RNGMIX-class {rebmix}

Class "RNGMIX"

Description

Object of class RNGMIX.

Objects from the Class

Objects can be created by calls of the form new("RNGMIX", ...). Accessor methods for the slots are a.Dataset.name(x = NULL), a.rseed(x = NULL), a.n(x = NULL), a.Theta(x = NULL), a.Dataset(x = NULL, pos = 0), a.Zt(x = NULL), a.w(x = NULL), a.Variables(x = NULL), a.ymin(x = NULL) and a.ymax(x = NULL), where x and pos stand for an object of class RNGMIX and a desired slot item, respectively.

Slots

Dataset.name:

a character vector containing list names of data frames of size n \times d that d-dimensional datasets are written in.

rseed:

set the random seed to any negative integer value to initialize the sequence. The first file in Dataset.name corresponds to it. For each next file the random seed is decremented r_{\mathrm{seed}} = r_{\mathrm{seed}} - 1. The default value is -1.

n:

a vector containing numbers of observations in classes n_{l}, where number of observations n = \sum_{l = 1}^{c} n_{l}.

Theta:

a list containing c parametric family types pdfl. One of "normal", "lognormal", "Weibull", "gamma", "Gumbel", "binomial", "Poisson", "Dirac", "uniform" or circular "vonMises" defined for 0 \leq y_{i} \leq 2 \pi. Component parameters theta1.l follow the parametric family types. One of \mu_{il} for normal, lognormal, Gumbel and von Mises distributions, \theta_{il} for Weibull, gamma, binomial, Poisson and Dirac distributions and a for uniform distribution. Component parameters theta2.l follow theta1.l. One of \sigma_{il} for normal, lognormal and Gumbel distributions, \beta_{il} for Weibull and gamma distributions, p_{il} for binomial distribution, \kappa_{il} for von Mises distribution and b for uniform distribution. Component parameters theta3.l follow theta2.l. One of \xi_{il} \in \{-1, 1\} for Gumbel distribution.

Dataset:

a list of length n_{\mathrm{D}} of data frames of size n \times d containing d-dimensional datasets. Each of the d columns represents one random variable. Numbers of observations n equal the number of rows in the datasets.

Zt:

a factor of true cluster membership.

w:

a vector of length c containing component weights w_{l} summing to 1.

Variables:

a character vector containing types of variables. One of "continuous" or "discrete".

ymin:

a vector of length d containing minimum observations.

ymax:

a vector of length d containing maximum observations.

Author(s)

Marko Nagode