plots, histograms, etc).

Usage

normfit(data, plot.it = TRUE, volatility = FALSE)

Arguments

data a numeric vector of data values.
plot.it Should graphical inference be plotted (histogram, densities, qqplot, ...); default TRUE; otherwise only test results are returned.
volatility Should the squared data and its autocorrelation be plotted? Useful for financial time series to see if squares exhibit dependence (typically they do); default: FALSE

Value

A list containing 4 normality tests (each of class htest)

ad Anderson Darling
cvm Cramer-von-Mises
sf Shapiro-Francia
sw Shapiro-Wilk

Author(s)

Georg M. Goerg

References

Thode Jr., H.C. (2002): “Testing for Normality”. Marcel Dekker, New York.

See Also

ad.test, shapiro.test, sf.test,cvm.test

Examples

y=rLambertW(200, theta=c(0.1, 0, 1))
normfit(y)

fit=IGMM(y)
summary(fit)

fitl=MLE_LambertW(y)
plot(fitl)
x=get.input(y, fitl$theta)

normfit(y)
x11()
normfit(x)

[Package LambertW version 0.1.9 Index]