Factmle_cov: Calculates the Maximum likelihood Factor analysis with a...
In FACTMLE: Maximum Likelihood Factor Analysis

Description Usage Arguments Value See Also Examples

Calculates the Maximum likelihood Factor analysis with a covariance Matrix.

1 2	Factmle_cov(S, rnk, Psi_init = c(), lb = 0.01, index = c(), lb2 = 0.01, tol = 10^-7, Max_iter = 1000)

`S`	The Covariance Matrix. It is a p*p numeric matrix, where p is the number of variables.
`rnk`	Rank constraint for the Factor analysis problem. It must a positive integer less than the number of variables p
`Psi_init`	The initial value of Psi. It is a p*1 numeric vetor, where p is the number of variables.Default value is a vector of uniform random numbers.
`lb`	The lower bound on the Psi values. The default value is set to 0.05
`index`	This option is for modified version of factmle.The default value is a null vector. If assigned a zero vector, it will perform MLFA keeping some of the Psi values specified by the index at a specifed level lb2
`lb2`	This option of modified version of factmle algorithm. The default value is 0.001. The Psi values specified by the index is kept constant at lb2 while doing MLFA.
`tol`	Precision parameter. Default is 10^-7
`Max_iter`	Maximum number of iterations. Default is 1000.

A list with the following components

Psi: A vector containing the unique variances.
Lambda: A p*rnk matrix containing the factor loadings in the columns.
Nll: A vector containing the negative Log-likelihood values at every iteration.
Nllopt: The value of the negative log-likelihood upon convergence.

eigs_sym

library(MASS)
library(stats)
Psi=runif(15,min=0.2,max=1.3)
Lambda=mvrnorm(n=15,mu=rep(0,3),Sigma = diag(rep(1,3)))
data=mvrnorm(n=5000,mu=rep(0,15),Sigma = diag(Psi)+Lambda%*%t(Lambda))
S=cov(data)
x=Factmle_cov(S,3)