Predicts Class Membership Based Upon the Best First Search Algorithm

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Description

Returns as default the optimized RCLSMIX algorithm output for mixtures of conditionally independent normal, lognormal, Weibull, gamma, binomial, Poisson or Dirac component densities. If model equals "RCLSMVNORM" optimized output for mixtures of multivariate normal component densities with unrestricted variance-covariance matrices is returned.

Usage

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## S4 method for signature 'RCLSMIX'
BFSMIX(model = "RCLSMIX", x = list(), Dataset = data.frame(),
       Zt = factor(), ...)
## ... and for other signatures     

Arguments

model

see Methods section below.

x

a list of objects of class REBMIX of length o obtained by running REBMIX on g = 1, …, s train datasets Y_{\mathrm{train}g} all of length n_{\mathrm{train}g}. For the train datasets the corresponding class membership \bm{Ω}_{g} is known. This yields n_{\mathrm{train}} = ∑_{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().

Dataset

a data frame containing test dataset Y_{\mathrm{test}} of length n_{\mathrm{test}}. For the test dataset the corresponding class membership \bm{Ω}_{g} is not known. The default value is data.frame().

Zt

a factor of true class membership \bm{Ω}_{g} for the test dataset. The default value is factor().

...

currently not used.

Value

Returns an optimized object of class RCLSMIX or RCLSMVNORM.

Methods

signature(model = "RCLSMIX")

a character giving the default class name "RCLSMIX" for mixtures of conditionally independent normal, lognormal, Weibull, gamma, binomial, Poisson or Dirac component densities.

signature(model = "RCLSMVNORM")

a character giving the class name "RCLSMVNORM" for mixtures of multivariate normal component densities with unrestricted variance-covariance matrices.

Author(s)

Marko Nagode

References

R. Kohavi and G. H. John. Wrappers for feature subset selection, Artificial Intelligence, 97(1-2):273-324, 1997. http://dx.doi.org/10.1016/S0004-3702(97)00043-X.

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