vip {rchemo}

Variable Importance in Projection (VIP)

Description

vip calculates the Variable Importance in Projection (VIP) for a PLS model.

Usage

vip(object, X, Y = NULL, nlv = NULL)

Arguments

object

A fitted model, output of a call to a fitting function among plskern, plsnipals, plsrannar, plsrda, plslda), plsqda).

X

X-data involved in the fitted model

Y

Y-data involved in the fitted model. If Y is NULL (default value), the VIP calculation is based on the proportion of Y-variance explained by the components, as proposed by Mehmood et al (2012, 2020). If Y is not NULL, the VIP calculation is based on the redundancy, as proposed by Tenenhaus (1998).

nlv

Number of components (LVs) to consider.

Value

matrix ((q,nlv)) with VIP values, for models with 1 to nlv latent variables.

References

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

Mehmood, T., Sæbø, S.,Liland, K.H., 2020. Comparison of variable selection methods in partial least squares regression. Journal of Chemometrics, 34, e3226.

Tenenhaus, M., 1998. La régression PLS: théorie et pratique. Editions Technip, Paris, France.

Examples

## EXAMPLE OF PLS

n <- 50 ; p <- 4
Xtrain <- matrix(rnorm(n * p), ncol = p)
ytrain <- rnorm(n)
Ytrain <- cbind(y1 = ytrain, y2 = 100 * ytrain)
m <- 3
Xtest <- Xtrain[1:m, , drop = FALSE] 
Ytest <- Ytrain[1:m, , drop = FALSE] ; ytest <- Ytest[1:m, 1]

nlv <- 3
fm <- plskern(Xtrain, Ytrain, nlv = nlv)
vip(fm, Xtrain, Ytrain, nlv = nlv)
vip(fm, Xtrain, nlv = nlv)

fm <- plskern(Xtrain, ytrain, nlv = nlv)
vip(fm, Xtrain, ytrain, nlv = nlv)
vip(fm, Xtrain, nlv = nlv)

## EXAMPLE OF PLSDA

n <- 50 ; p <- 8
Xtrain <- matrix(rnorm(n * p), ncol = p)
ytrain <- sample(c("1", "4", "10"), size = n, replace = TRUE)

Xtest <- Xtrain[1:5, ] ; ytest <- ytrain[1:5]

nlv <- 5
fm <- plsrda(Xtrain, ytrain, nlv = nlv)
vip(fm, Xtrain, ytrain, nlv = nlv)