KLFDA: Kernel local Fisher discriminant analysis

In xinghuq/KLFDAPC: KLFDAPC: Kernel Local Fisher Discriminant Analysis of Principal Components- A Non-linear Tool for Population Structure Inference.

Kernel local Fisher discriminant analysis

Description

This function performs Kernel local Fisher discriminant analysis. The function provided here allows users to carry out the KLFDA using other input datasets. We used the gaussan matrix as example. Users can input the

Usage

KLFDA(kdata, y, r, sigma = 0.5, metric = c("weighted", "orthonormalized", "plain"),tol=1e-5,knn = 6, reg = 0.001)

Arguments

kdata	The input dataset (kernel matrix). The input data can be a genotype matrix, dataframe, species occurence matrix, or principal components. The dataset have to convert to a kernel matrix before feed into this function.
y	The group lables
r	Number of reduced features
sigma	The sigma used to estimate gaussan matrix.
metric	Type of metric in the embedding space (default: 'weighted') 'weighted' - weighted eigenvectors 'orthonormalized' - orthonormalized 'plain' - raw eigenvectors
knn	The number of nearest neighbours
reg	The regularization parameter

Value

Z	The reduced features
Tr	The transformation matrix

References

Tang, Y., & Li, W. (2019). lfda: Local Fisher Discriminant Analysis inR. Journal of Open Source Software, 4(39), 1572.

See Also

KLFDAPC

Examples

kmat <- kmatrixGauss(iris[, -5],sigma=1)
zklfda=KLFDA(kmat, iris[, 5], r=3,tol=1e-5, metric = "plain")
print(zklfda$Z)

xinghuq/KLFDAPC documentation built on June 12, 2022, 7:20 p.m.