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 effectsize of the parameter of interest. If input 
param0 
Value of the effectsize 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 foldchange 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 logabundance levels
nsam<5 #number of individuals
nfeat<6 #number of features (metabolites, genes,...)
diffs<c(1,4) #features with differential logabundance 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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