# KNN-LWPLS-DA Models - `lwplsrda`: KNN-LWPLSRDA models. This is the same methodology as for `lwplsr` except that PLSR is replaced by PLSRDA (`plsrda`). See the help page of `lwplsr` for details. - `lwplslda` and `lwplsqda`: Same as above, but PLSRDA is replaced by either PLSLDA (`plslda`) or PLSQDA ((`plsqda`), respecively. latin1 ```r lwplsrda( X, y, nlvdis, diss = c("eucl", "mahal"), h, k, nlv, cri = 4, verb = FALSE ) lwplslda( X, y, nlvdis, diss = c("eucl", "mahal"), h, k, nlv, prior = c("unif", "prop"), cri = 4, verb = FALSE ) lwplsqda( X, y, nlvdis, diss = c("eucl", "mahal"), h, k, nlv, prior = c("unif", "prop"), cri = 4, verb = FALSE ) ## S3 method for class 'Lwplsrda' predict(object, X, ..., nlv = NULL) ## S3 method for class 'Lwplsprobda' predict(object, X, ..., nlv = NULL) ``` ## Arguments - `X`: For the main functions: Training X-data ($n, p$). --- For the auxiliary functions: New X-data ($m, p$) to consider. - `y`: Training class membership ($n$). Note: If `y` is a factor, it is replaced by a character vector. - `nlvdis`: The number of LVs to consider in the global PLS used for the dimension reduction before calculating the dissimilarities. If `nlvdis = 0`, there is no dimension reduction. - `diss`: The type of dissimilarity used for defining the neighbors. Possible values are "eucl" (default; Euclidean distance), "mahal" (Mahalanobis distance), or "correlation". Correlation dissimilarities are calculated by sqrt(.5 * (1 - rho)). - `h`: A scale scalar defining the shape of the weight function. Lower is $h$, sharper is the function. See `wdist`. - `k`: The number of nearest neighbors to select for each observation to predict. - `nlv`: The number(s) of LVs to calculate in the local PLSDA models. - `prior`: The prior probabilities of the classes. Possible values are "unif" (default; probabilities are set equal for all the classes) or "prop" (probabilities are set equal to the observed proportions of the classes in `y`). - `cri`: Argument `cri` in function `wdist`. - `verb`: Logical. If `TRUE`, fitting information are printed. - `object`: For the auxiliary functions: A fitted model, output of a call to the main function. - `...`: For the auxiliary functions: Optional arguments. Not used. ## Returns For `lwplsrda`, `lwplslda`, `lwplsqda`: object of class `Lwplsrda` or `Lwplsprobda`, For `predict.Lwplsrda`, `predict.Lwplsprobda` : - **pred**: class predicted for each observation - **listnn**: list with the neighbors used for each observation to be predicted - **listd**: list with the distances to the neighbors used for each observation to be predicted - **listw**: list with the weights attributed to the neighbors used for each observation to be predicted ## Examples ```r n <- 50 ; p <- 7 Xtrain <- matrix(rnorm(n * p), ncol = p) ytrain <- sample(c(1, 4, 10), size = n, replace = TRUE) m <- 4 Xtest <- matrix(rnorm(m * p), ncol = p) ytest <- sample(c(1, 4, 10), size = m, replace = TRUE) nlvdis <- 5 ; diss <- "mahal" h <- 2 ; k <- 10 nlv <- 2 fm <- lwplsrda( Xtrain, ytrain, nlvdis = nlvdis, diss = diss, h = h, k = k, nlv = nlv ) res <- predict(fm, Xtest) res$pred res$listnn err(res$pred, ytest) res <- predict(fm, Xtest, nlv = 0:2) res$pred ```

lwplsrda function