score.cpp: Calculte -log10(p) by score test (slow, for general cases)
In KosukeHamazaki/RAINBOW: Genome-Wide Association Study with SNP-Set Methods

Calculte -log10(p) by score test (slow, for general cases)

1	score.cpp(y, Gs, Gu, Ge, P0, chi0.mixture = 0.5)

`y`	A n \times 1 vector. A vector of phenotypic values should be used. NA is allowed.
`Gs`	A list of kernel matrices you want to test. For example, Gs = list(A.part = K.A.part, D.part = K.D.part)
`Gu`	A n \times n matrix. You should assign ZKZ', where K is covariance (relationship) matrix and Z is its design matrix.
`Ge`	A n \times n matrix. You should assign identity matrix I (diag(n)).
`P0`	A n \times n matrix. The Moore-Penrose generalized inverse of SV0S, where S = X(X'X)^{-1}X' and V0 = σ^2_u Gu + σ^2_e Ge. σ^2_u and σ^2_e are estimators of the null model.
`chi0.mixture`	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.