scores: Compute Latent Variable Scores

In rrr: Reduced-Rank Regression

Description

Compute Latent Variable Scores

Usage

scores(x, y, type = "pca", rank = "full", k = 0)

Arguments

x	data frame or matrix of predictor variables
y	data frame or matrix of response variables
type	type of reduced-rank regression model to fit. type = "identity", the default, uses \mathbf{Γ} = \mathbf{I} to fit a reduced-rank regression. type = "pca" fits a principal component analysis model as a special case of reduced-rank regression. type = "cva" fits a canonical variate analysis model as a special case of reduced-rank regression. type = "lda" fits a linear discriminant analysis model as a special case of reduced-rank regression.
rank	rank of coefficient matrix.
k	small constant added to diagonal of covariance matrices to make inversion easier.

References

Izenman, A.J. (2008) Modern Multivariate Statistical Techniques. Springer.

Examples

data(pendigits)
digits_features <- pendigits[, -35:-36]
scores(digits_features, digits_features, type = "pca", rank = 3)

library(dplyr)
data(COMBO17)
galaxy <- as_data_frame(COMBO17)
galaxy <- select(galaxy, -starts_with("e."), -Nr, -UFS:-IFD)
galaxy <- na.omit(galaxy)
galaxy_x <- select(galaxy, -Rmag:-chi2red)
galaxy_y <- select(galaxy, Rmag:chi2red)
scores(galaxy_x, galaxy_y, type = "cva", rank = 4)

data(iris)
iris_x <- iris[,1:4]
iris_y <- iris[5]
scores(iris_x, iris_y, type = "lda")

rrr documentation built on May 1, 2019, 9:16 p.m.