Armitage {GeneticsBase} | R Documentation |
Cochran-Armitage test for linear trends in proportions and frequencies.
Armitage(geneSetObj, method="A") Armitage.default(pedObj, method="A") ArmitageTest(x, mem)
geneSetObj |
a geneSet object |
pedObj |
a pedigree object |
method |
genotype coding method. The default is additive coding (A ). The other two available coding methods are recessive coding (R ) and dominant coding (D ), respectively. |
x |
a vector of bialleleic markers coded by additive, recessive, or dominant model.
Denote B as common allele and A as minor allele.
|
mem |
disease membership. 1 – case; 0 – control |
The functions Armitage
and Armitage.default
return a matrix with nMarkers
rows and 2 columns, where nMarkers
is the number of markers. The two columns are test statistic (stat
) and p-value (pvalue
), respectively.
The function ArmitageTest
returns a list of two elements:
stat |
test statistic |
pvalue |
p-value of the test |
This implementation is based on the documentation at webpage: http://linkage.rockefeller.edu/pawe3d/help/Linear-trend-test-ncp.html.
Gregory Warnes <warnes@bst.rochester.edu> Ross Lazarus <ross.lazarus@channing.harvard.edu> Weiliang Qiu <stwxq@channing.harvard.edu>
Gordon D, Haynes C, Blumenfeld J, Finch SJ (2005) PAWE-3D: visualizing Power for Association With Error in case/control genetic studies of complex traits. Bioinformatics 21:3935-3937.
Gordon D, Finch SJ, Nothnagel M, Ott J (2002) Power and sample size calculations for case-control genetic association tests when errors are present: application to single nucleotide polymorphisms. Hum Hered 54:22-33.
Chapman, D.G. and Nam, J.M. (1968) Asymptotic power of chi square tests for linear trends in proportions. Biometrics. 24, 315-327.
Armitage, P. (1955) Tests for linear trends in proportions and frequencies. Biometrics. 11, 375-386.
Cochran, W.G. (1954) Some methods for strengthening the common chi-squared tests. Biometrics. 10, 417-451.
# not significant result ArmitageTest(x=c(2,1,1,1,0,0,1,0,0,1), mem=c(1,1,1,1,1,0,0,0,0,0)) # significant result ArmitageTest(x=c(2,2,1,1,0,0,0,0,0,0), mem=c(1,1,1,1,1,0,0,0,0,0))