vc_test_asym | R Documentation |
This function computes an approximation of the variance component test based on the asymptotic
distribution of a mixture of χ^{2}s using Davies method
from davies
vc_test_asym( y, x, indiv = rep(1, nrow(x)), phi, w, Sigma_xi = diag(ncol(phi)), genewise_pvals = FALSE, homogen_traj = FALSE, na.rm = FALSE )
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 |
genewise_pvals |
a logical flag indicating whether gene-wise p-values should be returned. Default
is |
homogen_traj |
a logical flag indicating whether trajectories should be considered homogeneous.
Default is |
na.rm |
logical: should missing values (including |
A list with the following elements when the set p-value is computed :
set_score_obs
: the approximation of the observed set score
set_pval
: the associated set p-value
or a list with the following elements when gene-wise p-values are computed:
gene_scores_obs
: vector of approximating the observed gene-wise scores
gene_pvals
: vector of associated gene-wise p-values
davies
#rm(list=ls()) set.seed(123) ##generate some fake data ######################## n <- 100 r <- 12 t <- matrix(rep(1:(r/4)), 4, ncol=1, nrow=r) sigma <- 0.4 b0 <- 1 #under the null: b1 <- 0 #under the alternative: #b1 <- 0.5 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 asymTestRes <- vc_test_asym(y, x, phi=cbind(t, t^2), w=matrix(1, ncol=ncol(y), nrow=nrow(y)), Sigma_xi=diag(2), indiv=1:r, genewise_pvals=TRUE) plot(density(asymTestRes$gene_pvals)) quantile(asymTestRes$gene_pvals)
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