Weight Randomization Test algorithm for PLS1
wrtpls.fit(X, Y, ncomp, perms, alpha, ...)An object of class mvdareg is returned. The object contains all components returned by the underlying fit function. In addition, it contains the following:
X loadings
weights
bidiag2 matrix
inverse of bidiag2 matrix
mean of reponse variable
mean of predictor variables
regression coefficients
y-loadings
X scores
orthogonal weights
scaled response values
actual response values
fitted values
residuals
X matrix
predicted values
scaled y-loadings
permutations effected
Significant LVs
weight critical values
normed weights
model fitting time
validation method
number of latent variables
number of permutations performed
permutation alpha value
PLS algorithm
scaling used
was scaling performed
model call
model terms
model matrix
fitted model
a matrix of observations. NAs and Infs are not allowed.
a vector. NAs and Infs are not allowed.
the number of components to include in the model (see below).
the significance level for wrtpls
the number of permutations to run for wrtpls
additional arguments. Currently ignored.
Nelson Lee Afanador (nelson.afanador@mvdalab.com), Thanh Tran (thanh.tran@mvdalab.com)
This function should not be called directly, but through plsFit with the argument method="wrtpls". It implements the Bidiag2 scores algorithm with a permutation test for selecting the statistically significant components.
Indahl, Ulf G., (2014) The geometry of PLS1 explained properly: 10 key notes on mathematical properties of and some alternative algorithmic approaches to PLS1 modeling. Journal of Chemometrics, 28, 168:180.
Manne R., Analysis of two partial-least-squares algorithms for multi-variate calibration. Chemom. Intell. Lab. Syst. 1987; 2: 187:197.
Thanh Tran, Ewa Szymanska, Jan Gerretzen, Lutgarde Buydens, Nelson Lee Afanador, Lionel Blanchet, Weight Randomization Test for the Selection of the Number of Components in PLS Models. Chemom. Intell. Lab. Syst., accepted for publication - Jan 2017.
plsFit