scenarioCor: Simulated quantitative data according SRUW modeling

In masedki/SelvarMix: Regularization for variable selection in model-based clustering and discriminant analysis.

Description

The dataset consists of 2000 data points in R^{14}. On the subset of relevant clustering variables S = \{1, 2\}, data are distributed from a mixture of four equiprobable spherical Gaussian distributions with means (0,0), (4,0) (0,2) and (4,2). The subset of redundant variables is U =\{3-11\} that are explained by the subset of predictor variables R = \{1,2\}. The last three variables are independent W = \{11, 12, 13\}.

Format

A data matrix with 2000 observations on 14 variables and the last column contains the labels.

scenarioCor[,1:14]

a numeric matrix containing the observations

scenarioCor[,15]

an integer vector containing the labels

Details

The subset U of redundant variables is simulated as follows :

x^{U} = (0,0, 0.4, 0.8, ..., 2) + x^{S} b + \varepsilon, with \varepsilon \sim N(0_9, Ω)

The subset W of independent variables is simulated as follows :

x^{W} \sim N((3.2, 3.6, 4), I_3)

For more details on the regression coefficients b and the covariance matrix Ω see Maugis et al.(2009).

References

Maugis, C., Celeux, G., and Martin-Magniette, M. L., 2009. "Variable selection in model-based clustering: A general variable role modeling". Computational Statistics and Data Analysis, vol. 53/11, pp. 3872-3882.

Examples

data(scenarioCor)

masedki/SelvarMix documentation built on May 21, 2019, 12:42 p.m.