normfit {LambertW} | R Documentation |
Performs a check whether data is Gaussian or not. Graphical and statistical inference.
normfit(data, volatility = FALSE, plot.it = TRUE)
data |
data |
volatility |
Should the squared data and its autocorrelation be plotted. Useful for financial data to see if squares exhibit dependence (typically they do for asset returns) |
plot.it |
Should graphical inference be plotted (histogram, densities, qqplot, ...); default TRUE |
Three results of normality tests are returned (each of class htest
)
ad |
Anderson Darling |
sw |
Shapiro-Wilk |
sf |
Shapiro-Francia |
Georg M. Goerg
Goerg, G.M. (2009). “Lambert W Random Variables - A new class of skewed distribution functions”. Unpublished
Thode Jr., H.C. (2002): “Testing for Normality”. Marcel Dekker, New York.
y=rLambertW(200, theta=c(0.1, 0, 1)) normfit(y) fit=IGMM(y) summary(fit) fitl=MLE_LambertW(y) plot(fitl) x.d=get.input(y, fitl$theta)$x normfit(y) x11() normfit(x.d)