Examples cvm.test(rnorm(100, mean = 5, sd = 3)) cvm.test(runif(100, min = 2, max = 4)) lillie.test Lilliefors (Kolmogorov-Smirnov) test for normality Description Performs the Lilliefors (Kolmogorov-Smirnov) test for the composite hypothesis of normality, see e.g. Thode (2002, Sec. 5.1.1). Usage lillie.test(x) Arguments, Functions in nortest (1.0-4) Search functions. lillie.test. Lilliefors (Kolmogorov-Smirnov) test for normality. ad.test. Anderson-Darling test for normality. pearson.test. Pearson chi-square test for normality. cvm.test. Cramer-von Mises test for normality. sf.test. Shapiro-Francia test for normality.
Examples sf.test(rnorm(100, mean = 5, sd = 3)) sf.test(runif(100, min = 2, max = 4)) Documentation reproduced from package nortest , version 1.0-4 , License: GPL (>= 2), The nortest package provides five more normality test such as Lilliefors (Kolmogorov-Smirnov) test for normality, Anderson-Darling test for normality, Pearson chi-square test for normality, Cramer-von Mises test for normality, Shapiro-Francia test for normality. > nortest::ad.test(LakeHuron) Anderson-Darling normality test. data: LakeHuron, 4/22/2019 · Example: Anderson-Darling Test in R To conduct an Anderson-Darling Test in R , we can use the ad.test() function within the nortest library. The following code illustrates how to conduct an A-D test to test whether or not a vector of 100 values follows a normal distribution:, Examples ajb.norm.test(rnorm(100)) ajb.norm.test(abs(runif(100,-2,5))) frosini.norm.test Frosini test for normality Description Performs Frosini test for the composite hypothesis of normality, see e.g. Frosini (1987). Usage frosini.norm.test(x, nrepl=2000) Arguments x a numeric vector of data values.
Introduction Regression models are typically “global”. In some cases it can make sense to fit more flexible “local” models. In a spatial context local refers to location.
a list of arguments to be passed to the function estimating the distribution parameters. For example, if test=sw and distribution=gamma, setting est.arg.list=list(method=bcmle) indicates using the bias-corrected maximum-likelihood estimators of shape and scale (see the help file for egamma).
Let’s see an example of the use of ks.test (also available as stats::ks.test), the function in base R . # Sample data from a N(0, 1) n <- 50 set.seed ( 3245678 ) x <- rnorm ( n = n) # Kolmogorov-Smirnov test for H_0: F = N(0, 1).
