Description Usage Arguments Value Note Author(s) References Examples
Estimators based on a model selection criteria: Bayes factor, Akaike information or Bayesian information criteria.
1 2 3 4 5 6 7 8 | nIC.est(x, y = NULL, opt = c('BF','AIC','BIC'), param0 = NULL, param = NULL,
logx = TRUE, ...)
nBF_estimator(x, y = NULL, param0 = NULL, param = NULL, logx = TRUE, ...)
nAICc_estimator(x, y = NULL, param0 = NULL, param = NULL, logx = TRUE, ...)
nBIC_estimator(x, y = NULL, param0 = NULL, param = NULL, logx = TRUE, ...)
|
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:
|
param |
Numeric vector, the effect-size of the parameter of interest. If input |
param0 |
Value of the effect-size of the parameter of interest corresponding to the null hypothesis (null value)(i.e. log fold change corresponding to no change, usually 0). If input |
logx |
If |
... |
Further arguments to pass to an internal function. |
A vector of length equal to the total number of features (i.e. proteins, genes,...).
When inputs param
and/or param0
are not given, they are computed internally from matrices x
and y
.
If logx = TRUE
then param
= \bar{x} - \bar{y} and param0
is set to 0, while if logx = FALSE
then param
= \bar{x} / \bar{y} and param0
is set to 1.
Code: Zahra Montazeri, Corey M. Yanofsky, 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 17 18 | #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 xprnSet, xprnSetPair and numeric objects:
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
# examples: ----------
z1 <- nIC.est(x=x,opt='BIC')
z2 <- nIC.est(x=x,opt='BF')
z3 <- nIC.est(x=x,opt='AIC')
z4 <- nIC.est(x=x,y=y,opt='BIC')
z5 <- nIC.est(x=x,y=y,opt='BF')
z6 <- nIC.est(x=x,y=y,opt='AIC')
|
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