ifa.aic: A function to compute the AIC

In ifa: Independent Factor Analysis

Description

A function to compute the Akaike Information Criterion (AIC) for the fitted IFA model, according to the formula -2*log-likelihood + 2*npar, where npar represents the number of parameters.

Usage

ifa.aic(output)

Arguments

output

The fitted IFA model object, a list including the log-likelihood and the number of parameters

Value

It returns a numeric value with the corresponding AIC.

Author(s)

Cinzia Viroli

References

Sakamoto, Y., Ishiguro, M., and Kitagawa G. (1986). Akaike Information Criterion Statistics. D. Reidel Publishing Company.

Viroli, C. (2005). Choosing the number of factors in Independent Factor Analysis model, Metodoloski Zvezki, Advances in Methodology and Statistics, Vol. II, N. 2, 219-229. Available at $www2.stat.unibo.it/viroli$.

See Also

ifa.bic

Examples

data(memory)
fit<-ifa.em(memory$x,c(2,2),it=50,eps=0.001)
ifa.aic(fit)

ifa documentation built on May 2, 2019, 1:07 p.m.