JarqueBeraTest {DescTools} | R Documentation |
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)
x |
a numeric vector of data values. |
robust |
defines, whether the robust version should be used.
Default is |
method |
a character string out of |
N |
number of Monte Carlo simulations for the empirical critical values |
na.rm |
defines if |
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)).
A list with class htest
containing the following components:
statistic |
the value of the test statistic. |
parameter |
the degrees of freedom. |
p.value |
the p-value of the test. |
method |
type of test was performed. |
data.name |
a character string giving the name of the data. |
This function is melted from the jarque.bera.test
(in tseries
package) and the rjb.test
from the package lawstat
.
W. Wallace Hui, Yulia R. Gel, Joseph L. Gastwirth, Weiwen Miao
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
x <- rnorm(100) # null hypothesis
JarqueBeraTest(x)
x <- runif(100) # alternative hypothesis
JarqueBeraTest(x, robust=TRUE)