fit O2PLS model with best nc, nx, ny
o2pls(X, Y, nc, nx, ny, scale = FALSE, center = FALSE)An object containing
Joint \(X\) scores
Joint \(X\) loadings
Joint \(Y\) scores
Joint \(Y\) loadings
Orthogonal \(X\) scores
Orthogonal \(X\) loadings
Orthogonal \(X\) weights
Orthogonal \(Y\) scores
Orthogonal \(Y\) loadings
Orthogonal \(Y\) weights
Regression coefficient in Tt ~ U
Regression coefficient in U ~ Tt
Prediction of \(X\) with \(Y\)
Prediction of \(Y\) with \(X\)
Variation of the predicted \(X\) as proportion of variation in \(X\)
Variation of the predicted \(Y\) as proportion of variation in \(Y\)
Variation of the modeled part in \(X\) (defined by Joint + Orthogonal variation) as proportion of total variation in \(X\)
Variation of the modeled part in \(Y\) (defined by Joint + Orthogonal variation) as proportion of total variation in \(Y\)
Variation of the joint part in \(X\)
Variation of the joint part in \(Y\)
Variation of the orthogonal part in \(X\) as proportion of variation in \(X\)
Variation of the orthogonal part in \(Y\) as proportion of variation in \(Y\)
Variation in \(X\) joint part predicted by \(Y\) Joint part
Variation in \(Y\) joint part predicted by \(X\) Joint part
Variation in each Latent Variable (LV) in \(X\) Joint part
Variation in each Latent Variable (LV) in \(Y\) Joint part
Variation in each Latent Variable (LV) in \(X\) Orthogonal part
Variation in each Latent Variable (LV) in \(Y\) Orthogonal part
Residuals in \(X\)
Residuals in \(Y\)
a Numeric matrix (input)
a Numeric matrix (input)
Integer. Number of joint PLS components.
Integer. Number of orthogonal components in X
Integer. Number of orthogonal components in Y
boolean values determining if data should be scaled or not
boolean values determining if data should be centered or not
Kai Guo
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)
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