wrtpls.fit: Weight Randomization Test PLS

Description Usage Arguments Details Value Author(s) References See Also

View source: R/wrtpls.fit.R

Description

Weight Randomization Test algorithm for PLS1

Usage

1
wrtpls.fit(X, Y, ncomp, perms, alpha, ...)

Arguments

X

a matrix of observations. NAs and Infs are not allowed.

Y

a vector. NAs and Infs are not allowed.

ncomp

the number of components to include in the model (see below).

alpha

the significance level for wrtpls

perms

the number of permutations to run for wrtpls

...

additional arguments. Currently ignored.

Details

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.

Value

An object of class mvdareg is returned. The object contains all components returned by the underlying fit function. In addition, it contains the following:

loadings

X loadings

weights

weights

D2

bidiag2 matrix

iD2

inverse of bidiag2 matrix

Ymean

mean of reponse variable

Xmeans

mean of predictor variables

coefficients

regression coefficients

y.loadings

y-loadings

scores

X scores

R

orthogonal weights

Y

scaled response values

Yactual

actual response values

fitted

fitted values

residuals

residuals

Xdata

X matrix

iPreds

predicted values

y.loadings2

scaled y-loadings

wrtpls

permutations effected

wrtpls.out.Sig

Significant LVs

wrtpls.crit

weight critical values

actual.normwobs

normed weights

fit.time

model fitting time

val.method

validation method

ncomp

number of latent variables

perms

number of permutations performed

alpha

permutation alpha value

contrasts

contrast matrix used

method

PLS algorithm

scale

scaling used

scaled

was scaling performed

call

model call

terms

model terms

mm

model matrix

model

fitted model

Author(s)

Nelson Lee Afanador ([email protected]), Thanh Tran ([email protected])

References

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.

See Also

plsFit


mvdalab documentation built on Nov. 17, 2017, 6 a.m.