Description Usage Arguments Value Author(s) References Examples
Frequentist Q1 and Q2 estimators.
1 2 3 4 5 |
x |
Input data matrix: features (rows) x samples (columns). See examples. |
y |
Optional input data matrix. |
opt |
Option for selecting the type of estimator, it is a character:
|
h |
Tuning parameters for Q1 estimator. |
a,b |
Tuning parameters for Q2 estimator. |
c |
Tuning parameter for Q1 and Q2 estimator. |
mu0 |
Effect size corresponding to the null hypothesis (i.e. log fold change corresponding to no change, usually 0). |
A vector of length equal to the total number of features (i.e. proteins, genes,...).
Code: Zahra Montazeri, Corey M. Yanofsky, David R. Bickel and Marta Padilla (modifications)
Documentation: Alaa Ali and Marta Padilla
Montazeri, Z., Yanofsky, C. M., & Bickel, D. R. (2010). Shrinkage estimation of effect sizes as an alternative to hypothesis testing followed by estimation in high-dimensional biology: Applications to differential gene expression. Statistical Applications in Genetics and Molecular Biology, 9, 23.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | #simulate some data sets: matrices of log-abundance levels
nsam<-5 #number of individuals
nfeat<-6 #number of features (metabolites, genes,...)
diffs<-c(1,4) #features with differential log-abundance levels
lfc<-5 #differential quantity
# create data matrices; features x samples:
x <- matrix(runif(nfeat*nsam), nrow = nfeat, ncol = nsam) #case
y <- matrix(runif(nfeat*nsam), nrow = nfeat, ncol = nsam) #control
x[diffs,] <- x[diffs,] + lfc
# Q1: ----------
out <- nQs.est(x=x,opt='Q1')
out <- nQ1.est(x=x,y=y,h=0.9)
out <- nQ1.est(x=x,y=y)
out <- nQ1.est(x=x,mu0=0.1,c=0.4)
# Q2: ----------
z1 <- nQs.est(x=x,y=y,opt='Q2',mu0=0.2)
z2 <- nQ2.est(x=x,y=y,c=0.4)
z3 <- nQ2.est(x=x,a=0.4,b=0.02)
z4 <- nQ2.est(x=x)
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