vc_score: Computes variance component test statistic for longitudinal

Description Usage Arguments Value See Also Examples

View source: R/vc_score.R

Description

This function computes an approximation of the Variance Component test for a mixture of χ^{2}s using Davies method from davies

Usage

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vc_score(y, x, indiv, phi, w, Sigma_xi = diag(ncol(phi)), na_rm = FALSE)

Arguments

y

a numeric matrix of dim g x n containing the raw RNA-seq counts for g genes from n samples

x

a numeric design matrix of dim n x p containing the p covariates to be adjusted for

indiv

a vector of length n containing the information for attributing each sample to one of the studied individuals. Coerced to be a factor

phi

a numeric design matrix of size n x K containing the K variables to be tested

w

a vector of length n containing the weights for the n samples.

Sigma_xi

a matrix of size K x K containing the covariance matrix of the K random effects on phi

na_rm

logical: should missing values (including NA and NaN) be omitted from the calculations? Default is FALSE.

Value

A list with the following elements:

See Also

davies

Examples

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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

dearseq documentation built on Nov. 8, 2020, 5:49 p.m.