R: Variable Importance in the Projection (VIP)

vip {mixOmics} | R Documentation

## 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"`.

### 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.

### Value

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

### Author(s)

Sébastien Déjean and Ignacio Gonz\'alez.

### 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)

[Package mixOmics version 6.3.2 Index]