normfit {LambertW} | R Documentation |
Graphical and statistical check if data is Gaussian (4 Normality tests, QQ-plots, histograms, etc).
normfit(data, plot.it = TRUE, volatility = FALSE)
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 |
A list containing 4 normality tests (each of class htest
)
ad |
Anderson Darling |
cvm |
Cramer-von-Mises |
sf |
Shapiro-Francia |
sw |
Shapiro-Wilk |
Georg M. Goerg
Thode Jr., H.C. (2002): “Testing for Normality”. Marcel Dekker, New York.
ad.test
, shapiro.test
, sf.test
,cvm.test
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)