ipw_pls {plsVarSel}

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

y

vector of response values (numeric or factor).

X

numeric predictor matrix.

ncomp

integer number of components (default = 10).

no.iter

the number of iterations (default = 10).

IPW.threshold

threshold for regression coefficients (default = 0.1).

filter

which filtering method to use (among "RC", "SR", "LW", "VIP", "sMC")

scale

standardize data (default=TRUE, as in reference)

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) )