var_exp: var_exp

In dCUR: Dimension Reduction with Dynamic CUR

var_exp

Description

var_exp is used to compute the proportion of the fraction of variance explained by a principal component analysis.

Usage

var_exp(data, standardize = FALSE, ...)

Arguments

data	a data frame that contains the variables to be used in CUR decomposition.
standardize	logical. If TRUE rescale an original data frame to have a mean of zero and a standard deviation of one.
...	Additional arguments to be passed to dplyr::select

Details

The objective of CUR decomposition is to find the most relevant variables and observations within a data matrix and to reduce the dimensionality. It is well known that as more columns (variables) and rows are selected, the relative error will be lower; however, this is not true for k (number of components to calculate leverages). Given the above, this function seeks to find the best-balanced scenario of k, the number of relevant columns, and rows that have an error very close to the minimum, and that, in turn, uses a smaller amount of information.

Value

var_exp

a data frame with the proportion of explained variance for each principal component.

Author(s)

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

References

\insertRef

Mahoney697dCUR \insertRefvillegas2018modelodCUR \insertRefdynamyCURdCUR

See Also

dCUR CUR

Examples

var_exp(AASP, standardize = TRUE, hoessem:notabachillerato)

dCUR documentation built on Oct. 18, 2023, 5:10 p.m.