Exponential empirical likelihood hypothesis testing for two mean vectors | R Documentation |
Exponential empirical likelihood hypothesis testing for two mean vectors.
eel.test2(y1, y2, tol = 1e-07, R = 0, graph = FALSE)
y1 |
A matrix containing the Euclidean data of the first group. |
y2 |
A matrix containing the Euclidean data of the second group. |
tol |
The tolerance level used to terminate the Newton-Raphson algorithm. |
R |
If R is 0, the classical chi-square distribution is used, if R = 1, the corrected chi-square distribution (James, 1954) is used and if R = 2, the modified F distribution (Krishnamoorthy and Yanping, 2006) is used. If R is greater than 3 bootstrap calibration is performed. |
graph |
A boolean variable which is taken into consideration only when bootstrap calibration is performed. IF TRUE the histogram of the bootstrap test statistic values is plotted. |
Exponential empirical likelihood is a non-parametric hypothesis testing procedure for one sample. The generalization to two (or more samples) is via searching for the mean vector that minimises the sum of the two test statistics.
A list including:
test |
The empirical likelihood test statistic value. |
modif.test |
The modified test statistic, either via the chi-square or the F distribution. |
dof |
The degrees of freedom of the chi-square or the F distribution. |
pvalue |
The asymptotic or the bootstrap p-value. |
mu |
The estimated common mean vector. |
runtime |
The runtime of the bootstrap calibration. |
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
Jing Bing-Yi and Andrew TA Wood (1996). Exponential empirical likelihood is not Bartlett correctable. Annals of Statistics 24(1): 365-369.
G.S. James (1954). Tests of Linear Hypothese in Univariate and Multivariate Analysis when the Ratios of the Population Variances are Unknown. Biometrika, 41(1/2): 19-43
Krishnamoorthy K. and Yanping Xia (2006). On Selecting Tests for Equality of Two Normal Mean Vectors. Multivariate Behavioral Research 41(4): 533-548.
Owen A. B. (2001). Empirical likelihood. Chapman and Hall/CRC Press.
Amaral G.J.A., Dryden I.L. and Wood A.T.A. (2007). Pivotal bootstrap methods for k-sample problems in directional statistics and shape analysis. Journal of the American Statistical Association 102(478): 695-707.
Preston S.P. and Wood A.T.A. (2010). Two-Sample Bootstrap Hypothesis Tests for Three-Dimensional Labelled Landmark Data. Scandinavian Journal of Statistics 37(4): 568-587.
Tsagris M., Preston S. and Wood A.T.A. (2017). Nonparametric hypothesis testing for equality of means on the simplex. Journal of Statistical Computation and Simulation, 87(2): 406-422.
el.test2, maovjames, maov, hotel2T2,
james, comp.test
y1 = as.matrix(iris[1:25, 1:4])
y2 = as.matrix(iris[26:50, 1:4])
eel.test2(y1, y2)
eel.test2(y1, y2 )
eel.test2( y1, y2 )
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