mixOmics (version 6.3.2)

vip: Variable Importance in the Projection (VIP)

Description

The function vip computes the influence on the \(Y\)-responses of every predictor \(X\) in the model.

Usage

vip(object)

Arguments

object

object of class inheriting from "pls", "plsda", "spls" or "splsda".

Value

vip produces a matrix of VIP coefficients for each \(X\) variable (rows) on each variate component (columns).

Details

Variable importance in projection (VIP) coefficients reflect the relative importance of each \(X\) variable for each \(X\) variate in the prediction model. VIP coefficients thus represent the importance of each \(X\) variable in fitting both the \(X\)- and \(Y\)-variates, since the \(Y\)-variates are predicted from the \(X\)-variates.

VIP allows to classify the \(X\)-variables according to their explanatory power of \(Y\). Predictors with large VIP, larger than 1, are the most relevant for explaining \(Y\).

References

Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: Editions Technic.

See Also

pls, spls, summary.

Examples

# NOT RUN {
data(linnerud)
X <- linnerud$exercise
Y <- linnerud$physiological
linn.pls <- pls(X, Y)

linn.vip <- vip(linn.pls)

barplot(linn.vip,
        beside = TRUE, col = c("lightblue", "mistyrose", "lightcyan"),
        ylim = c(0, 1.7), legend = rownames(linn.vip),
        main = "Variable Importance in the Projection", font.main = 4)
# }