mixOmics (version 5.1.2)

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" or "spls".

Value

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

encoding

latin1

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

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)