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GFM (version 1.2.1)

Factorm: Factor Analysis Model

Description

Factor analysis to extract latent linear factor and estimate loadings.

Usage

Factorm(X, q=NULL)

Value

return a list with class named fac, including following components:

hH

a n-by-q matrix, the extracted lantent factor matrix.

hB

a p-by-q matrix, the estimated loading matrix.

q

an integer between 1 and p, the number of factor extracted.

sigma2vec

a p-dimensional vector, the estimated variance for each error term in model.

propvar

a positive number between 0 and 1, the explained propotion of cummulative variance by the q factors.

egvalues

a n-dimensional(n<=p) or p-dimensional(p<n) vector, the eigenvalues of sample covariance matrix.

Arguments

X

a n-by-p matrix, the observed data

q

an integer between 1 and p or NULL, default as NULL and automatically choose q by the eigenvalue ratio method.

Author

Liu Wei

References

Fan, J., Xue, L., and Yao, J. (2017). Sufficient forecasting using factor models. Journal of Econometrics.

See Also

gfm.

Examples

Run this code
  dat <- gendata(n = 300, p = 500)
  res <- Factorm(dat$X)
  measurefun(res$hH, dat$H0) # the smallest canonical correlation

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