Empirical likelihood hypothesis testing for two mean vectors | R Documentation |
Empirical likelihood hypothesis testing for two mean vectors.
el.test2(y1, y2, R = 0, ncores = 1, graph = FALSE)
y1 |
A matrix containing the Euclidean data of the first group. |
y2 |
A matrix containing the Euclidean data of the second group. |
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. |
ncores |
How many to cores to use. |
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. |
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 minimizes 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 |
Thre 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.
G.S. James (1954). Tests of Linear Hypotheses 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.
Owen A.B. (1988). Empirical likelihood ratio confidence intervals for a single functional. Biometrika 75(2): 237-249.
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.
eel.test2, maovjames, maov, hotel2T2, james, comp.test
el.test2( y1 = as.matrix(iris[1:25, 1:4]), y2 = as.matrix(iris[26:50, 1:4]), R = 0 )
el.test2( y1 = as.matrix(iris[1:25, 1:4]), y2 = as.matrix(iris[26:50, 1:4]), R = 1 )
el.test2( y1 =as.matrix(iris[1:25, 1:4]), y2 = as.matrix(iris[26:50, 1:4]), R = 2 )
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