BFSMIX-methods {rebmix}R Documentation

Predicts Class Membership Based Upon the Best First Search Algorithm

Description

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.

Usage

## 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 oo obtained by running REBMIX on g=1,,sg = 1, \ldots, s train datasets YtraingY_{\mathrm{train}g} all of length ntraingn_{\mathrm{train}g}. For the train datasets the corresponding class membership Ωg\bm{\Omega}_{g} is known. This yields ntrain=g=1sntraingn_{\mathrm{train}} = \sum_{g = 1}^{s} n_{\mathrm{train}g}, while YtrainqYtraing=Y_{\mathrm{train}q} \cap Y_{\mathrm{train}g} = \emptyset for all qgq \neq g. Each object in the list corresponds to one chunk, e.g., (y1j,y3j)(y_{1j}, y_{3j})^{\top}. The default value is list().

Dataset

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

Zt

a factor of true class membership Ωg\bm{\Omega}_{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, 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.

Author(s)

Marko Nagode

References

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


[Package rebmix version 2.16.0 Index]