summaryFAMT
(Package: FAMT) :
Summary of a FAMTdata or a FAMTmodel
The function produces summaries of 'FAMTdata' or 'FAMTmodel'. The function involves a specific method depending on the class of the main argument.
● Data Source:
CranContrib
● Keywords:
● Alias: summaryFAMT
●
0 images
|
pi0FAMT
(Package: FAMT) :
Estimation of the Proportion of True Null Hypotheses
A function to estimate the proportion pi0 of true null hypotheses from a 'FAMTmodel' (see also function "pval.estimate.eta0" in package "fdrtool").
● Data Source:
CranContrib
● Keywords:
● Alias: pi0FAMT
●
1 images
|
as.FAMTdata
(Package: FAMT) :
Create a 'FAMTdata' object from an expression, covariates and annotations dataset
The function creates a 'FAMTdata' object containing the expression, the covariates and the annotations dataset if provided. The function checks the consistency of dataframes between them. Then missing values of expression can be imputed.
● Data Source:
CranContrib
● Keywords:
● Alias: as.FAMTdata
●
0 images
|
FAMT-package
(Package: FAMT) :
Factor Analysis for Multiple Testing (FAMT) : simultaneous tests under dependence in high-dimensional data
The method proposed in this package takes into account the impact of dependence on multiple testing procedures for high-throughput data as proposed by Friguet et al. (2009). The common information shared by all the variables is modeled by a factor analysis structure. The number of factors considered in the model is chosen to reduce the variance of the number of false discoveries. The model parameters are estimated thanks to an EM algorithm. Factor-adjusted tests statistics are derived, as well as the associated p-values. The proportion of true null hypotheses (an important parameter when controlling the false discovery rate) is also estimated from the FAMT model. Diagnostic plots are proposed to interpret and describe the factors.
● Data Source:
CranContrib
● Keywords: Factor analysis, Multiple testing, False discovery rate, Dependence.
● Alias: FAMT, FAMT-package
●
0 images
|
modelFAMT
(Package: FAMT) :
The FAMT complete multiple testing procedure
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.
● Data Source:
CranContrib
● Keywords:
● Alias: modelFAMT
●
0 images
|
residualsFAMT
(Package: FAMT) :
Calculation of residual under null hypothesis
internal function
● Data Source:
CranContrib
● Keywords:
● Alias: residualsFAMT
●
0 images
|
emfa
(Package: FAMT) :
Factor Analysis model adjustment with the EM algorithm
A function to fit a Factor Analysis model with the EM algorithm.
● Data Source:
CranContrib
● Keywords:
● Alias: emfa
●
0 images
|
defacto
(Package: FAMT) :
FAMT factors description
This function helps the user to describe and interpret the factors using some available external information on either genes or arrays. Diagnostic plots are provided.
● Data Source:
CranContrib
● Keywords:
● Alias: defacto
●
1 images
|
raw.pvalues
(Package: FAMT) :
Calculation of classical multiple testing statistics and p-values
Calculates for each gene expression, the Fisher test statistics and the corresponding p-value for H0: the gene expression does not depend on the experimental condition in a model with possible covariates.
● Data Source:
CranContrib
● Keywords:
● Alias: raw.pvalues
●
1 images
|
nbfactors
(Package: FAMT) :
Estimation of the optimal number of factors of the FA model
The optimal number of factors of the FA model is estimated to minimize the variance of the number of false positives (see Friguet et al., 2009).
● Data Source:
CranContrib
● Keywords:
● Alias: nbfactors
●
0 images
|
providing 
.