Variable Importance in the Projection (VIP)
The function vip computes the influence on the Y-responses of
every predictor X in the model.
vip(object)
object |
object of class inheriting from |
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.
vip produces a matrix of VIP coefficients for each X
variable (rows) on each variate component (columns).
Sébastien Déjean, Ignacio Gonzalez, Florian Rohart, Al J Abadi
Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: Editions Technic.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.