This function performs the Jarque-Bera tests of normality either the robust or the classical way.
JarqueBeraTest(x, robust = TRUE, method = c("chisq", "mc"),
N = 0, na.rm = FALSE)a numeric vector of data values.
defines, whether the robust version should be used.
Default is TRUE.
a character string out of chisq or mc, specifying how the critical
values should be obtained. Default is approximated by the
chisq-distribution or empirically via Monte Carlo.
number of Monte Carlo simulations for the empirical critical values
defines if NAs should be omitted. Default is FALSE.
A list with class htest containing the following components:
the value of the test statistic.
the degrees of freedom.
the p-value of the test.
type of test was performed.
a character string giving the name of the data.
The test is based on a joint statistic using skewness and kurtosis
coefficients. The robust Jarque-Bera (RJB) version of utilizes
the robust standard deviation (namely the mean absolute deviation
from the median, as provided e. g. by MeanAD(x, FUN=median)) to estimate sample kurtosis and skewness. For more details see Gel and Gastwirth (2006).
Setting robust to FALSE will perform the original Jarque-Bera test (see
Jarque, C. and Bera, A (1980)).
Gastwirth, J. L.(1982) Statistical Properties of A Measure of Tax Assessment Uniformity, Journal of Statistical Planning and Inference 6, 1-12.
Gel, Y. R. and Gastwirth, J. L. (2008) A robust modification of the Jarque-Bera test of normality, Economics Letters 99, 30-32.
Jarque, C. and Bera, A. (1980) Efficient tests for normality, homoscedasticity and serial independence of regression residuals, Economics Letters 6, 255-259.
Alternative tests for normality as
shapiro.test,
AndersonDarlingTest, CramerVonMisesTest, LillieTest, PearsonTest, ShapiroFranciaTest
qqnorm, qqline for producing a normal quantile-quantile plot
# NOT RUN {
x <- rnorm(100) # null hypothesis
JarqueBeraTest(x)
x <- runif(100) # alternative hypothesis
JarqueBeraTest(x, robust=TRUE)
# }
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