Description Usage Arguments Value Author(s) References Examples
Other shrinkage estimators.
1 2 3 4 5 6 7 8 9 10 11 12 | other.est(x, y = NULL, opt = c("limma","pseudo","lfdr0","lfdr1"), pval.fun = t.test,
alternative = "greater", arglis.pvalfun = list(), ...)
npseudo.est(x, y = NULL, ...)
nlimma.est(x, y = NULL, ...)
nlfdr0.est(x, y = NULL, pval.fun = t.test, alternative = "greater",
arglis.pvalfun = list(), ...)
nlfdr1.est(x, y = NULL, pval.fun = t.test, alternative = "greater",
arglis.pvalfun = list(), ...)
|
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:
|
pval.fun |
Function to compute p-values from the input data. Usually: "t.test", "wilcox.test", etc. |
alternative |
Argument for input function |
arglis.pvalfun |
Further arguments to pass to input function |
... |
Further arguments to pass to internal an function. |
A vector of length equal to the total number of features (i.e. proteins, genes,...).
Code: Corey M. Yanofsky, Zahra Montazeri, David R. Bickel and Marta Padilla (modifications)
Documentation: Alaa Ali and Marta Padilla
Yanofsky, C. M., & Bickel, D. R. (2010). Validation of differential gene expression algorithms: Application comparing fold-change estimation to hypothesis testing. BMC Bioinformatics, 11, 63.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | #simulate some data sets: matrices of log-abundance levels
nsam<-25 #number of individuals
nfeat<-50 #number of features (metabolites, genes,...)
diffs<-c(1,4) #features with differential log-abundance levels
lfc<-5 #differential quantity
# create data sets:
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
# moderated t-stat estimators: ----------
z1 <- other.est (x=x,y=y,opt="limma")
z2 <- other.est (x=x,y=y,opt="pseudo")
z3 <- other.est (x=x,y=y,opt="lfdr0",pval.fun="t.test")
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