Description Usage Arguments Value See Also Examples
This function computes an approximation of the Variance Component test for a
mixture of χ^{2}s using Davies method from
davies
1 |
y |
a numeric matrix of dim |
x |
a numeric design matrix of dim |
indiv |
a vector of length |
phi |
a numeric design matrix of size |
w |
a vector of length |
Sigma_xi |
a matrix of size |
na_rm |
logical: should missing values (including |
A list with the following elements:
score
: approximation of the set observed score
q
: observation-level contributions to the score
q_ext
: pseudo-observations used to compute the covariance,
taking into account the contributions of OLS estimates
gene_scores_unscaled
: a vector of the approximations of the
individual gene scores
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | set.seed(123)
##generate some fake data
########################
n <- 100
r <- 12
t <- matrix(rep(1:3), r/3, ncol=1, nrow=r)
sigma <- 0.4
b0 <- 1
#under the null:
b1 <- 0
#under the alternative:
b1 <- 0.7
y.tilde <- b0 + b1*t + rnorm(r, sd = sigma)
y <- t(matrix(rnorm(n*r, sd = sqrt(sigma*abs(y.tilde))), ncol=n, nrow=r) +
matrix(rep(y.tilde, n), ncol=n, nrow=r))
x <- matrix(1, ncol=1, nrow=r)
#run test
scoreTest <- vc_score(y, x, phi=t, w=matrix(1, ncol=ncol(y), nrow=nrow(y)),
Sigma_xi=matrix(1), indiv=rep(1:(r/3), each=3))
scoreTest$score
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