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

score.linker.cpp

R Documentation

Calculte -log10(p) by score test (fast, for limited cases)

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
)

`y`	A `n \times 1` vector. A vector of phenotypic values should be used. NA is allowed.
`Ws`	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)
`Gammas`	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)))
`gammas.diag`	If each Gamma is the diagonal matrix, please set this argument TRUE. The calculation time can be saved.
`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 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`