filterPLSR: Optimisation of filters for Partial Least Squares

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

View source: R/filters.R

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

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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

y

vector of response values (numeric or factor).

X

numeric predictor matrix.

ncomp

integer number of components (default = 10).

ncomp.opt

use the number of components corresponding to minimum error (minimum) or ncomp (same).

validation

type of validation in the PLS modelling (default = "LOO").

LW.threshold

threshold for Loading Weights if applied (default = NULL).

RC.threshold

threshold for Regression Coefficients if applied (default = NULL).

URC.threshold

threshold for Unit normalized Regression Coefficients if applied (default = NULL).

FRC.threshold

threshold for Fitness normalized Regression Coefficients if applied (default = NULL).

JT.threshold

threshold for Jackknife Testing if applied (default = NULL).

VIP.threshold

threshold for Variable Importance on Projections if applied (default = NULL).

SR.threshold

threshold for Selectivity Ration if applied (default = NULL).

sMC.threshold

threshold for Significance Multivariate Correlation if applied (default = NULL).

mRMR.threshold

threshold for minimum Redundancy Maximum Releveance if applied (default = NULL).

WVC.threshold

threshold for Weighted Variable Contribution if applied (default = NULL).

...

additional paramters for pls, e.g. segmentation or similar.

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

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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)

plsVarSel documentation built on April 3, 2020, 9:08 a.m.