chemometrics (version 0.5)

pls2_nipals: PLS2 by NIPALS

Description

NIPALS algorithm for PLS2 regression (y is multivariate)

Usage

pls2_nipals(X, Y, a, it = 50, tol = 1e-08, scale = FALSE)

Arguments

X

original X data matrix

Y

original Y-data matrix

a

number of PLS components

it

number of iterations

tol

tolerance for convergence

scale

if TRUE the X and y data will be scaled in addition to centering, if FALSE only mean centering is performed

Value

• P
  matrix with loadings for X
• T
  matrix with scores for X
• Q
  matrix with loadings for Y
• U
  matrix with scores for Y
• D
  D-matrix within the algorithm
• W
  weights for X
• C
  weights for Y
• B
  final regression coefficients

Details

The NIPALS algorithm is the originally proposed algorithm for PLS. Here, the Y-data matrix is multivariate.

References

K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.

See Also

mvr, pls1_nipals

Examples

data(cereal)
res <- pls2_nipals(cereal$X,cereal$Y,a=5)