select.fanc: select from a "fanc" object for fixed value of gamma.

In fanc: Penalized Likelihood Factor Analysis via Nonconvex Penalty

select from a "fanc" object for fixed value of gamma.

Description

This functions give us the loadings from a "fanc" object for fixed value of gamma.

Usage

select(x, criterion=c("BIC","AIC","CAIC","EBIC"), 
	gamma,  scores=FALSE, df.method="active")

Arguments

x	Fitted "fanc" model object.
criterion	The criterion by which to select the tuning parameter rho. One of "AIC", "BIC", "CAIC", or "EBIC". Default is "BIC".
gamma	The value of gamma.
scores	Logical flag for outputting the factor scores. Defalut is FALSE.
df.method	Two types of degrees of freedom are supported. If "active", the degrees of freedom of are the number of nonzero parameters. If "reparametrization", the degrees of freedom of the MC+ are reparametrized based on the degrees of freedom of the lasso.

Value

loadings	factor loadings
uniquenesses	unique variances
Phi	factor correlation
scores	factor scores
df	degrees of freedom (number of non-zero parameters for the lasso estimation)
criteria	values of AIC, BIC and CAIC
goodness.of.fit	values of GFI and AGFI
rho	a value of rho
gamma	a value of gamma

Author(s)

Kei Hirose
[e-mail]

References

Hirose, K. and Yamamoto, M. (2014). Sparse estimation via nonconcave penalized likelihood in a factor analysis model,
Statistics and Computing, in press

See Also

fanc and plot.fanc objects.

fanc documentation built on Nov. 15, 2022, 1:05 a.m.