Calculate several information or model selection criteria (MSC) such as the Akaike information criterion (AIC), the Bayesian information criterion (BIC) or the Hannan-Quinn criterion (HQ).
calculateModelSelectionCriteria( .object = NULL, .ms_criterion = c("all", "aic", "aicc", "aicu", "bic", "fpe", "gm", "hq", "hqc", "mallows_cp"), .by_equation = TRUE, .only_structural = TRUE )
Character string. Either a single character string or a vector
of character strings naming the model selection criterion to compute.
Should the criteria be computed for each structural model
equation separately? Defaults to
Should the the log-likelihood be based on the
structural model? Ignored if
.by_equation == TRUE. Defaults to
.by_equation == TRUE a named list of model selection criteria.
By default, all criteria are calculated (
.ms_criterion == "all"). To compute only
a subset of the criteria a vector of criteria may be given.
.by_equation == TRUE (the default), the criteria are computed for each
structural equation of the model separately, as suggested by
Sharma et al. (2019)
in the context of PLS. The relevant formula can be found in
Table B1 of the appendix of Sharma et al. (2019)
.by_equation == FALSE the AIC, the BIC and the HQ for whole model
are calculated. All other criteria are currently ignored in this case!
The relevant formulae are (see, e.g., (Akaike 1974)
Hannan and Quinn (1979)
$$AIC = - 2*log(L) + 2*k$$ $$BIC = - 2*log(L) + k*ln(n)$$ $$HQ = - 2*log(L) + 2*k*ln(ln(n))$$
where log(L) is the log likelihood function of the multivariate normal distribution of the observable variables, k the (total) number of estimated parameters, and n the sample size.
.only_structural == TRUE, log(L) is based on the structural model only.
The argument is ignored if
.by_equation == TRUE.
Akaike H (1974).
“A New Look at the Statistical Model Identification.”
IEEE Transactions on Automatic Control, 19(6), 716--723.
Hannan EJ, Quinn BG (1979). “The Determination of the order of an autoregression.” Journal of the Royal Statistical Society: Series B (Methodological), 41(2), 190--195.
Schwarz G (1978). “Estimating the Dimension of a Model.” The Annals of Statistics, 6(2), 461--464. doi:10.1214/aos/1176344136 .
Sharma P, Sarstedt M, Shmueli G, Kim KH, Thiele KO (2019). “PLS-Based Model Selection: The Role of Alternative Explanations in Information Systems Research.” Journal of the Association for Information Systems, 20(4).