mixture_plots {dCUR}

mixture_plots

Description

This function returns different plots associated with the fitting of leverages scores through Mixture Gaussian Models.

Usage

mixture_plots(data)

Arguments

data

An object resulting from a call to CUR when "mixture" is specified as cur_method.

Details

Gaussian Mixture Models Plots

Value

mixture_plots returns a list with the following plots:

BIC

BIC Plot of the Bayesian Information Criterion (BIC) for each number of mixture components. E and V stands for equal variance in mixture components or variable variance, respectively.

density

leverages score's density

Cumulative

cumulative density of leverages scores.

QQPlot

Plot the sample quantiles and controlled quantiles of the inverse of the cumulative distribution function.

Author(s)

Cesar Gamboa-Sanabria, Stefany Matarrita-Munoz, Katherine Barquero-Mejias, Greibin Villegas-Barahona, Mercedes Sanchez-Barba and Maria Purificacion Galindo-Villardon.

References

Mahoney MW, Drineas P (2009). “CUR matrix decompositions for improved data analysis.” Proceedings of the National Academy of Sciences, 106(3), 697–702. ISSN 0027-8424, doi:10.1073/pnas.0803205106. Villegas G, others (2018). “Modelo estadistico pedagogico para la toma de decisiones administrativas y academicas con impacto en el mejoramiento continuo del rendimiento de los estudiantes universitarios, basado en los metodos de seleccion CUR.” doi:10.14201/gredos.139405. Villegas G, Martin-Barreiro C, Gonzalez-Garcia N, Hernandez-Gonzalez S, Sanchez-Barba M, Galindo-Villardon M (2019). “Dynamic CUR, an alternative to variable selection in CUR decomposition.” Revistas Investigacion Operacional, 40(3), 391–399.

See Also

dCUR CUR

Examples

results <- CUR(data=AASP, variables=hoessem:notabachillerato,
k=20, rows = .9999999, columns = .10, standardize = TRUE,
cur_method = "mixture")
mixture_plots(results)