modelFAMT {FAMT}

The FAMT complete multiple testing procedure

Description

This function implements the whole FAMT procedure (including nbfactors and emfa). The number of factors considered in the model is chosen to reduce the variance of the number of the false discoveries. The model parameters are estimated using an EM algorithm. Factor-adjusted tests statistics are derived, as well as the corresponding p-values.

Usage

modelFAMT(data, x = 1, test = x[1], nbf = NULL, maxnbfactors = 8, 
min.err = 0.001)

Arguments

data

'FAMTdata' object, see as.FAMTdata

x

Column number(s) corresponding to the experimental condition and the optional covariates (1 by default) in the covariates data frame.

test

Column number corresponding to the experimental condition (x[1] by default) one which the test is performed.

nbf

The number of factors of the FA model (NULL by default). If NULL, the function estimates the optimal nbf (see nbfactors)

maxnbfactors

The maximum number of factors (8 by default)

min.err

Stopping criterion value for iterations (default value:0.001)

Value

adjpval

Vector of FAMT factor-adjusted p-values

adjtest

Vector of FAMT factor-adjusted F statistics

adjdata

Factor-adjusted FAMT data

FA

Estimation of the FA model parameters

pval

Vector of classical p-values

x

Column number(s) corresponding to the experimental condition and the optional covariates in the covariates data frame

test

Column number corresponding to the experimental condition on which the test is performed

nbf

The number of factors used to fit the FA model

idcovar

The column number used for the array identification in the 'covariates' data frame

Note

The user can perform individual test statistics putting the number of factors (nbf) equal to zero. The result of this function is a 'FAMTmodel'. It is used as argument in other functions of the package : summaryFAMT, pi0FAMT or defacto. We advise to carry out a summary of FAMT model with the function summaryFAMT.

Author(s)

David Causeur

References

Friguet C., Kloareg M. and Causeur D. (2009). A factor model approach to multiple testing under dependence. Journal of the American Statistical Association, 104:488, p.1406-1415

See Also

as.FAMTdata, raw.pvalues, nbfactors, emfa, summaryFAMT

Examples

## Reading 'FAMTdata'
data(expression)
data(covariates)
data(annotations)

chicken = as.FAMTdata(expression,covariates,annotations,idcovar=2)

# Classical method with modelFAMT 
## Not run: modelpval=modelFAMT(chicken,x=c(3,6),test=6,nbf=0)
## Not run: summaryFAMT(modelpval)

# FAMT complete multiple testing procedure
# when the optimal number of factors is unknown
## Not run: model = modelFAMT(chicken,x=c(3,6),test=6)

# when the optimal number of factors has already been estimated 
 model = modelFAMT(chicken,x=c(3,6),test=6,nbf=3)

summaryFAMT(model)
hist(model$adjpval)
## End(Not run)