vcr.knn.newdata: Carry out a k-nearest neighbor classification on new data,...

In classmap: Visualizing Classification Results

Carry out a k-nearest neighbor classification on new data, and prepare to visualize its results.

Description

Predicts class labels for new data by k nearest neighbors, using the output of vcr.knn.train on the training data. For cases in the new data whose given label ynew is not NA, additional output is produced for constructing graphical displays such as the classmap.

Usage

vcr.knn.newdata(Xnew, ynew = NULL, vcr.knn.train.out, LOO = FALSE)

Arguments

Xnew	If the training data was a matrix of coordinates, Xnew must be such a matrix with the same number of columns. If the training data was a set of dissimilarities, Xnew must be a rectangular matrix of dissimilarities, with each row containing the dissmilarities of a new case to all training cases. Missing values are not allowed.
ynew	factor with class membership of each new case. Can be NA for some or all cases. If NULL, is assumed to be NA everywhere.
vcr.knn.train.out	output of vcr.knn.train on the training data.
LOO	leave one out. Only used when testing this function on a subset of the training data. Default is LOO=FALSE.

Value

A list with components:

yintnew	number of the given class of each case. Can contain NA's.
ynew	given class label of each case. Can contain NA's.
levels	levels of the response, from vcr.knn.train.out.
predint	predicted class number of each case. Always exists.
pred	predicted label of each case.
altint	number of the alternative class. Among the classes different from the given class, it is the one with the highest posterior probability. Is NA for cases whose ynew is missing.
altlab	label of the alternative class. Is NA for cases whose ynew is missing.
PAC	probability of the alternative class. Is NA for cases whose ynew is missing.
fig	distance of each case i from each class g. Always exists.
farness	farness of each case from its given class. Is NA for cases whose ynew is missing.
ofarness	for each case i, its lowest fig[i,g] to any class g. Always exists.
k	the requested number of nearest neighbors, from vcr.knn.train.out.
ktrues	for each case this contains the actual number of elements in its neighborhood. This can be higher than k due to ties.
counts	a matrix with 3 columns, each row representing a case. For the neighborhood of each case it says how many members it has from the given class, the predicted class, and the alternative class. The first and third entry is NA for cases whose ynew is missing.

Author(s)

Raymaekers J., Rousseeuw P.J.

References

Raymaekers J., Rousseeuw P.J., Hubert M. (2021). Class maps for visualizing classification results. Technometrics, appeared online. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00401706.2021.1927849")}(link to open access pdf)

See Also

vcr.knn.train, classmap, silplot, stackedplot

Examples

data("data_floralbuds")
X <- data_floralbuds[, 1:6]; y <- data_floralbuds[, 7]
set.seed(12345); trainset <- sample(1:550, 275)
vcr.train <- vcr.knn.train(X[trainset, ], y[trainset], k = 5)
vcr.test <- vcr.knn.newdata(X[-trainset, ], y[-trainset], vcr.train)
confmat.vcr(vcr.train) # for comparison
confmat.vcr(vcr.test)
cols <- c("saddlebrown", "orange", "olivedrab4", "royalblue3")
stackedplot(vcr.train, classCols = cols) # for comparison
stackedplot(vcr.test, classCols = cols)
classmap(vcr.train, "bud", classCols = cols) # for comparison
classmap(vcr.test, "bud", classCols = cols)

# For more examples, we refer to the vignette:
## Not run: 
vignette("K_nearest_neighbors_examples")

## End(Not run)

classmap documentation built on April 23, 2023, 5:09 p.m.