Factor Analysis Model

Factor analysis to extract latent linear factor and estimate loadings.

Factorm(X, q=NULL)

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.

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.

References

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

Author

Liu Wei

Note

nothing

See also

gfm.

Examples

  dat <- gendata(n = 300, p = 500)
  res <- Factorm(dat$X)
  measurefun(res$hH, dat$H0) # the smallest canonical correlation
#> Error in ginv(t(H) %*% H): could not find function "ginv"