| Type: | Package |
| Title: | Discriminant Adaptive Nearest Neighbor Classification |
| Version: | 1.0.2 |
| Description: | Discriminant Adaptive Nearest Neighbor Classification is a variation of k nearest neighbors where the shape of the neighborhood is data driven. This package implements dann and sub_dann from Hastie (1996) https://web.stanford.edu/~hastie/Papers/dann_IEEE.pdf. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| Imports: | MASS (≥ 7.3), stats (≥ 3.5.3), tibble (≥ 2.1.1), ggplot2 (≥ 3.1.1), stringr (≥ 1.4.0), purrr (≥ 0.3.2), rlang (≥ 1.0.0), fpc (≥ 2.1-11.1), Rcpp (≥ 1.0.1), hardhat |
| RoxygenNote: | 7.3.2 |
| Suggests: | testthat (≥ 3.0.0), rmarkdown (≥ 1.18), mlbench (≥ 2.1-1), dplyr (≥ 0.8.0.1), magrittr (≥ 1.5), recipes |
| LinkingTo: | Rcpp, RcppArmadillo |
| Config/testthat/edition: | 3 |
| URL: | https://github.com/gmcmacran/dann |
| BugReports: | https://github.com/gmcmacran/dann/issues |
| NeedsCompilation: | yes |
| Packaged: | 2025-04-12 20:28:20 UTC; ixi_eulogy_ixi |
| Author: | Greg McMahan [aut, cre] |
| Maintainer: | Greg McMahan <gmcmacran@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-04-12 21:00:05 UTC |
Computes the distance between x0 and x1 using the DANN metric
Description
Computes the distance between x0 and x1 using the DANN metric
Usage
DANN_distance(x0, x1, sigma)
Arguments
x0 |
A numeric matrix with training predictors as columns. |
x1 |
A numeric matrix with training predictors as columns. |
sigma |
A numeric matrix defined in Hastie's DANN publication. |
Computes mode. Code found at Stack Overflow
Description
Computes mode. Code found at Stack Overflow
Usage
MODE(x)
Arguments
x |
A numeric vector. |
Computes class proportions
Description
Computes class proportions
Usage
class_proportions(x, possibleValues)
Arguments
x |
A numeric vector. |
possibleValues |
A vector of all possible values x can contain |
Discriminant Adaptive Nearest Neighbor Classification
Description
Discriminant Adaptive Nearest Neighbor Classification
Usage
dann(x, ..., k = 5, neighborhood_size = max(floor(nrow(x)/5), 50), epsilon = 1)
Arguments
x |
A matrix or a dataframe. |
... |
Additional parameters passed to methods. |
k |
The number of data points used for final classification. |
neighborhood_size |
The number of data points used to calculate between and within class covariance. |
epsilon |
Diagonal elements of a diagonal matrix. 1 is the identity matrix. |
Details
This is an implementation of Hastie and Tibshirani's Discriminant Adaptive Nearest Neighbor Classification publication..
Value
An S3 class of type dann.
Discriminant Adaptive Nearest Neighbor Classification
Description
Discriminant Adaptive Nearest Neighbor Classification
Usage
## S3 method for class 'data.frame'
dann(
x,
y,
k = 5,
neighborhood_size = max(floor(nrow(x)/5), 50),
epsilon = 1,
...
)
Arguments
x |
A data frame. |
y |
A vector. |
k |
The number of data points used for final classification. |
neighborhood_size |
The number of data points used to calculate between and within class covariance. |
epsilon |
Diagonal elements of a diagonal matrix. 1 is the identity matrix. |
... |
Additional parameters passed to methods. |
Details
This is an implementation of Hastie and Tibshirani's Discriminant Adaptive Nearest Neighbor Classification publication..
Value
An S3 class of type dann.
Examples
library(dann)
library(mlbench)
library(magrittr)
library(dplyr)
set.seed(1)
train <- mlbench.circle(300, 2) %>%
tibble::as_tibble()
colnames(train) <- c("X1", "X2", "Y")
y <- train$Y
x <- train[, 1:2]
dann(x, y)
Discriminant Adaptive Nearest Neighbor Classification
Description
Discriminant Adaptive Nearest Neighbor Classification
Usage
## Default S3 method:
dann(x, k = 5, neighborhood_size = max(floor(nrow(x)/5), 50), epsilon = 1, ...)
Arguments
x |
A data frame. |
k |
The number of data points used for final classification. |
neighborhood_size |
The number of data points used to calculate between and within class covariance. |
epsilon |
Diagonal elements of a diagonal matrix. 1 is the identity matrix. |
... |
Additional parameters passed to methods. |
Details
This is an implementation of Hastie and Tibshirani's Discriminant Adaptive Nearest Neighbor Classification publication..
Value
An S3 class of type dann.
Discriminant Adaptive Nearest Neighbor Classification
Description
Discriminant Adaptive Nearest Neighbor Classification
Usage
## S3 method for class 'formula'
dann(
formula,
data,
k = 5,
neighborhood_size = max(floor(nrow(data)/5), 50),
epsilon = 1,
...
)
Arguments
formula |
A formula. Y ~ X1 + X2 |
data |
A data frame. |
k |
The number of data points used for final classification. |
neighborhood_size |
The number of data points used to calculate between and within class covariance. |
epsilon |
Diagonal elements of a diagonal matrix. 1 is the identity matrix. |
... |
Additional parameters passed to methods. |
Details
This is an implementation of Hastie and Tibshirani's Discriminant Adaptive Nearest Neighbor Classification publication..
Value
An S3 class of type dann.
Examples
library(dann)
library(mlbench)
library(magrittr)
library(dplyr)
set.seed(1)
train <- mlbench.circle(300, 2) %>%
tibble::as_tibble()
colnames(train) <- c("X1", "X2", "Y")
dann(Y ~ X1 + X2, train)
Discriminant Adaptive Nearest Neighbor Classification
Description
Discriminant Adaptive Nearest Neighbor Classification
Usage
## S3 method for class 'matrix'
dann(
x,
y,
k = 5,
neighborhood_size = max(floor(nrow(x)/5), 50),
epsilon = 1,
...
)
Arguments
x |
A matrix. |
y |
A vector. |
k |
The number of data points used for final classification. |
neighborhood_size |
The number of data points used to calculate between and within class covariance. |
epsilon |
Diagonal elements of a diagonal matrix. 1 is the identity matrix. |
... |
Additional parameters passed to methods. |
Details
This is an implementation of Hastie and Tibshirani's Discriminant Adaptive Nearest Neighbor Classification publication..
Value
An S3 class of type dann.
Examples
library(dann)
library(mlbench)
library(magrittr)
library(dplyr)
set.seed(1)
train <- mlbench.circle(300, 2) %>%
tibble::as_tibble()
colnames(train) <- c("X1", "X2", "Y")
y <- as.numeric(train$Y)
x <- cbind(train$X1, train$X2)
dann(x, y)
Discriminant Adaptive Nearest Neighbor Classification
Description
Discriminant Adaptive Nearest Neighbor Classification
Usage
## S3 method for class 'recipe'
dann(
x,
data,
k = 5,
neighborhood_size = max(floor(nrow(data)/5), 50),
epsilon = 1,
...
)
Arguments
x |
A recipe from recipes library. |
data |
A data frame. |
k |
The number of data points used for final classification. |
neighborhood_size |
The number of data points used to calculate between and within class covariance. |
epsilon |
Diagonal elements of a diagonal matrix. 1 is the identity matrix. |
... |
Additional parameters passed to methods. |
Details
This is an implementation of Hastie and Tibshirani's Discriminant Adaptive Nearest Neighbor Classification publication..
Value
An S3 class of type dann.
Examples
library(dann)
library(mlbench)
library(magrittr)
library(dplyr)
library(recipes)
set.seed(1)
train <- mlbench.circle(300, 2) %>%
tibble::as_tibble()
colnames(train) <- c("X1", "X2", "Y")
rec_obj <- recipe(Y ~ X1 + X2, data = train)
dann(rec_obj, train)
A helper for sub_dann
Description
A helper for sub_dann
Usage
graph_eigenvalues(
x,
...,
neighborhood_size = max(floor(nrow(x)/5), 50),
weighted = FALSE,
sphere = "mcd"
)
Arguments
x |
A matrix or a dataframe. |
... |
Additional parameters passed to methods. |
neighborhood_size |
The number of data points used to calculate between and within class covariance. |
weighted |
weighted argument to ncoord. See |
sphere |
One of "mcd", "mve", "classical", or "none" See |
Details
This function plots the eigenvalues found by fpc::ncoord(). The user
should make a judgement call on how many eigenvalues are large and set sub_dann's
numDim to that number.
Value
A ggplot2 graph.
A helper for sub_dann
Description
A helper for sub_dann
Usage
## S3 method for class 'data.frame'
graph_eigenvalues(
x,
y,
neighborhood_size = max(floor(nrow(x)/5), 50),
weighted = FALSE,
sphere = "mcd",
...
)
Arguments
x |
A data frame. |
y |
A vector. |
neighborhood_size |
The number of data points used to calculate between and within class covariance. |
weighted |
weighted argument to ncoord. See |
sphere |
One of "mcd", "mve", "classical", or "none" See |
... |
Additional parameters passed to methods. |
Details
This function plots the eigenvalues found by fpc::ncoord(). The user
should make a judgement call on how many eigenvalues are large and set sub_dann's
numDim to that number.
Value
A ggplot2 graph.
Examples
library(dann)
library(mlbench)
library(magrittr)
library(dplyr)
set.seed(1)
train <- mlbench.circle(300, 2) %>%
tibble::as_tibble()
colnames(train) <- c("X1", "X2", "Y")
#' # Add 5 unrelated variables
train <- train %>%
mutate(
U1 = runif(300, -1, 1),
U2 = runif(300, -1, 1),
U3 = runif(300, -1, 1),
U4 = runif(300, -1, 1),
U5 = runif(300, -1, 1)
)
y <- train$Y
x <- cbind(train[, 1:2], train[, 4:8])
graph_eigenvalues(x, y)
A helper for sub_dann
Description
A helper for sub_dann
Usage
## Default S3 method:
graph_eigenvalues(
x,
neighborhood_size = max(floor(nrow(x)/5), 50),
weighted = FALSE,
sphere = "mcd",
...
)
Arguments
x |
A data frame. |
neighborhood_size |
The number of data points used to calculate between and within class covariance. |
weighted |
weighted argument to ncoord. See |
sphere |
One of "mcd", "mve", "classical", or "none" See |
... |
Additional parameters passed to methods. |
Details
This function plots the eigenvalues found by fpc::ncoord(). The user
should make a judgement call on how many eigenvalues are large and set sub_dann's
numDim to that number.
Value
A ggplot2 graph.
A helper for sub_dann
Description
A helper for sub_dann
Usage
## S3 method for class 'formula'
graph_eigenvalues(
formula,
data,
neighborhood_size = max(floor(nrow(data)/5), 50),
weighted = FALSE,
sphere = "mcd",
...
)
Arguments
formula |
A formula. Y ~ X1 + X1 |
data |
A data frame. |
neighborhood_size |
The number of data points used to calculate between and within class covariance. |
weighted |
weighted argument to ncoord. See |
sphere |
One of "mcd", "mve", "classical", or "none" See |
... |
Additional parameters passed to methods. |
Details
This function plots the eigenvalues found by fpc::ncoord(). The user
should make a judgement call on how many eigenvalues are large and set sub_dann's
numDim to that number.
Value
A ggplot2 graph.
Examples
library(dann)
library(mlbench)
library(magrittr)
library(dplyr)
set.seed(1)
train <- mlbench.circle(300, 2) %>%
tibble::as_tibble()
colnames(train) <- c("X1", "X2", "Y")
# Add 5 unrelated variables
train <- train %>%
mutate(
U1 = runif(300, -1, 1),
U2 = runif(300, -1, 1),
U3 = runif(300, -1, 1),
U4 = runif(300, -1, 1),
U5 = runif(300, -1, 1)
)
graph_eigenvalues(Y ~ X1 + X2 + U1 + U2 + U3 + U4 + U5, train)
A helper for sub_dann
Description
A helper for sub_dann
Usage
## S3 method for class 'matrix'
graph_eigenvalues(
x,
y,
neighborhood_size = max(floor(nrow(x)/5), 50),
weighted = FALSE,
sphere = "mcd",
...
)
Arguments
x |
A matrix. |
y |
A vector. |
neighborhood_size |
The number of data points used to calculate between and within class covariance. |
weighted |
weighted argument to ncoord. See |
sphere |
One of "mcd", "mve", "classical", or "none" See |
... |
Additional parameters passed to methods. |
Details
This function plots the eigenvalues found by fpc::ncoord(). The user
should make a judgement call on how many eigenvalues are large and set sub_dann's
numDim to that number.
Value
A ggplot2 graph.
Examples
library(dann)
library(mlbench)
library(magrittr)
library(dplyr)
set.seed(1)
train <- mlbench.circle(300, 2) %>%
tibble::as_tibble()
colnames(train) <- c("X1", "X2", "Y")
# Add 5 unrelated variables
train <- train %>%
mutate(
U1 = runif(300, -1, 1),
U2 = runif(300, -1, 1),
U3 = runif(300, -1, 1),
U4 = runif(300, -1, 1),
U5 = runif(300, -1, 1)
)
y <- as.numeric(train$Y)
x <- cbind(train$X1, train$X2, train$U1, train$U2, train$U3, train$U4, train$U5)
graph_eigenvalues(x, y)
A helper for sub_dann
Description
A helper for sub_dann
Usage
## S3 method for class 'recipe'
graph_eigenvalues(
x,
data,
neighborhood_size = max(floor(nrow(data)/5), 50),
weighted = FALSE,
sphere = "mcd",
...
)
Arguments
x |
A recipe from recipes library. |
data |
A data frame. |
neighborhood_size |
The number of data points used to calculate between and within class covariance. |
weighted |
weighted argument to ncoord. See |
sphere |
One of "mcd", "mve", "classical", or "none" See |
... |
Additional parameters passed to methods. |
Details
This function plots the eigenvalues found by fpc::ncoord(). The user
should make a judgement call on how many eigenvalues are large and set sub_dann's
numDim to that number.
Value
A ggplot2 graph.
Examples
library(dann)
library(mlbench)
library(magrittr)
library(dplyr)
library(recipes)
set.seed(1)
train <- mlbench.circle(300, 2) %>%
tibble::as_tibble()
colnames(train) <- c("X1", "X2", "Y")
# Add 5 unrelated variables
train <- train %>%
mutate(
U1 = runif(300, -1, 1),
U2 = runif(300, -1, 1),
U3 = runif(300, -1, 1),
U4 = runif(300, -1, 1),
U5 = runif(300, -1, 1)
)
rec_obj <- recipe(Y ~ X1 + X2 + U1 + U2 + U3 + U4 + U5, data = train)
graph_eigenvalues(rec_obj, train)
Discriminant Adaptive Nearest Neighbor Classification
Description
Discriminant Adaptive Nearest Neighbor Classification
Usage
## S3 method for class 'dann'
predict(object, new_data, type = "class", ...)
Arguments
object |
of class inheriting from "dann" |
new_data |
A data frame. |
type |
Type of prediction. (class, prob) |
... |
unused |
Details
This is an implementation of Hastie and Tibshirani's Discriminant Adaptive Nearest Neighbor Classification publication..
Value
A data frame containing either class or class probabilities. Adheres to tidy models standards.
Examples
library(dann)
library(mlbench)
library(magrittr)
library(dplyr)
set.seed(1)
train <- mlbench.circle(300, 2) %>%
tibble::as_tibble()
colnames(train) <- c("X1", "X2", "Y")
test <- mlbench.circle(300, 2) %>%
tibble::as_tibble()
colnames(test) <- c("X1", "X2", "Y")
model <- dann(Y ~ X1 + X2, train)
predict(model, test, "class")
predict(model, test, "prob")
Discriminant Adaptive Nearest Neighbor With Subspace Reduction
Description
Discriminant Adaptive Nearest Neighbor With Subspace Reduction
Usage
## S3 method for class 'sub_dann'
predict(object, new_data, type = "class", ...)
Arguments
object |
of class inheriting from "sub_dann" |
new_data |
A data frame. |
type |
Type of prediction. (class, prob) |
... |
unused |
Details
An implementation of Hastie and Tibshirani's sub-dann in section 4.1 of Discriminant Adaptive Nearest Neighbor Classification publication..
dann's performance suffers when noise variables are included in the model. Simulations show sub_dann will generally be more performant in this scenario.
Value
A data frame containing either class or class probabilities. Adheres to tidy models standards.
Examples
library(dann)
library(mlbench)
library(magrittr)
library(dplyr)
set.seed(1)
train <- mlbench.circle(300, 2) %>%
tibble::as_tibble()
colnames(train) <- c("X1", "X2", "Y")
test <- mlbench.circle(300, 2) %>%
tibble::as_tibble()
colnames(test) <- c("X1", "X2", "Y")
model <- sub_dann(Y ~ X1 + X2, train)
predict(model, test, "class")
predict(model, test, "prob")
Print dann model
Description
Print dann model
Usage
## S3 method for class 'dann'
print(x, ...)
Arguments
x |
a dann model. |
... |
arguments passed to other methods. |
Examples
library(dann)
library(mlbench)
library(magrittr)
library(dplyr)
set.seed(1)
train <- mlbench.circle(300, 2) %>%
tibble::as_tibble()
colnames(train) <- c("X1", "X2", "Y")
model <- dann(Y ~ X1 + X2, train)
print(model)
Print dann model.
Description
Print dann model.
Usage
## S3 method for class 'sub_dann'
print(x, ...)
Arguments
x |
a sub_dann model |
... |
arguments passed to other methods. |
Examples
library(dann)
library(mlbench)
library(magrittr)
library(dplyr)
set.seed(1)
train <- mlbench.circle(300, 2) %>%
tibble::as_tibble()
colnames(train) <- c("X1", "X2", "Y")
model <- sub_dann(Y ~ X1 + X2, train)
print(model)
Discriminant Adaptive Nearest Neighbor With Subspace Reduction
Description
Discriminant Adaptive Nearest Neighbor With Subspace Reduction
Usage
sub_dann(
x,
...,
k = 5,
neighborhood_size = max(floor(nrow(x)/5), 50),
epsilon = 1,
weighted = FALSE,
sphere = "mcd",
numDim = ceiling(ncol(x)/2)
)
Arguments
x |
A matrix or a dataframe. |
... |
Additional parameters passed to methods. |
k |
The number of data points used for final classification. |
neighborhood_size |
The number of data points used to calculate between and within class covariance. |
epsilon |
Diagonal elements of a diagonal matrix. 1 is the identity matrix. |
weighted |
weighted argument to ncoord. See |
sphere |
One of "mcd", "mve", "classical", or "none" See |
numDim |
Dimension of subspace used by dann. See |
Details
An implementation of Hastie and Tibshirani's sub-dann in section 4.1 of Discriminant Adaptive Nearest Neighbor Classification publication..
dann's performance suffers when noise variables are included in the model. Simulations show sub_dann will generally be more performant in this scenario.
Value
An S3 class of type sub_dann
Discriminant Adaptive Nearest Neighbor With Subspace Reduction
Description
Discriminant Adaptive Nearest Neighbor With Subspace Reduction
Usage
## S3 method for class 'data.frame'
sub_dann(
x,
y,
k = 5,
neighborhood_size = max(floor(nrow(x)/5), 50),
epsilon = 1,
weighted = FALSE,
sphere = "mcd",
numDim = ceiling(ncol(x)/2),
...
)
Arguments
x |
A data frame. |
y |
A vector. |
k |
The number of data points used for final classification. |
neighborhood_size |
The number of data points used to calculate between and within class covariance. |
epsilon |
Diagonal elements of a diagonal matrix. 1 is the identity matrix. |
weighted |
weighted argument to ncoord. See |
sphere |
One of "mcd", "mve", "classical", or "none" See |
numDim |
Dimension of subspace used by dann. See |
... |
Additional parameters passed to methods. |
Details
An implementation of Hastie and Tibshirani's sub-dann in section 4.1 of Discriminant Adaptive Nearest Neighbor Classification publication..
dann's performance suffers when noise variables are included in the model. Simulations show sub_dann will generally be more performant in this scenario.
Value
An S3 class of type sub_dann
Examples
library(dann)
library(mlbench)
library(magrittr)
library(dplyr)
set.seed(1)
train <- mlbench.circle(300, 2) %>%
tibble::as_tibble()
colnames(train) <- c("X1", "X2", "Y")
y <- train$Y
x <- train[, 1:2]
sub_dann(x, y)
Discriminant Adaptive Nearest Neighbor With Subspace Reduction
Description
Discriminant Adaptive Nearest Neighbor With Subspace Reduction
Usage
## Default S3 method:
sub_dann(
x,
k = 5,
neighborhood_size = max(floor(nrow(x)/5), 50),
epsilon = 1,
weighted = FALSE,
sphere = "mcd",
numDim = ceiling(ncol(x)/2),
...
)
Arguments
x |
A data frame. |
k |
The number of data points used for final classification. |
neighborhood_size |
The number of data points used to calculate between and within class covariance. |
epsilon |
Diagonal elements of a diagonal matrix. 1 is the identity matrix. |
weighted |
weighted argument to ncoord. See |
sphere |
One of "mcd", "mve", "classical", or "none" See |
numDim |
Dimension of subspace used by dann. See |
... |
Additional parameters passed to methods. |
Details
An implementation of Hastie and Tibshirani's sub-dann in section 4.1 of Discriminant Adaptive Nearest Neighbor Classification publication..
dann's performance suffers when noise variables are included in the model. Simulations show sub_dann will generally be more performant in this scenario.
Value
An S3 class of type sub_dann
Discriminant Adaptive Nearest Neighbor With Subspace Reduction
Description
Discriminant Adaptive Nearest Neighbor With Subspace Reduction
Usage
## S3 method for class 'formula'
sub_dann(
formula,
data,
k = 5,
neighborhood_size = max(floor(nrow(data)/5), 50),
epsilon = 1,
weighted = FALSE,
sphere = "mcd",
numDim = ceiling(ncol(data)/2),
...
)
Arguments
formula |
A formula. Y ~ X1 + X2 |
data |
A data frame. |
k |
The number of data points used for final classification. |
neighborhood_size |
The number of data points used to calculate between and within class covariance. |
epsilon |
Diagonal elements of a diagonal matrix. 1 is the identity matrix. |
weighted |
weighted argument to ncoord. See |
sphere |
One of "mcd", "mve", "classical", or "none" See |
numDim |
Dimension of subspace used by dann. See |
... |
Additional parameters passed to methods. |
Details
An implementation of Hastie and Tibshirani's sub-dann in section 4.1 of Discriminant Adaptive Nearest Neighbor Classification publication..
dann's performance suffers when noise variables are included in the model. Simulations show sub_dann will generally be more performant in this scenario.
Value
An S3 class of type sub_dann
Examples
library(dann)
library(mlbench)
library(magrittr)
library(dplyr)
set.seed(1)
train <- mlbench.circle(300, 2) %>%
tibble::as_tibble()
colnames(train) <- c("X1", "X2", "Y")
sub_dann(Y ~ X1 + X2, train)
Discriminant Adaptive Nearest Neighbor With Subspace Reduction
Description
Discriminant Adaptive Nearest Neighbor With Subspace Reduction
Usage
## S3 method for class 'matrix'
sub_dann(
x,
y,
k = 5,
neighborhood_size = max(floor(nrow(x)/5), 50),
epsilon = 1,
weighted = FALSE,
sphere = "mcd",
numDim = ceiling(ncol(x)/2),
...
)
Arguments
x |
A matrix. |
y |
A vector. |
k |
The number of data points used for final classification. |
neighborhood_size |
The number of data points used to calculate between and within class covariance. |
epsilon |
Diagonal elements of a diagonal matrix. 1 is the identity matrix. |
weighted |
weighted argument to ncoord. See |
sphere |
One of "mcd", "mve", "classical", or "none" See |
numDim |
Dimension of subspace used by dann. See |
... |
Additional parameters passed to methods. |
Details
An implementation of Hastie and Tibshirani's sub-dann in section 4.1 of Discriminant Adaptive Nearest Neighbor Classification publication..
dann's performance suffers when noise variables are included in the model. Simulations show sub_dann will generally be more performant in this scenario.
Value
An S3 class of type sub_dann
Examples
library(dann)
library(mlbench)
library(magrittr)
library(dplyr)
set.seed(1)
train <- mlbench.circle(300, 2) %>%
tibble::as_tibble()
colnames(train) <- c("X1", "X2", "Y")
y <- as.numeric(train$Y)
x <- cbind(train$X1, train$X2)
sub_dann(x, y)
Discriminant Adaptive Nearest Neighbor With Subspace Reduction
Description
Discriminant Adaptive Nearest Neighbor With Subspace Reduction
Usage
## S3 method for class 'recipe'
sub_dann(
x,
data,
k = 5,
neighborhood_size = max(floor(nrow(data)/5), 50),
epsilon = 1,
weighted = FALSE,
sphere = "mcd",
numDim = ceiling(ncol(data)/2),
...
)
Arguments
x |
A recipe from recipes library. |
data |
A data frame. |
k |
The number of data points used for final classification. |
neighborhood_size |
The number of data points used to calculate between and within class covariance. |
epsilon |
Diagonal elements of a diagonal matrix. 1 is the identity matrix. |
weighted |
weighted argument to ncoord. See |
sphere |
One of "mcd", "mve", "classical", or "none" See |
numDim |
Dimension of subspace used by dann. See |
... |
Additional parameters passed to methods. |
Details
An implementation of Hastie and Tibshirani's sub-dann in section 4.1 of Discriminant Adaptive Nearest Neighbor Classification publication..
dann's performance suffers when noise variables are included in the model. Simulations show sub_dann will generally be more performant in this scenario.
Value
An S3 class of type sub_dann
Examples
library(dann)
library(mlbench)
library(magrittr)
library(dplyr)
library(recipes)
set.seed(1)
train <- mlbench.circle(300, 2) %>%
tibble::as_tibble()
colnames(train) <- c("X1", "X2", "Y")
rec_obj <- recipe(Y ~ X1 + X2, data = train)
sub_dann(rec_obj, train)