Find the significant eigenvalues of a matrix.
tw(eigenvalues, eigenL, criticalpoint = 2.0234)
a numeric vector whose elements are the eigenvalues of a matrix. The values should be sorted in the descending order.
the number of eigenvalues.
a numeric value corresponding to the
significance level. If the significance level is 0.05, 0.01,
0.005, or 0.001, the criticalpoint
should be set to be 0.9793
,
2.0234
, 2.4224
, or 3.2724
, accordingly. The default is 2.0234
.
A list with class "htest
" containing the following components:
statistic |
|||
a vector of the Tracy-Widom statistics. | |||
alternative |
|||
a character string describing the alternative hypothesis. | |||
method |
|||
a character string indicating the type of test performed. | |||
data.name |
|||
a character string giving the name of the data. | |||
SigntEigenL |
Lin Wang, Wei Zhang, and Qizhai Li. AssocTests: An R Package for Genetic Association Studies. Journal of Statistical Software. 2020; 94(5): 1-26.
N Patterson, AL Price, and D Reich. Population Structure and Eigenanalysis. PloS Genetics. 2006; 2(12): 2074-2093.
CA Tracy and H Widom. Level-Spacing Distributions and the Airy Kernel. Communications in Mathematical Physics. 1994; 159(1): 151-174.
A Bejan. Tracy-Widom and Painleve II: Computational Aspects and Realisation in S-Plus. In First Workshop of the ERCIM Working Group on Computing and Statistics. 2008, Neuchatel, Switzerland.
A Bejan. Largest eigenvalues and sample covariance matrices. MSc Dissertation, the university of Warwick. 2005. (This function was written by A Bejan and publicly downloadable.)
# NOT RUN {
tw(eigenvalues = c(5, 3, 1, 0), eigenL = 4, criticalpoint = 2.0234)
# }
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