Returns as default the optimized RCLSMIX algorithm output for mixtures of conditionally independent normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac, uniform or von Mises component densities. If model equals "RCLSMVNORM" optimized output for mixtures of multivariate normal component densities with unrestricted variance-covariance matrices is returned.
# S4 method for RCLSMIX
BFSMIX(model = "RCLSMIX", x = list(), Dataset = data.frame(),
Zt = factor(), ...)
## ... and for other signaturesReturns an optimized object of class RCLSMIX or RCLSMVNORM.
see Methods section below.
a list of objects of class REBMIX of length \(o\) obtained by running REBMIX on \(g = 1, \ldots, s\) train datasets \(Y_{\mathrm{train}g}\) all of length \(n_{\mathrm{train}g}\).
For the train datasets the corresponding class membership \(\bm{\Omega}_{g}\) is known. This yields
\(n_{\mathrm{train}} = \sum_{g = 1}^{s} n_{\mathrm{train}g}\), while \(Y_{\mathrm{train}q} \cap Y_{\mathrm{train}g} = \emptyset\) for all \(q \neq g\).
Each object in the list corresponds to one chunk, e.g., \((y_{1j}, y_{3j})^{\top}\). The default value is list().
a data frame containing test dataset \(Y_{\mathrm{test}}\) of length \(n_{\mathrm{test}}\). For the test dataset the corresponding class membership \(\bm{\Omega}_{g}\) is not known.
The default value is data.frame().
a factor of true class membership \(\bm{\Omega}_{g}\) for the test dataset. The default value is factor().
currently not used.
signature(model = "RCLSMIX")a character giving the default class name "RCLSMIX" for mixtures of conditionally independent normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac, uniform or von Mises component densities.
signature(model = "RCLSMVNORM")a character giving the class name "RCLSMVNORM" for mixtures of multivariate normal component densities with unrestricted variance-covariance matrices.
Marko Nagode
R. Kohavi and G. H. John. Wrappers for feature subset selection, Artificial Intelligence, 97(1-2):273-324, 1997. tools:::Rd_expr_doi("10.1016/S0004-3702(97)00043-X").