rchemo (version 0.1-3)

lwplsrda: KNN-LWPLS-DA Models

Description

- 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.

Usage

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 Lwplsrda
predict(object, X, ..., nlv = NULL)  
# S3 method for Lwplsprobda
predict(object, X, ..., nlv = NULL)

Value

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

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.

Examples

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