Description Usage Arguments Value Examples
This function generates bootstrap samples using parametric bootstrap method.
1 2 |
BetaMat |
a matrix of estimates of regression coefficients. |
Sigma2Vec |
a vector of shrinkage estimates of error variances. |
RhoVec |
a vector of estimates of correlation. |
WeightMat |
a matrix of weights of all genes obtaining from voom. |
lib.size |
library size in voom method, we choose .75 quantile as library size. |
design |
a design matrix. |
Subject |
a vector of subjects/experimental units. |
Time |
a vector of time points. |
nrep |
simulation iteration. |
a matrix of count data that has nrow(BetaMat) rows and nrow(design) columns.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | data(resSymm)
v <- resSymm$ori.res$v[1:20,]
newlm <- resSymm$ori.res$newlm[1:20,]
BetaMat <- data.matrix(newlm[grep("fixed.", names(newlm))])
Sigma2Vec <- newlm$s2_shrunken
RhoVec <- data.matrix(newlm[grep("rho.", names(newlm))])
WeightMat <- v$weights
lib.size <- v$targets$lib.size
nrep <- 1
Subject <- covset$ear
Time <- covset$time
simcounts <- rmRNAseq:::sc_Symm(BetaMat, Sigma2Vec, RhoVec, WeightMat,
lib.size, design, Subject, Time,nrep)
dim(simcounts)
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