knnda: KNN-DA

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

KNN-DA

Description

KNN weighted discrimination. For each new observation to predict, a number of k nearest neighbors is selected and the prediction is calculated by the the most frequent class in y in this neighborhood.

In function knnda, the dissimilarities used for computing the neighborhood and the weights can be calculated from the original X-data or after a dimension reduction (argument nlvdis). In the last case, global PLS scores are computed from (X, Y) and the dissimilarities are calculated on these scores. For high dimension X-data, the dimension reduction is in general required for using the Mahalanobis distance.

Usage

knnda(X, y,
    nlvdis, diss = c("eucl", "mahal"),
    h, k)

## S3 method for class 'Knnda'
predict(object, X, ...)  

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

Value

See the examples.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

Examples

n <- 50 ; p <- 8
Xtrain <- matrix(rnorm(n * p), ncol = p)
ytrain <- sample(c(1, 4, 10), size = n, replace = TRUE)
#ytrain <- sample(c("a", "10", "d"), size = n, replace = TRUE)
m <- 5
Xtest <- Xtrain[1:m, ] ; ytest <- ytrain[1:m]

nlvdis <- 5 ; diss <- "mahal"
h <- 2 ; k <- 10
fm <- knnda(
    Xtrain, ytrain, 
    nlvdis = nlvdis, diss = diss,
    h = h, k = k
    )
res <- predict(fm, Xtest)
names(res)
res$pred
err(res$pred, ytest)

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