o2pls {o2plsda}

fit O2PLS model with best nc, nx, ny

Description

fit O2PLS model with best nc, nx, ny

Usage

o2pls(X, Y, nc, nx, ny, scale = FALSE, center = FALSE)

Arguments

X

a Numeric matrix (input)

Y

a Numeric matrix (input)

nc

Integer. Number of joint PLS components.

nx

Integer. Number of orthogonal components in X

ny

Integer. Number of orthogonal components in Y

scale

boolean values determining if data should be scaled or not

center

boolean values determining if data should be centered or not

Value

An object containing

Xscore

Joint X scores

Xloading

Joint X loadings

Yscore

Joint Y scores

Yloading

Joint Y loadings

TYosc

Orthogonal X scores

PYosc

Orthogonal X loadings

WYosc

Orthogonal X weights

UXosc

Orthogonal Y scores

PXosc

Orthogonal Y loadings

CXosc

Orthogonal Y weights

BU

Regression coefficient in Tt ~ U

BT

Regression coefficient in U ~ Tt

Xhat

Prediction of X with Y

Yhat

Prediction of Y with X

R2Xhat

Variation of the predicted X as proportion of variation in X

R2Yhat

Variation of the predicted Y as proportion of variation in Y

R2X

Variation of the modeled part in X (defined by Joint + Orthogonal variation) as proportion of total variation in X

R2Y

Variation of the modeled part in Y (defined by Joint + Orthogonal variation) as proportion of total variation in Y

R2Xcorr

Variation of the joint part in X

R2Ycorr

Variation of the joint part in Y

R2Xo

Variation of the orthogonal part in X as proportion of variation in X

R2Yo

Variation of the orthogonal part in Y as proportion of variation in Y

R2Xp

Variation in X joint part predicted by Y Joint part

R2Yp

Variation in Y joint part predicted by X Joint part

varXj

Variation in each Latent Variable (LV) in X Joint part

varYj

Variation in each Latent Variable (LV) in Y Joint part

varXorth

Variation in each Latent Variable (LV) in X Orthogonal part

varYorth

Variation in each Latent Variable (LV) in Y Orthogonal part

Exy

Residuals in X

Fxy

Residuals in Y

Author(s)

Kai Guo

Examples

set.seed(123)
X = matrix(rnorm(500),50,10)
Y = matrix(rnorm(500),50,10)
X = scale(X, scale = TRUE)
Y = scale(Y, scale = TRUE)
fit <- o2pls(X, Y, 1, 2, 2)
summary(fit)