Calculte -log10(p) by score test (slow, for general cases)
score.cpp(y, Gs, 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 kernel matrices you want to test. For example, Gs = list(A.part = K.A.part, D.part = K.D.part)
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 test statistic \(l1' F l1\) is considered to follow a x chisq(df = 0) + (1 - a) x chisq(df = r). where l1 is the first derivative of the log-likelihood and F is the Fisher information. And r is the degree of freedom. The argument chi0.mixture is a (0 <= a < 1), and default is 0.5.