NOTE THAT THIS FUNCTION DOES NOT CENTER NOR SCALES THE MATRICES! Any normalization you will have to do yourself. It is best practice to at least center the variables though. A stripped version of O2PLS
o2m_stripped(X, Y, n, nx, ny)
A list containing
Joint \(X\) scores
Joint \(X\) loadings
Joint \(Y\) scores
Joint \(Y\) loadings
Orthogonal \(X\) loadings
Orthogonal \(Y\) loadings
Regression coefficient in Tt
~ U
Regression coefficient in U
~ Tt
Residuals in Tt
in Tt
~ U
Residuals in U
in U
~ Tt
Numeric matrix. Vectors will be coerced to matrix with as.matrix
(if this is possible)
Numeric matrix. Vectors will be coerced to matrix with as.matrix
(if this is possible)
Integer. Number of joint PLS components. Must be positive.
Integer. Number of orthogonal components in \(X\). Negative values are interpreted as 0
Integer. Number of orthogonal components in \(Y\). Negative values are interpreted as 0
If both nx
and ny
are zero, o2m
is equivalent to PLS2 with orthonormal loadings.
This is a stripped implementation of O2PLS, using svd
. For data analysis purposes, consider using o2m
.
o2m