lpda.pca {lpda}

lpda.pca computes a PCA to the original data and selects the desired PCs when Variability is supplied

Description

lpda.pca computes the discriminating hyperplane for two groups with Principal Components (PC)

Usage

lpda.pca(data, group, PC = 2, Variability = NULL)

Arguments

data

Matrix containing data. Individuals in rows and variables in columns

group

Vector with the variable group

PC

Number of Principal Components (PC) for PCA. By default it is 2. When the number of PC is not decided, it can be determined choosing the desired proportion of explained variability (Variability parameter).

Variability

Parameter for Principal Components (PC) selection. This is the minimum desired proportion of variability explained for the PC of the variables. The analysis is always done with a minimum of 2 PCs. If it is NULL the PCA will be computed with PC parameter.

Value

loadings

Principal Components loadings.

scores

Principal Components scores.

var.exp

A matrix containing the explained variance for each component and the cumulative variance.

PCs

Number of Principal Components in the analysis.

Author(s)

Maria Jose Nueda, [e-mail]

References

Nueda MJ, Gandía C, Molina MD (2022) LPDA: A new classification method based on linear programming. PLoS ONE 17(7): e0270403. <https://doi.org/10.1371/journal.pone.0270403>

See Also

lpda