lwplsrda: KNN-LWPLS-DA Models

In mlesnoff/rchemo: Dimension reduction, Regression and Discrimination for Chemometrics

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 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	A fitted model, output of a call to the main function.
...	Optional arguments. Not used.

Value

See the examples.

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

mlesnoff/rchemo documentation built on April 15, 2023, 1:25 p.m.