Encoding:	UTF-8
Type:	Package
Title:	Variable Selection in Partial Least Squares
Version:	0.9.13
Date:	2025-04-09
Description:	Interfaces and methods for variable selection in Partial Least Squares. The methods include filter methods, wrapper methods and embedded methods. Both regression and classification is supported.
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/khliland/plsVarSel/
BugReports:	https://github.com/khliland/plsVarSel/issues/
Depends:	pls
Imports:	grDevices, graphics, genalg, mvtnorm, bdsmatrix, MASS, progress, parallel, stats, praznik
Suggests:	Rmpi
RoxygenNote:	7.3.2
NeedsCompilation:	no
Packaged:	2025-04-09 07:40:14 UTC; kristian
Author:	Kristian Hovde Liland [aut, cre], Tahir Mehmood [ctb], Solve Sæbø [ctb]
Maintainer:	Kristian Hovde Liland <kristian.liland@nmbu.no>
Repository:	CRAN
Date/Publication:	2025-04-09 08:00:02 UTC

Hotelling's T^2 based variable selection in PLS – T^2-PLS)

Description

Variable selection based on the T^2 statistic. A side effect of running the selection is printing of tables and production of plots.

Usage

T2_pls(ytr, Xtr, yts, Xts, ncomp = 10, alpha = c(0.2, 0.15, 0.1, 0.05, 0.01))

Arguments

Value

Parameters and variables corresponding to variable selections of minimum error and minimum variable set.

References

Tahir Mehmood, Hotelling T^2 based variable selection in partial least squares regression, Chemometrics and Intelligent Laboratory Systems 154 (2016), pp 23-28

Examples

data(gasoline, package = "pls")
library(pls)
if(interactive()){
  t2 <- T2_pls(gasoline$octane[1:40], gasoline$NIR[1:40,], 
             gasoline$octane[-(1:40)], gasoline$NIR[-(1:40),], 
             ncomp = 10, alpha = c(0.2, 0.15, 0.1, 0.05, 0.01))
  matplot(t(gasoline$NIR), type = 'l', col=1, ylab='intensity')
  points(t2$mv[[1]], colMeans(gasoline$NIR)[t2$mv[[1]]], col=2, pch='x')
  points(t2$mv[[2]], colMeans(gasoline$NIR)[t2$mv[[2]]], col=3, pch='o')
}

Filter methods for variable selection with Partial Least Squares.

Description

Various filter methods extracting and using information from mvr objects to assign importance to all included variables. Available methods are Significance Multivariate Correlation (sMC), Selectivity Ratio (SR), Variable Importance in Projections (VIP), Loading Weights (LW), Regression Coefficients (RC).

Usage

VIP(pls.object, opt.comp, p = dim(pls.object$coef)[1])

SR(pls.object, opt.comp, X)

sMC(pls.object, opt.comp, X, alpha_mc = 0.05)

LW(pls.object, opt.comp)

RC(pls.object, opt.comp)

URC(pls.object, opt.comp)

FRC(pls.object, opt.comp)

mRMR(pls.object, nsel, X)

Arguments

Details

From plsVarSel 0.9.10, the VIP method handles multiple responses correctly, as does the LW method. All other filter methods implemented in this package assume a single response and will give its results based on the first response in multi-response cases.

Value

A vector having the same length as the number of variables in the associated PLS model. High values are associated with high importance, explained variance or relevance to the model.

The sMC has an attribute "quantile", which is the associated quantile of the F-distribution, which can be used as a cut-off for significant variables, similar to the cut-off of 1 associated with the VIP.

Author(s)

Tahir Mehmood, Kristian Hovde Liland, Solve Sæbø.

References

T. Mehmood, K.H. Liland, L. Snipen, S. Sæbø, A review of variable selection methods in Partial Least Squares Regression, Chemometrics and Intelligent Laboratory Systems 118 (2012) 62-69. T. Mehmood, S. Sæbø, K.H. Liland, Comparison of variable selection methods in partial least squares regression, Journal of Chemometrics 34 (2020) e3226.

See Also

VIP (SR/sMC/LW/RC), filterPLSR, shaving, stpls, truncation, bve_pls, ga_pls, ipw_pls, mcuve_pls, rep_pls, spa_pls, lda_from_pls, lda_from_pls_cv, setDA.

Examples

data(gasoline, package = "pls")
library(pls)
pls  <- plsr(octane ~ NIR, ncomp = 10, validation = "LOO", data = gasoline)
comp <- which.min(pls$validation$PRESS)
X    <- unclass(gasoline$NIR)
vip <- VIP(pls, comp)
sr  <- SR (pls, comp, X)
smc <- sMC(pls, comp, X)
lw  <- LW (pls, comp)
rc  <- RC (pls, comp)
urc <- URC(pls, comp)
frc <- FRC(pls, comp)
mrm <- mRMR(pls, 401, X)$score
matplot(scale(cbind(vip, sr, smc, lw, rc, urc, frc, mrm)), type = 'l')

Weighted Variable Contribution in PLS (WVC-PLS)

Description

This implements the PLS-WVC2 component dependent version of WVC from Lin et al., i.e., using Equations 14, 16 and 19. The implementation is used in T. Mehmood, S. Sæbø, K.H. Liland, Comparison of variable selection methods in partial least squares regression, Journal of Chemometrics 34 (2020) e3226. However, there is a mistake in the notation in Mehmood et al. exchanging the denominator of Equation 19 (w'X'Xw) with (w'X'Yw).

Usage

WVC_pls(y, X, ncomp, normalize = FALSE, threshold = NULL)

Arguments

Value

loading weights, loadings, regression coefficients, scores and Y-loadings plus the WVC weights.

References

Variable selection in partial least squares with the weighted variable contribution to the first singular value of the covariance matrix, Weilu Lin, Haifeng Hang, Yingping Zhuang, Siliang Zhang, Chemometrics and Intelligent Laboratory Systems 183 (2018) 113–121.

Examples

library(pls)
data(mayonnaise, package = "pls")
wvc <- WVC_pls(factor(mayonnaise$oil.type), mayonnaise$NIR, 10)
wvcNT <- WVC_pls(factor(mayonnaise$oil.type), mayonnaise$NIR, 10, TRUE, 0.5)
old.par <- par(mfrow=c(3,1), mar=c(2,4,1,1))
matplot(t(mayonnaise$NIR), type='l', col=1, ylab='intensity')
matplot(wvc$W[,1:3], type='l', ylab='W')
matplot(wvcNT$W[,1:3], type='l', ylab='W, thr.=0.5')
par(old.par)

Backward variable elimination PLS (BVE-PLS)

Description

A backward variable elimination procedure for elimination of non informative variables.

Usage

bve_pls(y, X, ncomp = 10, ratio = 0.75, VIP.threshold = 1)

Arguments

Details

Variables are first sorted with respect to some importancemeasure, and usually one of the filter measures described above are used. Secondly, a threshold is used to eliminate a subset of the least informative variables. Then a model is fitted again to the remaining variables and performance is measured. The procedure is repeated until maximum model performance is achieved.

Value

Returns a vector of variable numbers corresponding to the model having lowest prediction error.

Author(s)

Tahir Mehmood, Kristian Hovde Liland, Solve Sæbø.

References

I. Frank, Intermediate least squares regression method, Chemometrics and Intelligent Laboratory Systems 1 (3) (1987) 233-242.

See Also

VIP (SR/sMC/LW/RC), filterPLSR, shaving, stpls, truncation, bve_pls, ga_pls, ipw_pls, mcuve_pls, rep_pls, spa_pls, lda_from_pls, lda_from_pls_cv, setDA.

Examples

data(gasoline, package = "pls")
with( gasoline, bve_pls(octane, NIR) )

Covariance Selection - CovSel

Description

Sequential selection of variables based on squared covariance with response and intermediate deflation (as in Partial Least Squares).

Usage

covSel(X, Y, nvar)

Arguments

Value

References

J.M. Roger, B. Palagos, D. Bertrand, E. Fernandez-Ahumada. CovSel: Variable selection for highly multivariate and multi-response calibration: Application to IR spectroscopy. Chemom Intel Lab Syst. 2011;106(2):216-223. P. Mishra, A brief note on a new faster covariate's selection (fCovSel) algorithm, Journal of Chemometrics 36(5) 2022.

Examples

data(gasoline, package = "pls")
sels <- with(gasoline, covSel(NIR, octane, 5))
matplot(t(gasoline$NIR), type = "l")
abline(v = sels$selected, col = 2)

Optimisation of filters for Partial Least Squares

Description

Extract the index of influential variables based on threshold defiend for LW (loading weights), RC (regression coef), JT (jackknife testing) and VIP (variable importance on projection).

Usage

filterPLSR(
  y,
  X,
  ncomp = 10,
  ncomp.opt = c("minimum", "same"),
  validation = "LOO",
  LW.threshold = NULL,
  RC.threshold = NULL,
  URC.threshold = NULL,
  FRC.threshold = NULL,
  JT.threshold = NULL,
  VIP.threshold = NULL,
  SR.threshold = NULL,
  sMC.threshold = NULL,
  mRMR.threshold = NULL,
  WVC.threshold = NULL,
  ...
)

Arguments

Details

Filter methods are applied for variable selection with PLSR. This function can return selected variables and Root Mean Squared Error of Cross-Validation for various filter methods and determine optimum numbers of components.

Value

Returns a list of lists containing filters (outer list), their selected variables, optimal numbers of components and prediction accuracies.

Author(s)

Tahir Mehmood, Kristian Hovde Liland, Solve Sæbø.

References

T. Mehmood, K.H. Liland, L. Snipen, S. Sæbø, A review of variable selection methods in Partial Least Squares Regression, Chemometrics and Intelligent Laboratory Systems 118 (2012) 62-69.

See Also

VIP (SR/sMC/LW/RC/URC/FRC/mRMR), filterPLSR, spa_pls, stpls, truncation, bve_pls, mcuve_pls, ipw_pls, ga_pls, rep_pls, WVC_pls, T2_pls.

Examples

data(gasoline, package = "pls")
## Not run: 
with( gasoline, filterPLSR(octane, NIR, ncomp = 10, "minimum", validation = "LOO",
 RC.threshold = c(0.1,0.5), SR.threshold = 0.5))

## End(Not run)

Genetic algorithm combined with PLS regression (GA-PLS)

Description

A subset search algorithm inspired by biological evolution theory and natural selection.

Usage

ga_pls(y, X, GA.threshold = 10, iters = 5, popSize = 100)

Arguments

Details

1. Building an initial population of variable sets by setting bits for each variable randomly, where bit '1' represents selection of corresponding variable while '0' presents non-selection. The approximate size of the variable sets must be set in advance.

2. Fitting a PLSR-model to each variable set and computing the performance by, for instance, a leave one out cross-validation procedure.

3. A collection of variable sets with higher performance are selected to survive until the next "generation".

4. Crossover and mutation: new variable sets are formed 1) by crossover of selected variables between the surviving variable sets, and 2) by changing (mutating) the bit value for each variable by small probability.

5. The surviving and modified variable sets form the population serving as input to point 2.

Value

Returns a vector of variable numbers corresponding to the model having lowest prediction error.

Author(s)

Tahir Mehmood, Kristian Hovde Liland, Solve Sæbø.

References

K. Hasegawa, Y. Miyashita, K. Funatsu, GA strategy for variable selection in QSAR studies: GA-based PLS analysis of calcium channel antagonists, Journal of Chemical Information and Computer Sciences 37 (1997) 306-310.

See Also

VIP (SR/sMC/LW/RC), filterPLSR, shaving, stpls, truncation, bve_pls, ga_pls, ipw_pls, mcuve_pls, rep_pls, spa_pls, lda_from_pls, lda_from_pls_cv, setDA.

Examples

data(gasoline, package = "pls")
# with( gasoline, ga_pls(octane, NIR, GA.threshold = 10) ) # Time-consuming

Iterative predictor weighting PLS (IPW-PLS)

Description

An iterative procedure for variable elimination.

Usage

ipw_pls(
  y,
  X,
  ncomp = 10,
  no.iter = 10,
  IPW.threshold = 0.01,
  filter = "RC",
  scale = TRUE
)

ipw_pls_legacy(y, X, ncomp = 10, no.iter = 10, IPW.threshold = 0.1)

Arguments

Details

This is an iterative elimination procedure where a measure of predictor importance is computed after fitting a PLSR model (with complexity chosen based on predictive performance). The importance measure is used both to re-scale the original X-variables and to eliminate the least important variables before subsequent model re-fitting

The IPW implementation was corrected in plsVarSel version 0.9.5. For backward compatibility the old implementation is included as ipw_pls_legacy.

Value

Returns a vector of variable numbers corresponding to the model having lowest prediction error.

Author(s)

Kristian Hovde Liland

References

M. Forina, C. Casolino, C. Pizarro Millan, Iterative predictor weighting (IPW) PLS: a technique for the elimination of useless predictors in regression problems, Journal of Chemometrics 13 (1999) 165-184.

See Also

VIP (SR/sMC/LW/RC), filterPLSR, shaving, stpls, truncation, bve_pls, ga_pls, ipw_pls, mcuve_pls, rep_pls, spa_pls, lda_from_pls, setDA.

Examples

data(gasoline, package = "pls")
with( gasoline, ipw_pls(octane, NIR) )

LDA/QDA classification from PLS model

Description

For each number of components LDA/QDA models are created from the scores of the supplied PLS model and classifications are performed.

Usage

lda_from_pls(model, grouping, newdata, ncomp)

Arguments

Value

matrix of classifications

See Also

VIP (SR/sMC/LW/RC), filterPLSR, shaving, stpls, truncation, bve_pls, ga_pls, ipw_pls, mcuve_pls, rep_pls, spa_pls, lda_from_pls, lda_from_pls_cv, setDA.

Examples

data(mayonnaise, package = "pls")
mayonnaise <- within(mayonnaise, {dummy <- model.matrix(~y-1,data.frame(y=factor(oil.type)))})
pls <- plsr(dummy ~ NIR, ncomp = 10, data = mayonnaise, subset = train)
with(mayonnaise, {
 classes <- lda_from_pls(pls, oil.type[train], NIR[!train,], 10)
 colSums(oil.type[!train] == classes) # Number of correctly classified out of 42
})

Cross-validated LDA/QDA classification from PLS model

Description

For each number of components LDA/QDA models are created from the scores of the supplied PLS model and classifications are performed. This use of cross-validation has limitations. Handle with care!

Usage

lda_from_pls_cv(model, X, y, ncomp, Y.add = NULL)

Arguments

Value

matrix of classifications

See Also

VIP (SR/sMC/LW/RC), filterPLSR, shaving, stpls, truncation, bve_pls, ga_pls, ipw_pls, mcuve_pls, rep_pls, spa_pls, lda_from_pls, lda_from_pls_cv, setDA.

Examples

data(mayonnaise, package = "pls")
mayonnaise <- within(mayonnaise, {dummy <- model.matrix(~y-1,data.frame(y=factor(oil.type)))})
pls <- plsr(dummy ~ NIR, ncomp = 8, data = mayonnaise, subset = train, 
            validation = "CV", segments = 40, segment.type = "consecutive")
with(mayonnaise, {
 classes <- lda_from_pls_cv(pls, NIR[train,], oil.type[train], 8)
 colSums(oil.type[train] == classes) # Number of correctly classified out of 120
})

Uninformative variable elimination in PLS (UVE-PLS)

Description

Artificial noise variables are added to the predictor set before the PLSR model is fitted. All the original variables having lower "importance" than the artificial noise variables are eliminated before the procedure is repeated until a stop criterion is reached.

Usage

mcuve_pls(y, X, ncomp = 10, N = 3, ratio = 0.75, MCUVE.threshold = NA)

Arguments

Value

Returns a vector of variable numbers corresponding to the model having lowest prediction error.

Author(s)

Tahir Mehmood, Kristian Hovde Liland, Solve Sæbø.

References

V. Centner, D. Massart, O. de Noord, S. de Jong, B. Vandeginste, C. Sterna, Elimination of uninformative variables for multivariate calibration, Analytical Chemistry 68 (1996) 3851-3858.

See Also

VIP (SR/sMC/LW/RC), filterPLSR, shaving, stpls, truncation, bve_pls, ga_pls, ipw_pls, mcuve_pls, rep_pls, spa_pls, lda_from_pls, lda_from_pls_cv, setDA.

Examples

data(gasoline, package = "pls")
with( gasoline, mcuve_pls(octane, NIR) )

Multivariate regression function

Description

Adaptation of mvr from package pls v 2.4.3.

Usage

mvrV(
  formula,
  ncomp,
  Y.add,
  data,
  subset,
  na.action,
  shrink,
  method = c("truncation", "stpls", "model.frame"),
  scale = FALSE,
  validation = c("none", "CV", "LOO"),
  model = TRUE,
  x = FALSE,
  y = FALSE,
  ...
)

Arguments

See Also

mvr

Matrix plotting

Description

Plot a heatmap with colorbar.

Usage

myImagePlot(x, main, ...)

Arguments

Author(s)

Tahir Mehmood, Kristian Hovde Liland, Solve Sæbø.

References

T. Mehmood, K.H. Liland, L. Snipen, S. Sæbø, A review of variable selection methods in Partial Least Squares Regression, Chemometrics and Intelligent Laboratory Systems 118 (2012) 62-69.

See Also

VIP (SR/sMC/LW/RC), filterPLSR, shaving, stpls, truncation, bve_pls, ga_pls, ipw_pls, mcuve_pls, rep_pls, spa_pls, lda_from_pls, lda_from_pls_cv, setDA.

Examples

myImagePlot(matrix(1:12,3,4), 'A header')

Variable selection in Partial Least Squares

Description

A large collection of variable selection methods for use with Partial Least Squares. These include all methods in Mehmood et al. 2012 and more. All functions treat numeric responses as regression and factor responses as classification. Default classification is PLS + LDA, but setDA() can be used to choose PLS + QDA or PLS with response column maximization.

References

T. Mehmood, K.H. Liland, L. Snipen, S. Sæbø, A review of variable selection methods in Partial Least Squares Regression, Chemometrics and Intelligent Laboratory Systems 118 (2012) 62-69. T. Mehmood, S. Sæbø, K.H. Liland, Comparison of variable selection methods in partial least squares regression, Journal of Chemometrics 34 (2020) e3226.

See Also

VIP (SR/sMC/LW/RC), filterPLSR, shaving, stpls, truncation, bve_pls, ga_pls, ipw_pls, mcuve_pls, rep_pls, spa_pls, lda_from_pls, lda_from_pls_cv, setDA.

Regularized elimination procedure in PLS

Description

A regularized variable elimination procedure for parsimonious variable selection, where also a stepwise elimination is carried out

Usage

rep_pls(y, X, ncomp = 5, ratio = 0.75, VIP.threshold = 0.5, N = 3)

Arguments

Details

A stability based variable selection procedure is adopted, where the samples have been split randomly into a predefined number of training and test sets. For each split, g, the following stepwise procedure is adopted to select the variables. This implementation does not follow the original publication exactly, but it opens for both regression and classification.

Value

Returns a vector of variable numbers corresponding to the model having lowest prediction error.

Author(s)

Tahir Mehmood, Kristian Hovde Liland, Solve Sæbø.

References

T. Mehmood, H. Martens, S. Sæbø, J. Warringer, L. Snipen, A partial least squares based algorithm for parsimonious variable selection, Algorithms for Molecular Biology 6 (2011).

See Also

VIP (SR/sMC/LW/RC), filterPLSR, shaving, stpls, truncation, bve_pls, ga_pls, ipw_pls, mcuve_pls, rep_pls, spa_pls, lda_from_pls, lda_from_pls_cv, setDA.

Examples

data(gasoline, package = "pls")
## Not run: 
with( gasoline, rep_pls(octane, NIR) )

## End(Not run)

Set chosen Discriminant Analysis

Description

The default methods is LDA, but QDA and column of maximum prediction can be chosen.

Usage

setDA(LQ = NULL)

Arguments

Value

Returns the default set method.

See Also

VIP (SR/sMC/LW/RC), filterPLSR, shaving, stpls, truncation, bve_pls, ga_pls, ipw_pls, mcuve_pls, rep_pls, spa_pls, lda_from_pls, lda_from_pls_cv, setDA.

Examples

## Not run: 
setDA() # Query 'lda', 'qda' or 'max'
setDA('qda') # Set default method to QDA

## End(Not run)

Repeated shaving of variables

Description

One of five filter methods can be chosen for repeated shaving of a certain percentage of the worst performing variables. Performance of the reduced models are stored and viewable through print and plot methods.

Usage

shaving(
  y,
  X,
  ncomp = 10,
  method = c("SR", "VIP", "sMC", "LW", "RC"),
  prop = 0.2,
  min.left = 2,
  comp.type = c("CV", "max"),
  validation = c("CV", 1),
  fixed = integer(0),
  newy = NULL,
  newX = NULL,
  segments = 10,
  plsType = "plsr",
  Y.add = NULL,
  ...
)

## S3 method for class 'shaved'
plot(x, y, what = c("error", "spectra"), index = "min", log = "x", ...)

## S3 method for class 'shaved'
print(x, ...)

Arguments

Details

Variables are first sorted with respect to some importancemeasure, and usually one of the filter measures described above are used. Secondly, a threshold is used to eliminate a subset of the least informative variables. Then a model is fitted again to the remaining variables and performance is measured. The procedure is repeated until maximum model performance is achieved.

Value

Returns a list object of class shaved containing the method type, the error, number of components, and number of variables per reduced model. It also contains a list of all sets of reduced variable sets plus the original data.

Author(s)

Kristian Hovde Liland

See Also

VIP (SR/sMC/LW/RC), filterPLSR, shaving, stpls, truncation, bve_pls, ga_pls, ipw_pls, mcuve_pls, rep_pls, spa_pls, lda_from_pls, lda_from_pls_cv, setDA.

Examples

data(mayonnaise, package = "pls")
sh <- shaving(mayonnaise$design[,1], pls::msc(mayonnaise$NIR), type = "interleaved")
pars <- par(mfrow = c(2,1), mar = c(4,4,1,1))
plot(sh)
plot(sh, what = "spectra")
par(pars)
print(sh)

Simulate classes

Description

Simulate multivariate normal data.

Usage

simulate_classes(p, n1, n2)

simulate_data(dims, n1 = 150, n2 = 50)

Arguments

Details

The class simulation is a straigh forward simulation of mulitvariate normal data into two classes for training and test data, respectively. The data simulation uses a strictly structured multivariate normal simulation for with continuous response data.

Value

Returns a list of predictor and response data for training and testing.

Author(s)

Tahir Mehmood, Kristian Hovde Liland, Solve Sæbø.

References

T. Mehmood, K.H. Liland, L. Snipen, S. Sæbø, A review of variable selection methods in Partial Least Squares Regression, Chemometrics and Intelligent Laboratory Systems 118 (2012) 62-69. T. Mehmood, S. Sæbø, K.H. Liland, Comparison of variable selection methods in partial least squares regression, Journal of Chemometrics 34 (2020) e3226.

See Also

VIP (SR/sMC/LW/RC), filterPLSR, shaving, stpls, truncation, bve_pls, ga_pls, ipw_pls, mcuve_pls, rep_pls, spa_pls, lda_from_pls, lda_from_pls_cv, setDA.

Examples

str(simulate_classes(5,4,4))

Sub-window permutation analysis coupled with PLS (SwPA-PLS)

Description

SwPA-PLS provides the influence of each variable without considering the influence of the rest of the variables through sub-sampling of samples and variables.

Usage

spa_pls(y, X, ncomp = 10, N = 3, ratio = 0.8, Qv = 10, SPA.threshold = 0.05)

Arguments

Value

Returns a vector of variable numbers corresponding to the model having lowest prediction error.

Author(s)

Tahir Mehmood, Kristian Hovde Liland, Solve Sæbø.

References

H. Li, M. Zeng, B. Tan, Y. Liang, Q. Xu, D. Cao, Recipe for revealing informative metabolites based on model population analysis, Metabolomics 6 (2010) 353-361. http://code.google.com/p/spa2010/downloads/list.

See Also

VIP (SR/sMC/LW/RC), filterPLSR, shaving, stpls, truncation, bve_pls, ga_pls, ipw_pls, mcuve_pls, rep_pls, spa_pls, lda_from_pls, lda_from_pls_cv, setDA.

Examples

data(gasoline, package = "pls")
with( gasoline, spa_pls(octane, NIR) )

Soft-Threshold PLS (ST-PLS)

Description

A soft-thresholding step in PLS algorithm (ST-PLS) based on ideas from the nearest shrunken centroid method.

Usage

stpls(..., method = c("stpls", "model.frame"))

Arguments

Details

The ST-PLS approach is more or less identical to the Sparse-PLS presented independently by Lè Cao et al. This implementation is an expansion of code from the pls package. Arguments for stpls.fit include ncomp and shrink, where the forme sets then number of components and the latter is the shrinkage parameter indicating how large proportion of the maximum absolute value of the loadings that should be subtracted from the loadings in the nearest shrunken centroid method.

Value

Returns an object of class mvrV, simliar to to mvr object of the pls package.

Author(s)

Solve Sæbø, Tahir Mehmood, Kristian Hovde Liland.

References

S. Sæbø, T. Almøy, J. Aarøe, A.H. Aastveit, ST-PLS: a multi-dimensional nearest shrunken centroid type classifier via pls, Journal of Chemometrics 20 (2007) 54-62.

See Also

VIP (SR/sMC/LW/RC), filterPLSR, shaving, stpls, truncation, bve_pls, ga_pls, ipw_pls, mcuve_pls, rep_pls, spa_pls, lda_from_pls, lda_from_pls_cv, setDA.

Examples

data(yarn, package = "pls")
st <- stpls(density~NIR, ncomp=5, shrink=c(0.1,0.2), validation="CV", data=yarn)
summary(st)

Summary method for stpls and trunc

Description

Adaptation of summary.mvr from the pls package v 2.4.3.

Usage

## S3 method for class 'mvrV'
summary(
  object,
  what = c("all", "validation", "training"),
  digits = 4,
  print.gap = 2,
  ...
)

Arguments

See Also

summary.mvr

Trunction PLS

Description

Distribution based truncation for variable selection in subspace methods for multivariate regression.

Usage

truncation(..., Y.add, weights, method = "truncation")

Arguments

Details

Loading weights are truncated around their median based on confidence intervals for modelling without replicates (Lenth et al.). The arguments passed to mvrV include all possible arguments to cppls and the following truncation parameters (with defaults) trunc.pow=FALSE, truncation=NULL, trunc.width=NULL, trunc.weight=0, reorth=FALSE, symmetric=FALSE.

The default way of performing truncation involves the following parameter values: truncation="Lenth", trunc.width=0.95, indicating Lenth's confidence intervals (assymmetric), with a confidence of 95 shrinkage instead of a hard threshold. An alternative truncation strategy can be used with: truncation="quantile", in which a quantile line is used for detecting outliers/inliers.

Value

Returns an object of class mvrV, simliar to to mvr object of the pls package.

Author(s)

Kristian Hovde Liland.

References

K.H. Liland, M. Høy, H. Martens, S. Sæbø: Distribution based truncation for variable selection in subspace methods for multivariate regression, Chemometrics and Intelligent Laboratory Systems 122 (2013) 103-111.

See Also

VIP (SR/sMC/LW/RC), filterPLSR, shaving, stpls, truncation, bve_pls, ga_pls, ipw_pls, mcuve_pls, rep_pls, spa_pls, lda_from_pls, lda_from_pls_cv, setDA.

Examples

data(yarn, package = "pls")
tr <- truncation(density ~ NIR, ncomp=5, data=yarn, validation="CV",
 truncation="Lenth", trunc.width=0.95) # Default truncation
summary(tr)