score.linker.cpp {RAINBOWR}

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

Description

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

Usage

score.linker.cpp(
  y,
  Ws,
  Gammas,
  gammas.diag = TRUE,
  Gu,
  Ge,
  P0,
  chi0.mixture = 0.5
)

Arguments

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

Value

-log10(p) calculated by score test