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Last data update: 2014.03.03

R: Factor Analysis model adjustment with the EM algorithm

emfa	R Documentation

Factor Analysis model adjustment with the EM algorithm

Description

A function to fit a Factor Analysis model with the EM algorithm.

Usage

emfa(data, nbf, x = 1, test = x[1], pvalues = NULL, min.err = 0.001)

Arguments

`data`	'FAMTdata' object, see `as.FAMTdata`
`nbf`	Number of factors of the FA model, see `nbfactors`
`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) on which the test is performed.
`pvalues`	p-values of the individual tests. If NULL, the classical procedure is applied (see `raw.pvalues`)
`min.err`	Stopping criterion value for iterations in EM algorithm (default value: 0.001)

Details

In order to use this function, the number of factors is needed (otherwise, use nbfactors).

Value

`B`	Estimation of the loadings
`Psi`	Estimation of Psi
`Factors`	Scores of the individuals on the factors
`commonvar`	Proportion of genes common variance (modeled on the factors)
`SelectHo`	Vector of row numbers corresponding to the non-significant genes

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

Examples

## Reading 'FAMTdata'
data(expression)
data(covariates)
data(annotations)
chicken = as.FAMTdata(expression,covariates,annotations,idcovar=2)

# EM fitting of the Factor Analysis model
chicken.emfa = emfa(chicken,nbf=3,x=c(3,6),test=6)
chicken.emfa$commonvar

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(FAMT)
Loading required package: mnormt
Loading required package: impute
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/FAMT/emfa.Rd_%03d_medium.png", width=480, height=480)
> ### Name: emfa
> ### Title: Factor Analysis model adjustment with the EM algorithm
> ### Aliases: emfa
> 
> ### ** Examples
> 
> ## Reading 'FAMTdata'
> data(expression)
> data(covariates)
> data(annotations)
> chicken = as.FAMTdata(expression,covariates,annotations,idcovar=2)
$`Rows with missing values`
integer(0)

$`Columns with missing values`
integer(0)

> 
> # EM fitting of the Factor Analysis model
> chicken.emfa = emfa(chicken,nbf=3,x=c(3,6),test=6)
[1] "Fitting Factor Analysis Model with 3 factors"
> chicken.emfa$commonvar
[1] 0.3919842
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>