MAX3 statistic
Compute the MAX3 statistic, which tests for three genetic models (additive, recessive and dominant).
snp_MAX3(Gna, y01.train, ind.train = rows_along(Gna), val = c(0, 0.5, 1))
Gna |
A FBM.code256
(typically |
y01.train |
Vector of responses, corresponding to |
ind.train |
An optional vector of the row indices that are used, for the training part. If not specified, all rows are used. Don't use negative indices. |
val |
Computing \smash{\displaystyle\max_{x \in val}}~Z_{CATT}^2(x).
|
P-values associated with returned scores are in fact the minimum of the p-values of each test separately. Thus, they are biased downward.
An object of classes mhtest and data.frame returning one
score by SNP. See methods(class = "mhtest").
Zheng, G., Yang, Y., Zhu, X., & Elston, R. (2012). Robust Procedures. Analysis Of Genetic Association Studies, 151-206. doi: 10.1007/978-1-4614-2245-7_6.
set.seed(1) # constructing a fake genotype big.matrix N <- 50; M <- 1200 fake <- snp_fake(N, M) G <- fake$genotypes G[] <- sample(as.raw(0:3), size = length(G), replace = TRUE) G[1:8, 1:10] # Specify case/control phenotypes fake$fam$affection <- rep(1:2, each = N / 2) # Get MAX3 statistics y01 <- fake$fam$affection - 1 str(test <- snp_MAX3(fake$genotypes, y01.train = y01)) # p-values are not well calibrated snp_qq(test) # genomic control is not of much help snp_qq(snp_gc(test)) # Armitage trend test (well calibrated because only one test) test2 <- snp_MAX3(fake$genotypes, y01.train = y01, val = 0.5) snp_qq(test2)
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