rjb.test {lawstat} | R Documentation |
This function performs the robust and classical Jarque-Bera tests of normality.
rjb.test(x, option = c("RJB", "JB"), crit.values = c("chisq.approximation", "empirical"), N = 0)
x |
a numeric vector of data values. |
option |
The choice of the test must be "RJB" (default) or "JB". |
crit.values |
a character string specifying how the critical values should be obtained, i.e. approximated by the chisq-distribution (default) or empirically. |
N |
number of Monte Carlo simulations for the empirical critical values |
The test is based on a joint statistic using skewness and kurtosis coefficients. The Robust Jarque-Bera (RJB) is the robust version of the Jarque-Bera (JB) test of normality. In particular, RJB utilizes the robust standard deviation (namely the Average Absolute Deviation from the Median (MAAD)) to estimate sample kurtosis and skewness (default option). For more details see Gel and Gastwirth (2006). Users can also choose to perform the classical 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. |
Modified from 'jarque.bera.test' (in 'tseries' package).
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.
Jarque, C. and Bera, A. (1980) Efficient tests for normality, homoscedasticity and serial independence of regression residuals., 6 Econometric Letters 255-259.
sj.test
, rqq
, jarque.bera.test
(in tseries package).
## Normally distributed data x = rnorm(100) rjb.test(x) ## Sample Output ## ## Robust Jarque Bera Test ## ## data: x ## X-squared = 0.962, df = 2, p-value = 0.6182 ## Using zuni data data(zuni) rjb.test(zuni[,"Revenue"]) ## Robust Jarque Bera Test ## ## data: zuni[, "Revenue"] ## X-squared = 54595.63, df = 2, p-value < 2.2e-16