Description Usage Arguments Value References See Also Examples
This function computes an approximation of the Variance Component test for a mixture of χ^{2}s using permutations. This is preferable to the asymptotic approximation for small sample sizes. We rely on exact pvalues following Phipson and Smyth, 2010 (see References).
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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 
n_perm 
the number of perturbations. Default is 
progressbar 
logical indicating whether a progress bar should be displayed when computing permutations (only in interactive mode). 
parallel_comp 
a logical flag indicating whether parallel computation
should be enabled. Only Linux and MacOS are supported, this is ignored on Windows.
Default is 
nb_cores 
an integer indicating the number of cores to be used when

genewise_pvals 
a logical flag indicating whether genewise pvalues 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 pvalue is computed :
set_score_obs
: the approximation of the observed set score
set_pval
: the associated set pvalue
or a list with the following elements when genewise pvalues are computed:
gene_scores_obs
: vector of approximating the observed genewise scores
gene_pvals
: vector of associated genewise pvalues
ds_fdr
: vector of associated genewise discrete false discovery rates
Phipson B, and Smyth GK (2010). Permutation pvalues should never be zero: calculating exact pvalues when permutations are randomly drawn. Statistical Applications in Genetics and Molecular Biology, Volume 9, Issue 1, Article 39. http://www.statsci.org/smyth/pubs/PermPValuesPreprint.pdf
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25  #rm(list=ls())
set.seed(123)
##generate some fake data
########################
n < 100
r < 12
t < matrix(rep(1:3), 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
permTestRes < vc_test_perm(y, x, phi=t, w=matrix(1, ncol=ncol(y), nrow=nrow(y)),
indiv=rep(1:4, each=3), n_perm=50, #1000,
parallel_comp = FALSE)
permTestRes$set_pval

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