Calculte -log10(p) by score test (fast, for limited cases)
score.linker.cpp(
y,
Ws,
Gammas,
gammas.diag = TRUE,
Gu,
Ge,
P0,
chi0.mixture = 0.5
)-log10(p) calculated by score test
A \(n \times 1\) vector. A vector of phenotypic values should be used. NA is allowed.
A list of low rank matrices (ZW; \(n \times k\) matrix). This forms linear kernel \(ZKZ' = ZW \Gamma (ZW)'\). For example, Ws = list(A.part = ZW.A, D.part = ZW.D)
A list of matrices for weighting SNPs (Gamma; \(k \times k\) matrix). This forms linear kernel \(ZKZ' = ZW \Gamma (ZW)'\). For example, if there is no weighting, Gammas = lapply(Ws, function(x) diag(ncol(x)))
If each Gamma is the diagonal matrix, please set this argument TRUE. The calculation time can be saved.
A \(n \times n\) matrix. You should assign \(ZKZ'\), where K is covariance (relationship) matrix and Z is its design matrix.
A \(n \times n\) matrix. You should assign identity matrix I (diag(n)).
A \(n \times n\) matrix. The Moore-Penrose generalized inverse of \(SV0S\), where \(S = X(X'X)^{-1}X'\) and \(V0 = \sigma^2_u Gu + \sigma^2_e Ge\). \(\sigma^2_u\) and \(\sigma^2_e\) are estimators of the null model.
RAINBOW assumes the statistic \(l1' F l1\) follows the mixture of \(\chi^2_0\) and \(\chi^2_r\), where l1 is the first derivative of the log-likelihood and F is the Fisher information. And r is the degree of freedom. chi0.mixture determins the proportion of \(\chi^2_0\)