# Variable Importance in Projection (VIP) `vip` calculates the Variable Importance in Projection (VIP) for a PLS model. UTF-8 ```r 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. ## Returns 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 ```r ## 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) ```

vip function