emfa | R Documentation |
A function to fit a Factor Analysis model with the EM algorithm.
emfa(data, nbf, x = 1, test = x[1], pvalues = NULL, min.err = 0.001)
data |
'FAMTdata' object, see |
nbf |
Number of factors of the FA model, see |
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 |
min.err |
Stopping criterion value for iterations in EM algorithm (default value: 0.001) |
In order to use this function, the number of factors is needed (otherwise, use nbfactors
).
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 |
David Causeur
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
as.FAMTdata
, nbfactors
## 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
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