@@ -244,6 +244,8 @@ reduceTSNE <- function(x, ncomp = 2, normalize = TRUE, ...) {

 ##' * `projection`: The projection matrix.

 ##' * `fitted.values`: An array of fitted values.

 ##' * `residuals`: An array of regression residuals.

+##' * `vip`: An array of VIP (Variable Importance in the Projection)

+##' coefficients.

 ##' * `centered`: A logical indicating whether the data was mean-centered prior

 ##' to PLS-DA.

 ##' * `scaled`: A logical indicating whether the data was scaled prior to

@@ -316,7 +318,7 @@ reducePLSDA <- function(x, y, ncomp = 2, center = TRUE, scale = FALSE,

     attr(out, "predictors") <- pls::prednames(fit)

     attr(out, "coefficients") <- fit$coefficients

     attr(out, "loadings") <- fit$loadings

-    attr(out, "loadings.weights") <- fit$loadings.weights

+    attr(out, "loading.weights") <- fit$loading.weights

     attr(out, "Y.observed") <- y

     attr(out, "Y.predicted") <- y_predicted

     attr(out, "Y.scores") <- fit$Yscores

@@ -325,9 +327,24 @@ reducePLSDA <- function(x, y, ncomp = 2, center = TRUE, scale = FALSE,

     attr(out, "fitted.values") <- fitted(fit)

     attr(out, "residuals") <- residuals(fit)

     attr(out, "ncomp") <- fit$ncomp

+    attr(out, "vip") <- .vip_mat(fit)

     attr(out, "centered") <- center

     attr(out, "scaled") <- scale

     attr(out, "validation") <- fit$validation

     class(out) <- c("reduced.plsda", "matrix", class(out))

     out

 }

+

+.vip_mat <- function(fit) {

+  ncomp <- fit$ncomp

+  w <- fit$loading.weights

+  m <- cor(fit$model$y, fit$scores, use = "pairwise")**2

+  vip <- do.call(cbind, lapply(1:ncomp, function(x) .vip_vec(w, m, x)))

+  dimnames(vip) <- dimnames(w)

+  vip

+}

+

+.vip_vec <- function(w, m, comp) {

+  rd <- colSums(m[, 1:comp, drop = FALSE])

+  as.vector(sqrt(nrow(w) * rd %*% t(w[, 1:comp]^2) / sum(rd)))

+}

@@ -54,6 +54,8 @@ each component.

 \item \code{projection}: The projection matrix.

 \item \code{fitted.values}: An array of fitted values.

 \item \code{residuals}: An array of regression residuals.

+\item \code{vip}: An array of VIP (Variable Importance in the Projection)

+coefficients.

 \item \code{centered}: A logical indicating whether the data was mean-centered prior

 to PLS-DA.

 \item \code{scaled}: A logical indicating whether the data was scaled prior to