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

score.cpp

R Documentation

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

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

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 = \sigma^2_u Gu + \sigma^2_e Ge`. `\sigma^2_u` and `\sigma^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.