Package 'classmap'

Title:	Visualizing Classification Results
Description:	Tools to visualize the results of a classification of cases. The graphical displays include stacked plots, silhouette plots, quasi residual plots, and class maps. Implements the techniques described and illustrated in Raymaekers, Rousseeuw and Hubert (2021), Class maps for visualizing classification results, Technometrics, appeared online. <doi:10.1080/00401706.2021.1927849> (open access) and Raymaekers and Rousseeuw (2021), Silhouettes and quasi residual plots for neural nets and tree-based classifiers, <arXiv:2106.08814>. Examples can be found in the vignettes: "Discriminant_analysis_examples","K_nearest_neighbors_examples", "Support_vector_machine_examples", "Rpart_examples", "Random_forest_examples", and "Neural_net_examples".
Authors:	Jakob Raymaekers [aut, cre], Peter Rousseeuw [aut]
Maintainer:	Jakob Raymaekers <[email protected]>
License:	GPL (>= 2)
Version:	1.2.3
Built:	2024-11-16 04:39:13 UTC
Source:	https://github.com/cran/classmap

Help Index

Draw the class map to visualize classification results.
Build a confusion matrix from the output of a function vcr.*.*.
Amazon book reviews data
Floral buds data
Instagram data
Titanic data
Constructs feature vectors from a kernel matrix.
Compute kernel matrix
Draw a quasi residual plot of PAC versus a data feature
Draw the silhouette plot of a classification
Make a vertically stacked mosaic plot of class predictions.
Carry out discriminant analysis on new data, and prepare to visualize its results.
Carry out discriminant analysis on training data, and prepare to visualize its results.
Prepare for visualization of a random forest classification on new data.
Prepare for visualization of a random forest classification on training data
Carry out a k-nearest neighbor classification on new data, and prepare to visualize its results.
Carry out a k-nearest neighbor classification on training data, and prepare to visualize its results.
Prepare for visualization of a neural network classification on new data.
Prepare for visualization of a neural network classification on training data.
Prepare for visualization of an rpart classification on new data.
Prepare for visualization of an rpart classification on training data.
Prepare for visualization of a support vector machine classification on new data.
Prepare for visualization of a support vector machine classification on training data.

Draw the class map to visualize classification results.

Description

Draw the class map to visualize classification results, based on the output of one of the vcr.*.* functions in this package. The vertical axis of the class map shows each case's PAC, the conditional probability that it belongs to an alternative class. The farness on the horizontal axis is the probability of a member of the given class being at most as far from the class as the case itself.

Usage

classmap(vcrout, whichclass, classLabels = NULL, classCols = NULL,
         main = NULL, cutoff = 0.99, plotcutoff = TRUE,
         identify = FALSE, cex = 1, cex.main = 1.2, cex.lab = NULL,
         cex.axis = NULL, opacity = 1,
         squareplot = TRUE,  maxprob = NULL, maxfactor = NULL)
classmap(vcrout, whichclass, classLabels = NULL, classCols = NULL,
         main = NULL, cutoff = 0.99, plotcutoff = TRUE,
         identify = FALSE, cex = 1, cex.main = 1.2, cex.lab = NULL,
         cex.axis = NULL, opacity = 1,
         squareplot = TRUE,  maxprob = NULL, maxfactor = NULL)

Arguments

`vcrout`	output of `vcr..train` or `vcr..newdata`. Required.
`whichclass`	the number or level of the class to be displayed. Required.
`classLabels`	the labels (levels) of the classes. If `NULL`, they are taken from `vcrout`.
`classCols`	a list of colors for the class labels. There should be at least as many as there are levels. If `NULL` the `classCols` are taken as 2, 3, 4, ...
`main`	title for the plot.
`cutoff`	cases with overall farness `vcrout$ofarness` > `cutoff` are flagged as outliers.
`plotcutoff`	If true, plots the cutoff on the farness values as a vertical line.
`identify`	if `TRUE`, left-click on a point to get its number, then ESC to exit.
`cex`	passed on to `graphics::plot`.
`cex.main`	same, for title.
`cex.lab`	same, for labels on horizontal and vertical axes.
`cex.axis`	same, for axes.
`opacity`	determines opacity of plotted dots. Value between 0 and 1, where 0 is transparent and 1 is opaque.
`squareplot`	If `TRUE`, makes the axes of the plot equally long.
`maxprob`	draws the farness axis at least upto probability maxprob. If `NULL`, the limits are obtained automatically.
`maxfactor`	if not `NULL`, a number slightly higher than 1 to increase the space at the right hand side of the plot, to make room for marking points.

Value

Executing the function plots the class map and returns

coordinates

a matrix with 2 columns containing the coordinates of the plotted points. The first coordinate is the quantile of the farness probability. This makes it easier to add text next to interesting points. If identify = T, the attribute ids of coordinates contains the row numbers of the identified points in the matrix coordinates.

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. doi:10.1080/00401706.2021.1927849(link to open access pdf)

Raymaekers J., Rousseeuw P.J.(2021). Silhouettes and quasi residual plots for neural nets and tree-based classifiers. (link to open access pdf)

Examples

vcrout <- vcr.da.train(iris[, 1:4], iris[, 5])
classmap(vcrout, "setosa", classCols = 2:4) # tight class
classmap(vcrout, "versicolor", classCols = 2:4) # less tight
# The cases misclassified as virginica are shown in blue.
classmap(vcrout, "virginica", classCols = 2:4)
# The case misclassified as versicolor is shown in green.

# For more examples, we refer to the vignettes:
## Not run: 
vignette("Discriminant_analysis_examples")
vignette("K_nearest_neighbors_examples")
vignette("Support_vector_machine_examples")
vignette("Rpart_examples")
vignette("Random_forest_examples")
vignette("Neural_net_examples")

## End(Not run)
vcrout <- vcr.da.train(iris[, 1:4], iris[, 5])
classmap(vcrout, "setosa", classCols = 2:4) # tight class
classmap(vcrout, "versicolor", classCols = 2:4) # less tight
# The cases misclassified as virginica are shown in blue.
classmap(vcrout, "virginica", classCols = 2:4)
# The case misclassified as versicolor is shown in green.

# For more examples, we refer to the vignettes:
## Not run: 
vignette("Discriminant_analysis_examples")
vignette("K_nearest_neighbors_examples")
vignette("Support_vector_machine_examples")
vignette("Rpart_examples")
vignette("Random_forest_examples")
vignette("Neural_net_examples")

## End(Not run)

Build a confusion matrix from the output of a function `vcr..`.

Description

Build a confusion matrix from the output of a function vcr.*.*. Optionally, a separate column for outliers can be added to the confusion matrix.

Usage

confmat.vcr(vcrout, cutoff = 0.99, showClassNumbers = FALSE,
            showOutliers = TRUE, silent = FALSE)
confmat.vcr(vcrout, cutoff = 0.99, showClassNumbers = FALSE,
            showOutliers = TRUE, silent = FALSE)

Arguments

`vcrout`	output of `vcr..train` or `vcr..newdata`.
`cutoff`	cases with overall farness `vcrout$ofarness` > `cutoff` are flagged as outliers.
`showClassNumbers`	if `TRUE`, the row and column names are the number of each level instead of the level itself. Useful for long level names.
`showOutliers`	if `TRUE` and some points were flagged as outliers, it adds an extra column on the right of the confusion matrix for these outliers, with label "outl".
`silent`	if `FALSE`, the confusion matrix and accuracy are shown on the screen.

Value

A confusion matrix

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. doi:10.1080/00401706.2021.1927849(link to open access pdf)

Examples


vcrout <- vcr.knn.train(scale(iris[, 1:4]), iris[, 5], k = 5)
# The usual confusion matrix:
confmat.vcr(vcrout, showOutliers = FALSE)

# Cases with ofarness > cutoff are flagged as outliers:
confmat.vcr(vcrout, cutoff = 0.98)

# With the default cutoff = 0.99 only one case is flagged here:
confmat.vcr(vcrout)
# Note that the accuracy is computed before any cases
# are flagged, so it does not depend on the cutoff.

confmat.vcr(vcrout, showClassNumbers = TRUE)
# Shows class numbers instead of labels. This option can
# be useful for long level names.

# For more examples, we refer to the vignettes:
## Not run: 
vignette("Discriminant_analysis_examples")
vignette("K_nearest_neighbors_examples")
vignette("Support_vector_machine_examples")
vignette("Rpart_examples")
vignette("Random_forest_examples")
vignette("Neural_net_examples")

## End(Not run)
vcrout <- vcr.knn.train(scale(iris[, 1:4]), iris[, 5], k = 5)
# The usual confusion matrix:
confmat.vcr(vcrout, showOutliers = FALSE)

# Cases with ofarness > cutoff are flagged as outliers:
confmat.vcr(vcrout, cutoff = 0.98)

# With the default cutoff = 0.99 only one case is flagged here:
confmat.vcr(vcrout)
# Note that the accuracy is computed before any cases
# are flagged, so it does not depend on the cutoff.

confmat.vcr(vcrout, showClassNumbers = TRUE)
# Shows class numbers instead of labels. This option can
# be useful for long level names.

# For more examples, we refer to the vignettes:
## Not run: 
vignette("Discriminant_analysis_examples")
vignette("K_nearest_neighbors_examples")
vignette("Support_vector_machine_examples")
vignette("Rpart_examples")
vignette("Random_forest_examples")
vignette("Neural_net_examples")

## End(Not run)

Amazon book reviews data

Description

This is a subset of the data used in the paper, which was assembled by Prettenhofer and Stein (2010). It contains 1000 reviews of books on Amazon, of which 500 were selected from the original training data and 500 from the test data.

The full dataset has been used for a variety of things, including classification using svm. The subset was chosen small enough to keep the computation time low, while still containing the examples in the paper.

Usage

data("data_bookReviews")data("data_bookReviews")

Format

A data frame with 1000 observations on the following 2 variables.

review: the review in text format (character)
sentiment: factor indicating the sentiment of the review: negative (1) or positive (2)

Source

Prettenhofer, P., Stein, B. (2010). Cross-language text classification using structural correspondence learning. Proceedings of the 48th annual meeting of the association for computational linguistics, 1118-1127.

Examples

data(data_bookReviews)
# Example review:
data_bookReviews[5, 1]

# The data are used in:
## Not run: 
vignette("Support_vector_machine_examples")

## End(Not run)
data(data_bookReviews)
# Example review:
data_bookReviews[5, 1]

# The data are used in:
## Not run: 
vignette("Support_vector_machine_examples")

## End(Not run)

Floral buds data

Description

This data on floral pear bud detection was first described by Wouters et al. The goal is to classify the instances into buds, branches, scales and support. The numeric vectors resulted from a multispectral vision sensor and describe the scanned images.

Usage

data("data_floralbuds")data("data_floralbuds")

Format

A data frame with 550 observations on the following 7 variables.

X1: numeric vector
X2: numeric vector
X3: numeric vector
X4: numeric vector
X5: numeric vector
X6: numeric vector
y: a factor with levels branch bud scales support

Source

Wouters, N., De Ketelaere, B., Deckers, T. De Baerdemaeker, J., Saeys, W. (2015). Multispectral detection of floral buds for automated thinning of pear. Comput. Electron. Agric. 113, C, 93–103. <doi:10.1016/j.compag.2015.01.015>

Examples

data("data_floralbuds")
str(data_floralbuds)
summary(data_floralbuds)

# The data are used in:
## Not run: 
vignette("Discriminant_analysis_examples")
vignette("Neural_net_examples")

## End(Not run)
data("data_floralbuds")
str(data_floralbuds)
summary(data_floralbuds)

# The data are used in:
## Not run: 
vignette("Discriminant_analysis_examples")
vignette("Neural_net_examples")

## End(Not run)

Instagram data

Description

This dataset contains information on fake (spam) accounts on Instagram. The original source is https://www.kaggle.com/free4ever1/instagram-fake-spammer-genuine-accounts by Bardiya Bakhshandeh.

The data contains information on 696 Instagram accounts. For each account, 11 variables were recorded describing its characteristics. The goal is to detect fake instagram accounts, which are used for spamming.

Usage

data("data_instagram")data("data_instagram")

Format

A data frame with 696 observations on the following variables.

profile.pic: binary, indicates whether profile has picture.
nums.length.username: ratio of number of numerical chars in username to its length.
fullname.words: number of words in full name.
nums.length.fullname: ratio of number of numerical characters in full name to its length.
name..username: binary, indicates whether the name and username of the profile are the same.
description.length: length of the description/biography of the profile (in number of characters).
external.URL: binary, indicates whether profile has external url.
private: binary, indicates whether profile is private or not.
X.posts: number of posts made by profile.
X.followers: number of followers.
X.follows: numbers of follows.
y: whether profile is fake or not.
dataType: vector taking the values “train” or “test” indicating whether the observation belongs to the training or the test data.

Source

https://www.kaggle.com/free4ever1/instagram-fake-spammer-genuine-accounts

Examples

data(data_instagram)
str(data_instagram)

# The data are used in:
## Not run: 
vignette("Random_forest_examples")

## End(Not run)
data(data_instagram)
str(data_instagram)

# The data are used in:
## Not run: 
vignette("Random_forest_examples")

## End(Not run)

Titanic data

Description

This dataset contains information on 1309 passengers of the RMS Titanic. The goal is to predict survival based on 11 characteristics such as the travel class, age and sex of the passengers.

The original data source is https://www.kaggle.com/c/titanic/data

The data is split up in a training data consisting of 891 observations and a test data of 418 observations. The response in the test set was obtained by combining information from other data files, and has been verified by submitting it as a ‘prediction’ to kaggle and getting perfect marks.

Usage

data("data_titanic")data("data_titanic")

Format

A data frame with 1309 observations on the following variables.

PassengerId: a unique identified for each passenger.
Pclass: travel class of the passenger.
Name: name of the passenger.
Sex: sex of the passenger.
Age: age of the passenger.
SibSp: number of siblings and spouses traveling with the passenger.
Parch: number of parents and children traveling with the passenger.
Ticket: Ticket number of the passenger.
Fare: fare paid for the ticket.
Cabin: cabin number of the passenger.
Embarked: Port of embarkation. Takes the values C (Cherbourg), Q (Queenstown) and S (Southampton).
y: factor indicating casualty or survivor.
dataType: vector taking the values “train” or “test” indicating whether the observation belongs to the training or the test data.

Source

https://www.kaggle.com/c/titanic/data

Examples

data("data_titanic")
traindata <- data_titanic[which(data_titanic$dataType == "train"), -13]
testdata <- data_titanic[which(data_titanic$dataType == "test"), -13]
str(traindata)
table(traindata$y)

# The data are used in:
## Not run: 
vignette("Rpart_examples")

## End(Not run)
data("data_titanic")
traindata <- data_titanic[which(data_titanic$dataType == "train"), -13]
testdata <- data_titanic[which(data_titanic$dataType == "test"), -13]
str(traindata)
table(traindata$y)

# The data are used in:
## Not run: 
vignette("Rpart_examples")

## End(Not run)

Constructs feature vectors from a kernel matrix.

Description

Constructs feature vectors from a kernel matrix.

Usage

makeFV(kmat, transfmat = NULL, precS = 1e-12)
makeFV(kmat, transfmat = NULL, precS = 1e-12)

Arguments

`kmat`	a kernel matrix. If `transfmat` is `NULL`, we are dealing with training data and then `kmat` must be a square kernel matrix (of size $n$ by $n$ when there are $n$ cases). Such a PSD matrix kmat can e.g. be produced by `makeKernel` or by `kernlab::kernelMatrix`. If on the other hand `transfmat` is not `NULL`, we are dealing with a test set. See details for the precise working.
`transfmat`	transformation matrix. If not `NULL`, it is the value `transfmat` of `makeFV` on training data. It has to be a square matrix, with as many rows as there were training data.
`precS`	if not `NULL`, eigenvalues of `kmat` below `precS` will be set equal to precS.

Details

If transfmat is non-NULL, we are dealing with a test set. Denote the number of cases in the test set by $m \geq 1$ . Each row of kmat of the test set then must contain the kernel values of a new case with all cases in the training set. Therefore the kernel matrix kmat must have dimensions $m$ by $n$ . The matrix kmat can e.g. be produced by makeKernel. It can also be obtained by running kernlab::kernelMatrix on the union of the training set and the test set, yielding an $(n+m)$ by $(n+m)$ matrix, from which one then takes the $[(n+1):m , 1:n]$ submatrix.

Value

A list with components:

Xf

When makeKV is applied to the training set, Xf has coordinates of $n$ points (vectors), the plain inner products of which equal the kernel matrix of the training set. That is, kmat = Xf Xf'. The Xf are expressed in an orthogonal basis in which the variance of the coordinates is decreasing, which is useful when plotting the first few coordinates. When makeFV is applied to a test set, Xf are coordinates of the feature vectors of the test set in the same space as those of the training set, and then kmat = Xf %*% Xf of training data.

transfmat

square matrix for transforming kmat to Xf.

Author(s)

Raymaekers J., Rousseeuw P.J., Hubert, M.

References

Raymaekers J., Rousseeuw P.J., Hubert M. (2021). Class maps for visualizing classification results. Technometrics, appeared online. doi:10.1080/00401706.2021.1927849(link to open access pdf)

Examples

library(e1071)
set.seed(1); X <- matrix(rnorm(200 * 2), ncol = 2)
X[1:100, ] <- X[1:100, ] + 2
X[101:150, ] <- X[101:150, ] - 2
y <- as.factor(c(rep("blue", 150), rep("red", 50)))
cols <- c("deepskyblue3", "red")
plot(X, col = cols[as.numeric(y)], pch = 19)
# We now fit an SVM with radial basis kernel to the data:
svmfit <- svm(y~.,  data = data.frame(X = X, y = y),  scale = FALSE,
             kernel = "radial", cost = 10, gamma = 1, probability = TRUE)
Kxx <- makeKernel(X, svfit = svmfit)
outFV <- makeFV(Kxx)
Xf <- outFV$Xf # The data matrix in this feature space.
dim(Xf) # The feature vectors are high dimensional.
# The inner products of Xf match the kernel matrix:
max(abs(as.vector(Kxx - crossprod(t(Xf), t(Xf))))) # 3.005374e-13 # tiny, OK
range(rowSums(Xf^2)) # all points in Xf lie on the unit sphere.
pairs(Xf[, 1:5], col = cols[as.numeric(y)])
# In some of these we see spherical effects, e.g.
plot(Xf[, 1], Xf[, 5], col = cols[as.numeric(y)], pch = 19)
# The data look more separable here than in the original
# two-dimensional space.

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

## End(Not run)
library(e1071)
set.seed(1); X <- matrix(rnorm(200 * 2), ncol = 2)
X[1:100, ] <- X[1:100, ] + 2
X[101:150, ] <- X[101:150, ] - 2
y <- as.factor(c(rep("blue", 150), rep("red", 50)))
cols <- c("deepskyblue3", "red")
plot(X, col = cols[as.numeric(y)], pch = 19)
# We now fit an SVM with radial basis kernel to the data:
svmfit <- svm(y~.,  data = data.frame(X = X, y = y),  scale = FALSE,
             kernel = "radial", cost = 10, gamma = 1, probability = TRUE)
Kxx <- makeKernel(X, svfit = svmfit)
outFV <- makeFV(Kxx)
Xf <- outFV$Xf # The data matrix in this feature space.
dim(Xf) # The feature vectors are high dimensional.
# The inner products of Xf match the kernel matrix:
max(abs(as.vector(Kxx - crossprod(t(Xf), t(Xf))))) # 3.005374e-13 # tiny, OK
range(rowSums(Xf^2)) # all points in Xf lie on the unit sphere.
pairs(Xf[, 1:5], col = cols[as.numeric(y)])
# In some of these we see spherical effects, e.g.
plot(Xf[, 1], Xf[, 5], col = cols[as.numeric(y)], pch = 19)
# The data look more separable here than in the original
# two-dimensional space.

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

## End(Not run)

Compute kernel matrix

Description

Computes kernel value or kernel matrix, where the kernel type is extracted from an svm trained by e1071::svm.

Usage

makeKernel(X1, X2 = NULL, svfit)
makeKernel(X1, X2 = NULL, svfit)

Arguments

`X1`	first matrix (or vector) of coordinates.
`X2`	if not `NULL`, second data matrix or vector. If NULL, `X2` is assumed equal to `X1`.
`svfit`	output from `e1071::svm`

Value

the kernel matrix, of dimensions nrow(X1) by nrow(X2). When both X1 and X2 are vectors, the result is a single number.

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. doi:10.1080/00401706.2021.1927849(link to open access pdf)

Examples


library(e1071)
set.seed(1); X <- matrix(rnorm(200 * 2), ncol = 2)
X[1:100, ] <- X[1:100, ] + 2
X[101:150, ] <- X[101:150, ] - 2
y <- as.factor(c(rep("blue", 150), rep("red", 50))) # two classes
# We now fit an SVM with radial basis kernel to the data:
set.seed(1) # to make the result of svm() reproducible.
svmfit <- svm(y~.,  data = data.frame(X = X, y = y),  scale = FALSE,
             kernel = "radial", cost = 10, gamma = 1, probability = TRUE)
Kxx <- makeKernel(X, svfit = svmfit)
# The result is a square kernel matrix:
dim(Kxx) # 200 200
Kxx[1:5, 1:5]

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

## End(Not run)
library(e1071)
set.seed(1); X <- matrix(rnorm(200 * 2), ncol = 2)
X[1:100, ] <- X[1:100, ] + 2
X[101:150, ] <- X[101:150, ] - 2
y <- as.factor(c(rep("blue", 150), rep("red", 50))) # two classes
# We now fit an SVM with radial basis kernel to the data:
set.seed(1) # to make the result of svm() reproducible.
svmfit <- svm(y~.,  data = data.frame(X = X, y = y),  scale = FALSE,
             kernel = "radial", cost = 10, gamma = 1, probability = TRUE)
Kxx <- makeKernel(X, svfit = svmfit)
# The result is a square kernel matrix:
dim(Kxx) # 200 200
Kxx[1:5, 1:5]

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

## End(Not run)

Draw a quasi residual plot of PAC versus a data feature

Description

Draw a quasi residual plot to visualize classification results. The vertical axis of the quasi residual plot shows each case's probability of alternative class (PAC). The horizontal axis shows the feature given as the second argument in the function call.

Usage

qresplot(PAC, feat, xlab = NULL, xlim = NULL,
         main = NULL, identify = FALSE, gray = TRUE,
         opacity = 1, squareplot = FALSE, plotLoess = FALSE,
         plotErrorBars = FALSE, plotQuantiles = FALSE,
         grid = NULL, probs = c(0.5, 0.75),
         cols = NULL, fac = 1, cex = 1,
         cex.main = 1.2, cex.lab = 1,
         cex.axis = 1, pch = 19)
qresplot(PAC, feat, xlab = NULL, xlim = NULL,
         main = NULL, identify = FALSE, gray = TRUE,
         opacity = 1, squareplot = FALSE, plotLoess = FALSE,
         plotErrorBars = FALSE, plotQuantiles = FALSE,
         grid = NULL, probs = c(0.5, 0.75),
         cols = NULL, fac = 1, cex = 1,
         cex.main = 1.2, cex.lab = 1,
         cex.axis = 1, pch = 19)

Arguments

`PAC`	vector with the PAC values of a classification, typically the `$PAC` in the return of a call to a function `vcr..`
`feat`	the PAC will be plotted versus this data feature. Note that feat does not have to be one of the explanatory variables of the model. It can be another variable, a combination of variables (like a sum or a principal component score), the row number of the cases if they were recorded succesively, etc.
`xlab`	label for the horizontal axis, i.e. the name of variable feat.
`xlim`	limits for the horizontal axis. If `NULL`, the range of feat is used.
`main`	title for the plot.
`identify`	if `TRUE`, left-click on a point to get its number, then ESC to exit.
`gray`	logical, if `TRUE` (the default) the plot region where $PAC < 0.5$ gets a light gray background. Points in this region were classified into their given class, and the points above this region were misclassified.
`opacity`	determines opacity of plotted dots. Value between 0 and 1, where 0 is transparent and 1 is opaque.
`squareplot`	if `TRUE`, the horizontal and vertical axis will get the same length.
`plotLoess`	if `TRUE`, a standard loess curve is fitted and superimposed on the plot. May not work well if feat is discrete with few values. At most one of the options `plotLoess`, `plotErrorbars`, or `plotQuantiles` can be selected.
`plotErrorBars`	if `TRUE`, the average PAC and its standard error are computed on the intervals of a grid (see option grid). Then a red curve connecting the averages is plotted, as well as two blue curves corresponding to the average plus or minus one standard error. At most one of the options `plotLoess`, `plotErrorbars`, or `plotQuantiles` can be selected.
`plotQuantiles`	if `TRUE`, one or more quantiles of the PAC are computed on the intervals of a grid (see option grid). The quantiles correspond the probabilities in option probs. Then the curves connecting the quantiles are plotted. At most one of the options `plotLoess`, `plotErrorbars`, or `plotQuantiles` can be selected.
`grid`	only used when `plotErrorBars` or `plotQuantiles` are selected. This is a vector with increasing feat values, forming the grid. If `NULL`, the grid consists of the minimum and the maximum of feat, with 9 equispaced points between them.
`probs`	only used when `plotQuantiles` is selected. This is a vector with probabilities determining the quantiles. If `NULL`, defaults to `c(0.5, 0.75)`.
`cols`	only used when plotquantiles is selected. A vector with the colors of the quantile curves. If `NULL` the cols are taken as 2, 3, ...
`fac`	only used when `plotLoess`, `plotErrorBars` or `plotQuantiles` are selected. A real number to multiply the resulting curves. A value `fac > 1` can be useful to better visualize the curves when they would be too close to zero. By default (`fac = 1`) this is not done.
`cex`	passed on to `plot`.
`cex.main`	same, for title.
`cex.lab`	same, for labels on horizontal and vertical axes.
`cex.axis`	same, for axes.
`pch`	plot character for the points, defaults to 19.

Value

coordinates

a matrix with 2 columns containing the coordinates of the plotted points. This makes it easier to add text next to interesting points. If identify = TRUE, the attribute ids of coordinates contains the row numbers of the identified points in the matrix coordinates.

Author(s)

Raymaekers J., Rousseeuw P.J.

References

Raymaekers J., Rousseeuw P.J.(2021). Silhouettes and quasi residual plots for neural nets and tree-based classifiers. (link to open access pdf)

Examples

library(rpart)
data("data_titanic")
traindata <- data_titanic[which(data_titanic$dataType == "train"), -13]
set.seed(123) # rpart is not deterministic
rpart.out <- rpart(y ~ Pclass + Sex + SibSp +
                    Parch + Fare + Embarked,
                  data = traindata, method = 'class', model = TRUE)
mytype <- list(nominal = c("Name", "Sex", "Ticket", "Cabin", "Embarked"), ordratio = c("Pclass"))
x_train <- traindata[, -12]
y_train <- traindata[,  12]
vcrtrain <- vcr.rpart.train(x_train, y_train, rpart.out, mytype)
# Quasi residual plot versus age, for males only:
PAC <- vcrtrain$PAC[which(x_train$Sex == "male")]
feat <- x_train$Age[which(x_train$Sex == "male")]
qresplot(PAC, feat, xlab = "Age (years)", opacity = 0.5,
         main = "quasi residual plot for male passengers",
         plotLoess = TRUE)
text(x = 14, y = 0.60, "loess curve", col = "red", cex = 1)
library(rpart)
data("data_titanic")
traindata <- data_titanic[which(data_titanic$dataType == "train"), -13]
set.seed(123) # rpart is not deterministic
rpart.out <- rpart(y ~ Pclass + Sex + SibSp +
                    Parch + Fare + Embarked,
                  data = traindata, method = 'class', model = TRUE)
mytype <- list(nominal = c("Name", "Sex", "Ticket", "Cabin", "Embarked"), ordratio = c("Pclass"))
x_train <- traindata[, -12]
y_train <- traindata[,  12]
vcrtrain <- vcr.rpart.train(x_train, y_train, rpart.out, mytype)
# Quasi residual plot versus age, for males only:
PAC <- vcrtrain$PAC[which(x_train$Sex == "male")]
feat <- x_train$Age[which(x_train$Sex == "male")]
qresplot(PAC, feat, xlab = "Age (years)", opacity = 0.5,
         main = "quasi residual plot for male passengers",
         plotLoess = TRUE)
text(x = 14, y = 0.60, "loess curve", col = "red", cex = 1)

Draw the silhouette plot of a classification

Description

Draw the silhouette plot to visualize classification results, based on the output of one of the vcr.*.* functions in this package. The horizontal axis of the silhouette plot shows each case's s(i).

Usage

silplot(vcrout, classLabels = NULL, classCols = NULL,
        showLegend = TRUE, showClassNumbers = FALSE,
        showCases = FALSE, drawLineAtAverage = FALSE,
        topdown = TRUE, main = NULL, summary = TRUE)
silplot(vcrout, classLabels = NULL, classCols = NULL,
        showLegend = TRUE, showClassNumbers = FALSE,
        showCases = FALSE, drawLineAtAverage = FALSE,
        topdown = TRUE, main = NULL, summary = TRUE)

Arguments

`vcrout`	output of `vcr..train` or `vcr..newdata`. Required.
`classLabels`	the labels (levels) of the classes. If `NULL`, they are taken from `vcrout`.
`classCols`	a list of colors for the classes. There should be at least as many as there are levels. If `NULL` a default palette is used.
`showLegend`	if `TRUE`, a legend is shown to the right of the plot.
`showClassNumbers`	if `TRUE`, the legend will show the class numbers instead of the class labels.
`showCases`	if `TRUE`, the plot shows the numbers of the cases. They are only readable when the number of cases is relatively small.
`topdown`	if `TRUE` (the default), the silhouettes are plotted from top to bottom. Otherwise they are plotted from left to right.
`drawLineAtAverage`	if `TRUE`, drwas a line at the average value of the `s(i)`.
`main`	title for the plot. If `NULL`, a default title is used.
`summary`	if `TRUE`, puts a summary table on the screen with for each class its number, label, number of class members, and the average of its `s(i)`.

Value

A ggplot object containing the silhouette plot.

Author(s)

Raymaekers J., Rousseeuw P.J.

References

Raymaekers J., Rousseeuw P.J.(2021). Silhouettes and quasi residual plots for neural nets and tree-based classifiers. (link to open access pdf)

Examples

vcrout <- vcr.da.train(iris[, 1:4], iris[, 5])
silplot(vcrout)
# For more examples, we refer to the vignettes:
## Not run: 
vignette("Discriminant_analysis_examples")
vignette("K_nearest_neighbors_examples")
vignette("Support_vector_machine_examples")
vignette("Rpart_examples")
vignette("Forest_examples")
vignette("Neural_net_examples")

## End(Not run)
vcrout <- vcr.da.train(iris[, 1:4], iris[, 5])
silplot(vcrout)
# For more examples, we refer to the vignettes:
## Not run: 
vignette("Discriminant_analysis_examples")
vignette("K_nearest_neighbors_examples")
vignette("Support_vector_machine_examples")
vignette("Rpart_examples")
vignette("Forest_examples")
vignette("Neural_net_examples")

## End(Not run)

Make a vertically stacked mosaic plot of class predictions.

Description

Make a vertically stacked mosaic plot of class predictions from the output of vcr.*.train or vcr.*.newdata. Optionally, the outliers for each class can be shown as a gray rectangle at the top.

Usage

stackedplot(vcrout, cutoff = 0.99, classCols = NULL,
classLabels = NULL, separSize=1, minSize=1.5,
showOutliers = TRUE, showLegend = FALSE, main = NULL,
htitle = NULL, vtitle = NULL)
stackedplot(vcrout, cutoff = 0.99, classCols = NULL,
classLabels = NULL, separSize=1, minSize=1.5,
showOutliers = TRUE, showLegend = FALSE, main = NULL,
htitle = NULL, vtitle = NULL)

Arguments

`vcrout`	output of `vcr..train` or `vcr..newdata`.
`cutoff`	cases with overall farness `vcrout$ofarness` > `cutoff` are flagged as outliers.
`classCols`	user-specified colors for the classes. If `NULL` a default palette is used.
`classLabels`	names of given labels. If `NULL` they are taken from `vcrout`.
`separSize`	how much white between rectangles.
`minSize`	rectangles describing less than `minSize` percent of the data, are shown as `minSize` percent.
`showOutliers`	if `TRUE`, shows a separate class in gray with the outliers, always at the top.
`showLegend`	if `TRUE`, a legend is shown to the right of the plot. Default `FALSE`, since the legend is not necessary as the colors are already visible in the bottom part of each stack.
`main`	title for the plot.
`htitle`	title for horizontal axis (given labels). If `NULL`, a default title is shown.
`vtitle`	title for vertical axis (predicted labels). If `NULL`, a default title is shown.

Value

A ggplot object.

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. doi:10.1080/00401706.2021.1927849(link to open access pdf)

Examples


data("data_floralbuds")
X <- data_floralbuds[, 1:6]; y <- data_floralbuds[, 7]
vcrout <- vcr.da.train(X, y)
cols <- c("saddlebrown", "orange", "olivedrab4", "royalblue3")
stackedplot(vcrout, classCols = cols, showLegend = TRUE)

# The legend is not really needed, since we can read the
# color of a class from the bottom of its vertical bar:
stackedplot(vcrout, classCols = cols, main = "Stacked plot of QDA on foral buds data")

# If we do not wish to show outliers:
stackedplot(vcrout, classCols = cols, showOutliers = FALSE)

# For more examples, we refer to the vignettes:
## Not run: 
vignette("Discriminant_analysis_examples")
vignette("K_nearest_neighbors_examples")
vignette("Support_vector_machine_examples")
vignette("Rpart_examples")
vignette("Random_forest_examples")
vignette("Neural_net_examples")

## End(Not run)
data("data_floralbuds")
X <- data_floralbuds[, 1:6]; y <- data_floralbuds[, 7]
vcrout <- vcr.da.train(X, y)
cols <- c("saddlebrown", "orange", "olivedrab4", "royalblue3")
stackedplot(vcrout, classCols = cols, showLegend = TRUE)

# The legend is not really needed, since we can read the
# color of a class from the bottom of its vertical bar:
stackedplot(vcrout, classCols = cols, main = "Stacked plot of QDA on foral buds data")

# If we do not wish to show outliers:
stackedplot(vcrout, classCols = cols, showOutliers = FALSE)

# For more examples, we refer to the vignettes:
## Not run: 
vignette("Discriminant_analysis_examples")
vignette("K_nearest_neighbors_examples")
vignette("Support_vector_machine_examples")
vignette("Rpart_examples")
vignette("Random_forest_examples")
vignette("Neural_net_examples")

## End(Not run)

Carry out discriminant analysis on new data, and prepare to visualize its results.

Description

Predicts class labels for new data by discriminant analysis, using the output of vcr.da.train on the training data. For new data cases whose label in yintnew is non-missing, additional output is produced for constructing graphical displays such as the classmap.

Usage

vcr.da.newdata(Xnew, ynew=NULL, vcr.da.train.out)
vcr.da.newdata(Xnew, ynew=NULL, vcr.da.train.out)

Arguments

`Xnew`	data matrix of the new data, with the same number of columns as in the training data. 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.da.train.out`	output of `vcr.da.train` on the training data.

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.da.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 $i$ to each class $g$ . Always exists.
`farness`	farness of each case $i$ 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.
`classMS`	list with center and covariance matrix of each class, from `vcr.da.train.out`.
`lCurrent`	log of mixture density of each case in its given class. Is `NA` for cases with missing `ynew`.
`lPred`	log of mixture density of each case in its predicted class. Always exists.
`lAlt`	log of mixture density of each case in its alternative class. Is `NA` for cases with missing `ynew`.

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. doi:10.1080/00401706.2021.1927849(link to open access pdf)

Examples


vcr.train <- vcr.da.train(iris[, 1:4], iris[, 5])
inds <- c(51:150) # a subset, containing only 2 classes
iris2 <- iris[inds, ] # fake "new" data
iris2[c(1:10, 51:60), 5] <- NA
vcr.test <- vcr.da.newdata(iris2[, 1:4], iris2[, 5], vcr.train)
vcr.test$PAC[1:25] # between 0 and 1. Is NA where the response is.
plot(vcr.test$PAC, vcr.train$PAC[inds]); abline(0, 1) # match
plot(vcr.test$farness, vcr.train$farness[inds]); abline(0, 1) # match
confmat.vcr(vcr.train) # for comparison
confmat.vcr(vcr.test)
stackedplot(vcr.train) # for comparison
stackedplot(vcr.test)
classmap(vcr.train, "versicolor", classCols = 2:4) # for comparison
classmap(vcr.test, "versicolor", classCols = 2:4) # has fewer points

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

## End(Not run)
vcr.train <- vcr.da.train(iris[, 1:4], iris[, 5])
inds <- c(51:150) # a subset, containing only 2 classes
iris2 <- iris[inds, ] # fake "new" data
iris2[c(1:10, 51:60), 5] <- NA
vcr.test <- vcr.da.newdata(iris2[, 1:4], iris2[, 5], vcr.train)
vcr.test$PAC[1:25] # between 0 and 1. Is NA where the response is.
plot(vcr.test$PAC, vcr.train$PAC[inds]); abline(0, 1) # match
plot(vcr.test$farness, vcr.train$farness[inds]); abline(0, 1) # match
confmat.vcr(vcr.train) # for comparison
confmat.vcr(vcr.test)
stackedplot(vcr.train) # for comparison
stackedplot(vcr.test)
classmap(vcr.train, "versicolor", classCols = 2:4) # for comparison
classmap(vcr.test, "versicolor", classCols = 2:4) # has fewer points

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

## End(Not run)

Carry out discriminant analysis on training data, and prepare to visualize its results.

Description

Custom DA function which prepares for graphical displays such as the classmap. The disciminant analysis itself is carried out by the maximum a posteriori rule, which maximizes the density of the mixture.

Usage

vcr.da.train(X, y, rule = "QDA", estmethod = "meancov")
vcr.da.train(X, y, rule = "QDA", estmethod = "meancov")

Arguments

`X`	a numerical matrix containing the predictors in its columns. Missing values are not allowed.
`y`	a factor with the given class labels.
`rule`	either "`QDA`" for quadratic discriminant analysis or "`LDA`" for linear discriminant analysis.
`estmethod`	function for location and covariance estimation. Should return a list with the center `$m` and the covariance matrix `$S`. The default is `"meancov"` (classical mean and covariance matrix), and the option `"DetMCD"` (based on `robustbase::covMcd`) is also provided.

Value

A list with components:

`yint`	number of the given class of each case. Can contain `NA`'s.
`y`	given class label of each case. Can contain `NA`'s.
`levels`	levels of `y`
`predint`	predicted class number of each case. For each case this is the class with the highest posterior probability. 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 `y` is missing.
`altlab`	label of the alternative class. Is `NA` for cases whose `y` is missing.
`PAC`	probability of the alternative class. Is `NA` for cases whose `y` is missing.
`figparams`	parameters for computing `fig`, can be used for new data.
`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 `y` is missing.
`ofarness`	for each case `i`, its lowest `fig[i,g]` to any class `g`. Always exists.
`classMS`	list with center and covariance matrix of each class
`lCurrent`	log of mixture density of each case in its given class. Is `NA` for cases with missing `y`.
`lPred`	log of mixture density of each case in its predicted class. Always exists.
`lAlt`	log of mixture density of each case in its alternative class. Is `NA` for cases with missing `y`.

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. doi:10.1080/00401706.2021.1927849(link to open access pdf)

Examples


data("data_floralbuds")
X <- data_floralbuds[, 1:6]; y <- data_floralbuds[, 7]
vcrout <- vcr.da.train(X, y, rule = "QDA")
# For linear discriminant analysis, put rule = "LDA".
confmat.vcr(vcrout) # There are a few outliers
cols <- c("saddlebrown", "orange", "olivedrab4", "royalblue3")
stackedplot(vcrout, classCols = cols)
classmap(vcrout, "bud", classCols = cols)

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

## End(Not run)
data("data_floralbuds")
X <- data_floralbuds[, 1:6]; y <- data_floralbuds[, 7]
vcrout <- vcr.da.train(X, y, rule = "QDA")
# For linear discriminant analysis, put rule = "LDA".
confmat.vcr(vcrout) # There are a few outliers
cols <- c("saddlebrown", "orange", "olivedrab4", "royalblue3")
stackedplot(vcrout, classCols = cols)
classmap(vcrout, "bud", classCols = cols)

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

## End(Not run)

Prepare for visualization of a random forest classification on new data.

Description

Produces output for the purpose of constructing graphical displays such as the classmap on new data. Requires the output of vcr.forest.train as an argument.

Usage

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

Arguments

`Xnew`	data matrix of the new data, with the same number of columns `d` as in the training data. 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.forest.train.out`	output of `vcr.forest.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.forest.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`	alternative label if yintnew was given, else `NA`.
`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.

Author(s)

Raymaekers J., Rousseeuw P.J.

References

Raymaekers J., Rousseeuw P.J.(2021). Silhouettes and quasi residual plots for neural nets and tree-based classifiers. (link to open access pdf)

Examples

library(randomForest)
data("data_instagram")
traindata <- data_instagram[which(data_instagram$dataType == "train"), -13]
set.seed(71) # randomForest is not deterministic
rfout <- randomForest(y ~ ., data = traindata, keep.forest = TRUE)
mytype <- list(symm = c(1, 5, 7, 8)) # These 4 columns are
# (symmetric) binary variables. The variables that are not
# listed are interval-scaled by default.
x_train <- traindata[, -12]
y_train <- traindata[, 12]
vcrtrain <- vcr.forest.train(X = x_train, y = y_train,
                            trainfit = rfout, type = mytype)
testdata <- data_instagram[which(data_instagram$dataType == "test"), -13]
Xnew <- testdata[, -12]
ynew <- testdata[, 12]
vcrtest <- vcr.forest.newdata(Xnew, ynew, vcrtrain)
confmat.vcr(vcrtest)
stackedplot(vcrtest, classCol = c(4, 2))
silplot(vcrtest, classCols = c(4, 2))
classmap(vcrtest, "genuine", classCols = c(4, 2))
classmap(vcrtest, "fake", classCols = c(4, 2))

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

## End(Not run)
library(randomForest)
data("data_instagram")
traindata <- data_instagram[which(data_instagram$dataType == "train"), -13]
set.seed(71) # randomForest is not deterministic
rfout <- randomForest(y ~ ., data = traindata, keep.forest = TRUE)
mytype <- list(symm = c(1, 5, 7, 8)) # These 4 columns are
# (symmetric) binary variables. The variables that are not
# listed are interval-scaled by default.
x_train <- traindata[, -12]
y_train <- traindata[, 12]
vcrtrain <- vcr.forest.train(X = x_train, y = y_train,
                            trainfit = rfout, type = mytype)
testdata <- data_instagram[which(data_instagram$dataType == "test"), -13]
Xnew <- testdata[, -12]
ynew <- testdata[, 12]
vcrtest <- vcr.forest.newdata(Xnew, ynew, vcrtrain)
confmat.vcr(vcrtest)
stackedplot(vcrtest, classCol = c(4, 2))
silplot(vcrtest, classCols = c(4, 2))
classmap(vcrtest, "genuine", classCols = c(4, 2))
classmap(vcrtest, "fake", classCols = c(4, 2))

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

## End(Not run)

Prepare for visualization of a random forest classification on training data

Description

Produces output for the purpose of constructing graphical displays such as the classmap and silplot. The user first needs to train a random forest on the data by randomForest::randomForest. This then serves as an argument to vcr.forest.train.

Usage

vcr.forest.train(X, y, trainfit, type = list(),
                 k = 5, stand = TRUE)
vcr.forest.train(X, y, trainfit, type = list(),
                 k = 5, stand = TRUE)

Arguments

`X`	A rectangular matrix or data frame, where the columns (variables) may be of mixed type.
`y`	factor with the given class labels. It is crucial that `X` and `y` are exactly the same as in the call to `randomForest::randomForest`. `y` is allowed to contain `NA`'s.
`trainfit`	the output of a `randomForest::randomForest` training run.
`k`	the number of nearest neighbors used in the farness computation.
`type`	list for specifying some (or all) of the types of the variables (columns) in `X`, used for computing the dissimilarity matrix, as in `cluster::daisy`. The list may contain the following components: `"ordratio"` (ratio scaled variables to be treated as ordinal variables), `"logratio"` (ratio scaled variables that must be logarithmically transformed), `"asymm"` (asymmetric binary) and `"symm"` (symmetric binary variables). Each component's value is a vector, containing the names or the numbers of the corresponding columns of `X`. Variables not mentioned in the `type` list are interpreted as usual (see argument `X`).
`stand`	whether or not to standardize numerical (interval scaled) variables by their range as in the original `cluster::daisy` code for the farness computation. Defaults to `TRUE`.

Value

A list with components:

`X`	The data used to train the forest.
`yint`	number of the given class of each case. Can contain `NA`'s.
`y`	given class label of each case. Can contain `NA`'s.
`levels`	levels of `y`
`predint`	predicted class number of each case. For each case this is the class with the highest posterior probability. 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 `y` is missing.
`altlab`	label of the alternative class. Is `NA` for cases whose `y` is missing.
`PAC`	probability of the alternative class. Is `NA` for cases whose `y` is missing.
`figparams`	parameters for computing `fig`, can be used for new data.
`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 `y` is missing.
`ofarness`	for each case `i`, its lowest `fig[i,g]` to any class `g`. Always exists.
`trainfit`	The trained random forest which was given as an input to this function.

Author(s)

Raymaekers J., Rousseeuw P.J.

References

Raymaekers J., Rousseeuw P.J.(2021). Silhouettes and quasi residual plots for neural nets and tree-based classifiers. (link to open access pdf)

Examples

library(randomForest)
data("data_instagram")
traindata <- data_instagram[which(data_instagram$dataType == "train"), -13]
set.seed(71) # randomForest is not deterministic
rfout <- randomForest(y~., data = traindata, keep.forest = TRUE)
mytype <- list(symm = c(1, 5, 7, 8)) # These 4 columns are
# (symmetric) binary variables. The variables that are not
# listed are interval-scaled by default.
x_train <- traindata[, -12]
y_train <- traindata[, 12]
# Prepare for visualization:
vcrtrain <- vcr.forest.train(X = x_train, y = y_train,
                            trainfit = rfout, type = mytype)
confmat.vcr(vcrtrain)
stackedplot(vcrtrain, classCols = c(4, 2))
silplot(vcrtrain, classCols = c(4, 2))
classmap(vcrtrain, "genuine", classCols = c(4, 2))
classmap(vcrtrain, "fake", classCols = c(4, 2))

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

## End(Not run)
library(randomForest)
data("data_instagram")
traindata <- data_instagram[which(data_instagram$dataType == "train"), -13]
set.seed(71) # randomForest is not deterministic
rfout <- randomForest(y~., data = traindata, keep.forest = TRUE)
mytype <- list(symm = c(1, 5, 7, 8)) # These 4 columns are
# (symmetric) binary variables. The variables that are not
# listed are interval-scaled by default.
x_train <- traindata[, -12]
y_train <- traindata[, 12]
# Prepare for visualization:
vcrtrain <- vcr.forest.train(X = x_train, y = y_train,
                            trainfit = rfout, type = mytype)
confmat.vcr(vcrtrain)
stackedplot(vcrtrain, classCols = c(4, 2))
silplot(vcrtrain, classCols = c(4, 2))
classmap(vcrtrain, "genuine", classCols = c(4, 2))
classmap(vcrtrain, "fake", classCols = c(4, 2))

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

## End(Not run)

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)
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. doi:10.1080/00401706.2021.1927849(link to open access pdf)

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)
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)

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

Description

Carries out a k-nearest neighbor classification on the training data. Various additional output is produced for the purpose of constructing graphical displays such as the classmap.

Usage

vcr.knn.train(X, y, k)
vcr.knn.train(X, y, k)

Arguments

`X`	This can be a rectangular matrix or data frame of (already standardized) measurements, or a dist object obtained from `stats::dist` or `cluster::daisy`. Missing values are not allowed.
`y`	factor with the given (observed) class labels. There need to be non-missing `y` in order to be able to train the classifier.
`k`	the number of nearest neighbors used. It can be selected by running cross-validation using a different package.

Value

A list with components:

`yint`	number of the given class of each case. Can contain `NA`'s.
`y`	given class label of each case. Can contain `NA`'s.
`levels`	levels of `y`
`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 `y` is missing.
`altlab`	label of the alternative class. Is `NA` for cases whose `y` is missing.
`PAC`	probability of the alternative class. Is `NA` for cases whose `y` is missing.
`figparams`	parameters used to compute `fig`.
`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 `y` 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 the arguments. Will also be used for classifying new data.
`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 `y` is missing.
`X`	If the argument `X` was a data frame or matrix of coordinates, `as.matrix(X)` is returned here. This is useful for classifying new data.

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. doi:10.1080/00401706.2021.1927849(link to open access pdf)

Examples

vcrout <- vcr.knn.train(iris[, 1:4], iris[, 5], k = 5)
confmat.vcr(vcrout)
stackedplot(vcrout)
classmap(vcrout, "versicolor", classCols = 2:4)
# The cases misclassified as virginica are shown in blue.

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

## End(Not run)
vcrout <- vcr.knn.train(iris[, 1:4], iris[, 5], k = 5)
confmat.vcr(vcrout)
stackedplot(vcrout)
classmap(vcrout, "versicolor", classCols = 2:4)
# The cases misclassified as virginica are shown in blue.

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

## End(Not run)

Prepare for visualization of a neural network classification on new data.

Description

Prepares graphical display of new data fitted by a neural net that was modeled on the training data, using the output of vcr.neural.train on the training data.

Usage

vcr.neural.newdata(Xnew, ynew = NULL, probs,
                   vcr.neural.train.out)
vcr.neural.newdata(Xnew, ynew = NULL, probs,
                   vcr.neural.train.out)

Arguments

`Xnew`	data matrix of the new data, with the same number of columns as in the training data. Missing values in `Xnew` 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.
`probs`	posterior probabilities obtained by running the neural net on the new data.
`vcr.neural.train.out`	output of `vcr.neural.train` on the training data.

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.svm.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`	alternative label if yintnew was given, else `NA`.
`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.

Author(s)

Raymaekers J., Rousseeuw P.J.

References

Raymaekers J., Rousseeuw P.J.(2021). Silhouettes and quasi residual plots for neural nets and tree-based classifiers. (link to open access pdf)

Examples

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

## End(Not run)
# For examples, we refer to the vignette:
## Not run: 
vignette("Neural_net_examples")

## End(Not run)

Prepare for visualization of a neural network classification on training data.

Description

Produces output for the purpose of constructing graphical displays such as the classmap. The user first needs train a neural network. The representation of the data in a given layer (e.g. the final layer before applying the softmax function) then serves as the argument X to vcr.neural.train.

Usage

vcr.neural.train(X, y, probs, estmethod = meancov)
vcr.neural.train(X, y, probs, estmethod = meancov)

Arguments

`X`	the coordinates of the `n` objects of the training data, in the layer chosen by the user. Missing values are not allowed.
`y`	factor with the given class labels of the objects. Make sure that the levels are in the same order as used in the neural net, i.e. the columns of its binary "once-hot-encoded" response vectors.
`probs`	posterior probabilities obtained by the neural net, e.g. in keras. For each case (row of `X`), the classes have probabilities that add up to 1. Each row of the matrix probs contains these probabilities. The columns of probs must be in the same order as the levels of `y`.
`estmethod`	function for location and covariance estimation. Should return a list with `$m` and `$S`. Can be `meancov` (classical mean and covariance matrix) or `DetMCD`. If one or more classes have a singular covariance matrix, the function automatically switches to the PCA-based farness used in `vcr.svm.train`.

Value

A list with components:

`X`	the coordinates of the `n` objects of the training data, in the layer chosen by the user.
`yint`	number of the given class of each case. Can contain `NA`'s.
`y`	given class label of each case. Can contain `NA`'s.
`levels`	levels of `y`
`predint`	predicted class number of each case. For each case this is the class with the highest posterior probability. 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 `y` is missing.
`altlab`	label of the alternative class. Is `NA` for cases whose `y` is missing.
`ncolX`	number of columns in `X`. Keep??
`PAC`	probability of the alternative class. Is `NA` for cases whose `y` is missing.
`computeMD`	Whether or not the farness is computed using the Mahalanobis distance.
`classMS`	list with center and covariance matrix of each class
`PCAfits`	if not `NULL`, PCA fits to each class, estimated from the training data but also useful for new data.
`figparams`	parameters for computing `fig`, can be used for new data.
`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 `y` is missing.
`ofarness`	for each case `i`, its lowest `fig[i,g]` to any class `g`. Always exists.

Author(s)

Raymaekers J., Rousseeuw P.J.

References

Raymaekers J., Rousseeuw P.J.(2021). Silhouettes and quasi residual plots for neural nets and tree-based classifiers. (link to open access pdf)

Examples

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

## End(Not run)
# For examples, we refer to the vignette:
## Not run: 
vignette("Neural_net_examples")

## End(Not run)

Prepare for visualization of an rpart classification on new data.

Description

Produces output for the purpose of constructing graphical displays such as the classmap on new data. Requires the output of vcr.rpart.train as an argument.

Usage

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

Arguments

`Xnew`	data matrix of the new data, with the same number of columns `d` as in the training data. 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.rpart.train.out`	output of `vcr.rpart.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.rpart.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`	alternative label if yintnew was given, else `NA`.
`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.

Author(s)

Raymaekers J., Rousseeuw P.J.

References

Raymaekers J., Rousseeuw P.J.(2021). Silhouettes and quasi residual plots for neural nets and tree-based classifiers. (link to open access pdf)

Examples

library(rpart)
data("data_titanic")
traindata <- data_titanic[which(data_titanic$dataType == "train"), -13]
str(traindata); table(traindata$y)
set.seed(123) # rpart is not deterministic
rpart.out <- rpart(y ~ Pclass + Sex + SibSp +
                    Parch + Fare + Embarked,
                  data = traindata, method = 'class', model = TRUE)
y_train <- traindata[, 12]
x_train <- traindata[, -12]
mytype <- list(nominal = c("Name", "Sex", "Ticket", "Cabin", "Embarked"), ordratio = c("Pclass"))
# These are 5 nominal columns, and one ordinal.
# The variables not listed are by default interval-scaled.
vcrtrain <- vcr.rpart.train(x_train, y_train, rpart.out, mytype)
testdata <- data_titanic[which(data_titanic$dataType == "test"), -13]
dim(testdata)
x_test <- testdata[, -12]
y_test <- testdata[, 12]
vcrtest <- vcr.rpart.newdata(x_test, y_test, vcrtrain)
confmat.vcr(vcrtest)
silplot(vcrtest, classCols = c(2, 4))
classmap(vcrtest, "casualty", classCols = c(2, 4))
classmap(vcrtest, "survived", classCols = c(2, 4))

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

## End(Not run)
library(rpart)
data("data_titanic")
traindata <- data_titanic[which(data_titanic$dataType == "train"), -13]
str(traindata); table(traindata$y)
set.seed(123) # rpart is not deterministic
rpart.out <- rpart(y ~ Pclass + Sex + SibSp +
                    Parch + Fare + Embarked,
                  data = traindata, method = 'class', model = TRUE)
y_train <- traindata[, 12]
x_train <- traindata[, -12]
mytype <- list(nominal = c("Name", "Sex", "Ticket", "Cabin", "Embarked"), ordratio = c("Pclass"))
# These are 5 nominal columns, and one ordinal.
# The variables not listed are by default interval-scaled.
vcrtrain <- vcr.rpart.train(x_train, y_train, rpart.out, mytype)
testdata <- data_titanic[which(data_titanic$dataType == "test"), -13]
dim(testdata)
x_test <- testdata[, -12]
y_test <- testdata[, 12]
vcrtest <- vcr.rpart.newdata(x_test, y_test, vcrtrain)
confmat.vcr(vcrtest)
silplot(vcrtest, classCols = c(2, 4))
classmap(vcrtest, "casualty", classCols = c(2, 4))
classmap(vcrtest, "survived", classCols = c(2, 4))

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

## End(Not run)

Prepare for visualization of an rpart classification on training data.

Description

Produces output for the purpose of constructing graphical displays such as the classmap. The user first needs to train a classification tree on the data by rpart::rpart. This then serves as an argument to vcr.rpart.train.

Usage

vcr.rpart.train(X, y, trainfit, type = list(),
                k = 5, stand = TRUE)
vcr.rpart.train(X, y, trainfit, type = list(),
                k = 5, stand = TRUE)

Arguments

`X`	A rectangular matrix or data frame, where the columns (variables) may be of mixed type and may contain `NA`'s.
`y`	factor with the given class labels. It is crucial that `X` and `y` are exactly the same as in the call to `rpart::rpart`. `y` is allowed to contain `NA`'s.
`k`	the number of nearest neighbors used in the farness computation.
`trainfit`	the output of an `rpart::rpart` training cycle.
`type`	list for specifying some (or all) of the types of the variables (columns) in `X`, used for computing the dissimilarity matrix, as in `cluster::daisy`. The list may contain the following components: `"ordratio"` (ratio scaled variables to be treated as ordinal variables), `"logratio"` (ratio scaled variables that must be logarithmically transformed), `"asymm"` (asymmetric binary) and `"symm"` (symmetric binary variables). Each component's value is a vector, containing the names or the numbers of the corresponding columns of `X`. Variables not mentioned in the `type` list are interpreted as usual (see argument `X`).
`stand`	whether or not to standardize numerical (interval scaled) variables by their range as in the original `cluster::daisy` code for the farness computation. Defaults to `TRUE`.

Value

A list with components:

`X`	The input data `X`. Keep??
`yint`	number of the given class of each case. Can contain `NA`'s.
`y`	given class label of each case. Can contain `NA`'s.
`levels`	levels of `y`
`predint`	predicted class number of each case. For each case this is the class with the highest posterior probability. 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 `y` is missing.
`altlab`	label of the alternative class. Is `NA` for cases whose `y` is missing.
`PAC`	probability of the alternative class. Is `NA` for cases whose `y` is missing.
`figparams`	parameters for computing `fig`, can be used for new data.
`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 `y` is missing.
`ofarness`	for each case `i`, its lowest `fig[i,g]` to any class `g`. Always exists.
`trainfit`	the trainfit used to build the VCR object.

Author(s)

Raymaekers J., Rousseeuw P.J.

References

Raymaekers J., Rousseeuw P.J.(2021). Silhouettes and quasi residual plots for neural nets and tree-based classifiers. (link to open access pdf)

Examples

library(rpart)
data("data_titanic")
traindata <- data_titanic[which(data_titanic$dataType == "train"), -13]
str(traindata); table(traindata$y)
set.seed(123) # rpart is not deterministic
rpart.out <- rpart(y ~ Pclass + Sex + SibSp +
                    Parch + Fare + Embarked,
                  data = traindata, method = 'class', model = TRUE)
y_train <- traindata[, 12]
x_train <- traindata[, -12]
mytype <- list(nominal = c("Name", "Sex", "Ticket", "Cabin", "Embarked"), ordratio = c("Pclass"))
# These are 5 nominal columns, and one ordinal.
# The variables not listed are by default interval-scaled.
vcrtrain <- vcr.rpart.train(x_train, y_train, rpart.out, mytype)
confmat.vcr(vcrtrain)
silplot(vcrtrain, classCols = c(2, 4))
classmap(vcrtrain, "casualty", classCols = c(2, 4))
classmap(vcrtrain, "survived", classCols = c(2, 4))

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

## End(Not run)
library(rpart)
data("data_titanic")
traindata <- data_titanic[which(data_titanic$dataType == "train"), -13]
str(traindata); table(traindata$y)
set.seed(123) # rpart is not deterministic
rpart.out <- rpart(y ~ Pclass + Sex + SibSp +
                    Parch + Fare + Embarked,
                  data = traindata, method = 'class', model = TRUE)
y_train <- traindata[, 12]
x_train <- traindata[, -12]
mytype <- list(nominal = c("Name", "Sex", "Ticket", "Cabin", "Embarked"), ordratio = c("Pclass"))
# These are 5 nominal columns, and one ordinal.
# The variables not listed are by default interval-scaled.
vcrtrain <- vcr.rpart.train(x_train, y_train, rpart.out, mytype)
confmat.vcr(vcrtrain)
silplot(vcrtrain, classCols = c(2, 4))
classmap(vcrtrain, "casualty", classCols = c(2, 4))
classmap(vcrtrain, "survived", classCols = c(2, 4))

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

## End(Not run)

Prepare for visualization of a support vector machine classification on new data.

Description

Carries out a support vector machine classification of new data using the output of vcr.svm.train on the training data, and computes the quantities needed for its visualization.

Usage

vcr.svm.newdata(Xnew, ynew = NULL, vcr.svm.train.out)
vcr.svm.newdata(Xnew, ynew = NULL, vcr.svm.train.out)

Arguments

`Xnew`	data matrix of the new data, with the same number of columns as in the training data. Missing values in `Xnew` 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.svm.train.out`	output of `vcr.svm.train` on the training data.

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.svm.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`	alternative label if yintnew was given, else `NA`.
`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.

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. doi:10.1080/00401706.2021.1927849(link to open access pdf)

Examples


library(e1071)
set.seed(1); X <- matrix(rnorm(200 * 2), ncol = 2)
X[1:100, ] <- X[1:100, ] + 2
X[101:150, ] <- X[101:150, ] - 2
y <- as.factor(c(rep("blue", 150), rep("red", 50)))
# We now fit an SVM with radial basis kernel to the data:
set.seed(1) # to make the result of svm() reproducible.
svmfit <- svm(y~., data = data.frame(X = X, y = y),
scale = FALSE, kernel = "radial", cost = 10,
gamma = 1, probability = TRUE)
vcr.train <- vcr.svm.train(X, y, svfit = svmfit)
# As "new" data we take a subset of the training data:
inds <- c(1:25, 101:125, 151:175)
vcr.test <- vcr.svm.newdata(X[inds, ], y[inds], vcr.train)
plot(vcr.test$PAC, vcr.train$PAC[inds]); abline(0, 1) # match
plot(vcr.test$farness, vcr.train$farness[inds]); abline(0, 1)
confmat.vcr(vcr.test)
cols <- c("deepskyblue3", "red")
stackedplot(vcr.test, classCols = cols)
classmap(vcr.train, "blue", classCols = cols) # for comparison
classmap(vcr.test, "blue", classCols = cols)
classmap(vcr.train, "red", classCols = cols) # for comparison
classmap(vcr.test, "red", classCols = cols)


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

## End(Not run)
library(e1071)
set.seed(1); X <- matrix(rnorm(200 * 2), ncol = 2)
X[1:100, ] <- X[1:100, ] + 2
X[101:150, ] <- X[101:150, ] - 2
y <- as.factor(c(rep("blue", 150), rep("red", 50)))
# We now fit an SVM with radial basis kernel to the data:
set.seed(1) # to make the result of svm() reproducible.
svmfit <- svm(y~., data = data.frame(X = X, y = y),
scale = FALSE, kernel = "radial", cost = 10,
gamma = 1, probability = TRUE)
vcr.train <- vcr.svm.train(X, y, svfit = svmfit)
# As "new" data we take a subset of the training data:
inds <- c(1:25, 101:125, 151:175)
vcr.test <- vcr.svm.newdata(X[inds, ], y[inds], vcr.train)
plot(vcr.test$PAC, vcr.train$PAC[inds]); abline(0, 1) # match
plot(vcr.test$farness, vcr.train$farness[inds]); abline(0, 1)
confmat.vcr(vcr.test)
cols <- c("deepskyblue3", "red")
stackedplot(vcr.test, classCols = cols)
classmap(vcr.train, "blue", classCols = cols) # for comparison
classmap(vcr.test, "blue", classCols = cols)
classmap(vcr.train, "red", classCols = cols) # for comparison
classmap(vcr.test, "red", classCols = cols)


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

## End(Not run)

Prepare for visualization of a support vector machine classification on training data.

Description

Produces output for the purpose of constructing graphical displays such as the classmap. The user first needs to run a support vector machine classification on the data by e1071::svm, with the option probability = TRUE. This classification can be with two or more classes. The output of e1071::svm is then an argument to vcr.svm.train. As e1071::svm does not output the data itself, it needs to be given as well, in the arguments X and y.

Usage

vcr.svm.train(X, y, svfit, ortho = FALSE)
vcr.svm.train(X, y, svfit, ortho = FALSE)

Arguments

`X`	matrix of data coordinates, as used in `e1071::svm`. Missing values are not allowed.
`y`	factor with the given (observed) class labels. It is crucial that X and y are exactly the same as in the call to `e1071::svm`.
`svfit`	an object returned by `e1071::svm`, called with exactly the same `X` and `y` as above.
`ortho`	If `TRUE`, will compute farness in the orthogonal complement of the vector beta given by `e1071::svm`. Is only possible for 2 classes, else there would be several beta vectors.

Value

A list with components:

`yint`	number of the given class of each case. Can contain `NA`'s.
`y`	given class label of each case. Can contain `NA`'s.
`levels`	levels of the response `y`.
`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 `y` is missing.
`altlab`	label of the alternative class. Is `NA` for cases whose `y` is missing.
`PAC`	probability of the alternative class. Is `NA` for cases whose `y` is missing.
`figparams`	parameters used in `fig`, can be used for new data.
`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 `y` is missing.
`ofarness`	for each case $i$ , its lowest `fig[i,g]` to any class `g`. Always exists.
`svfit`	as it was input, will be useful for new data.
`X`	the matrix of data coordinates from the arguments. This is useful for classifying new data.

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. doi:10.1080/00401706.2021.1927849(link to open access pdf)

Examples

library(e1071)
set.seed(1); X <- matrix(rnorm(200 * 2), ncol = 2)
X[1:100, ] <- X[1:100, ] + 2
X[101:150, ] <- X[101:150, ] - 2
y <- as.factor(c(rep("blue", 150), rep("red", 50)))
cols <- c("deepskyblue3", "red")
plot(X, col = cols[as.numeric(y)], pch = 19)
# We now fit an SVM with radial basis kernel to the data:
set.seed(1) # to make the result of svm() reproducible.
svmfit <- svm(y~., data = data.frame(X = X, y = y),
scale = FALSE, kernel = "radial", cost = 10,
gamma = 1, probability = TRUE)
plot(svmfit$decision.values, col = cols[as.numeric(y)]); abline(h = 0)
# so the decision values separate the classes reasonably well.
plot(svmfit, data = data.frame(X = X, y = y), X.2~X.1, col = cols)
# The boundary is far from linear (but in feature space it is).
vcr.train <- vcr.svm.train(X, y, svfit = svmfit)
confmat.vcr(vcr.train)
stackedplot(vcr.train, classCols = cols)
classmap(vcr.train, "blue", classCols = cols)
classmap(vcr.train, "red", classCols = cols)

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

## End(Not run)
library(e1071)
set.seed(1); X <- matrix(rnorm(200 * 2), ncol = 2)
X[1:100, ] <- X[1:100, ] + 2
X[101:150, ] <- X[101:150, ] - 2
y <- as.factor(c(rep("blue", 150), rep("red", 50)))
cols <- c("deepskyblue3", "red")
plot(X, col = cols[as.numeric(y)], pch = 19)
# We now fit an SVM with radial basis kernel to the data:
set.seed(1) # to make the result of svm() reproducible.
svmfit <- svm(y~., data = data.frame(X = X, y = y),
scale = FALSE, kernel = "radial", cost = 10,
gamma = 1, probability = TRUE)
plot(svmfit$decision.values, col = cols[as.numeric(y)]); abline(h = 0)
# so the decision values separate the classes reasonably well.
plot(svmfit, data = data.frame(X = X, y = y), X.2~X.1, col = cols)
# The boundary is far from linear (but in feature space it is).
vcr.train <- vcr.svm.train(X, y, svfit = svmfit)
confmat.vcr(vcr.train)
stackedplot(vcr.train, classCols = cols)
classmap(vcr.train, "blue", classCols = cols)
classmap(vcr.train, "red", classCols = cols)

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

## End(Not run)

Package 'classmap'

Help Index

Draw the class map to visualize classification results.

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Build a confusion matrix from the output of a function vcr.*.*.

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Amazon book reviews data

Description

Usage

Format

Source

Examples

Floral buds data

Description

Usage

Format

Source

Examples

Instagram data

Description

Usage

Format

Source

Examples

Titanic data

Description

Usage

Format

Source

Examples

Constructs feature vectors from a kernel matrix.

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Compute kernel matrix

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Draw a quasi residual plot of PAC versus a data feature

Description

Usage

Arguments

Value

Author(s)

References

Examples

Draw the silhouette plot of a classification

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Build a confusion matrix from the output of a function `vcr..`.