Package 'fdm2id' reference manual

Title:	Data Mining and R Programming for Beginners
Description:	Contains functions to simplify the use of data mining methods (classification, regression, clustering, etc.), for students and beginners in R programming. Various R packages are used and wrappers are built around the main functions, to standardize the use of data mining methods (input/output): it brings a certain loss of flexibility, but also a gain of simplicity. The package name came from the French "Fouille de Données en Master 2 Informatique Décisionnelle".
Authors:	Alexandre Blansché [aut, cre]
Maintainer:	Alexandre Blansché <[email protected]>
License:	GPL-3
Version:	0.9.9
Built:	2024-11-03 04:52:53 UTC
Source:	https://github.com/cran/fdm2id

Sample of car accident location in the UK during year 2014.

Description

Longitude and latitude of 500 car accident during year 2014 (source: www.data.gov.uk).

Usage

accident2014
accident2014

Format

The dataset has 500 instances described by 2 variables (coordinates).

Source

https://www.data.gov.uk/

Classification using AdaBoost

Description

Ensemble learning, through AdaBoost Algorithm.

Usage

ADABOOST(
  x,
  y,
  learningmethod,
  nsamples = 100,
  fuzzy = FALSE,
  tune = FALSE,
  seed = NULL,
  ...
)
ADABOOST(
  x,
  y,
  learningmethod,
  nsamples = 100,
  fuzzy = FALSE,
  tune = FALSE,
  seed = NULL,
  ...
)

Arguments

`x`	The dataset (description/predictors), a `matrix` or `data.frame`.
`y`	The target (class labels or numeric values), a `factor` or `vector`.
`learningmethod`	The boosted method.
`nsamples`	The number of samplings.
`fuzzy`	Indicates whether or not fuzzy classification should be used or not.
`tune`	If true, the function returns paramters instead of a classification model.
`seed`	A specified seed for random number generation.
`...`	Other specific parameters for the leaning method.

Value

The classification model.

Examples

## Not run: 
require (datasets)
data (iris)
ADABOOST (iris [, -5], iris [, 5], NB)

## End(Not run)
## Not run: 
require (datasets)
data (iris)
ADABOOST (iris [, -5], iris [, 5], NB)

## End(Not run)

Alcohol dataset

Description

This dataset has been extracted from the WHO database and depict the alcool habits in the 27 european contries (in 2010).

Usage

alcohol
alcohol

Format

The dataset has 27 instances described by 4 variables. The variables are the average amount of alcool of different types per year par inhabitent.

Source

https://www.who.int/

Classification using APRIORI

Description

This function builds a classification model using the association rules method APRIORI.

Usage

APRIORI(
  train,
  labels,
  supp = 0.05,
  conf = 0.8,
  prune = FALSE,
  tune = FALSE,
  ...
)
APRIORI(
  train,
  labels,
  supp = 0.05,
  conf = 0.8,
  prune = FALSE,
  tune = FALSE,
  ...
)

Arguments

`train`	The training set (description), as a `data.frame`.
`labels`	Class labels of the training set (`vector` or `factor`).
`supp`	The minimal support of an item set (numeric value).
`conf`	The minimal confidence of an item set (numeric value).
`prune`	A logical indicating whether to prune redundant rules or not (default: `FALSE`).
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other parameters.

Value

The classification model, as an object of class apriori.

Examples

require ("datasets")
data (iris)
d = discretizeDF (iris,
    default = list (method = "interval", breaks = 3, labels = c ("small", "medium", "large")))
APRIORI (d [, -5], d [, 5], supp = .1, conf = .9, prune = TRUE)
require ("datasets")
data (iris)
d = discretizeDF (iris,
    default = list (method = "interval", breaks = 3, labels = c ("small", "medium", "large")))
APRIORI (d [, -5], d [, 5], supp = .1, conf = .9, prune = TRUE)

APRIORI classification model

Description

This class contains the classification model obtained by the APRIORI association rules method.

Slots

rules: The set of rules obtained by APRIORI.
transactions: The training set as a transaction object.
train: The training set (description). A matrix or data.frame.
labels: Class labels of the training set. Either a factor or an integer vector.
supp: The minimal support of an item set (numeric value).
conf: The minimal confidence of an item set (numeric value).

Duplicate and add noise to a dataset

Description

This function is a data augmentation technique. It duplicates rows and add gaussian noise to the duplicates.

Usage

augmentation(dataset, target, n = 5, sigma = 0.1, seed = NULL)
augmentation(dataset, target, n = 5, sigma = 0.1, seed = NULL)

Arguments

`dataset`	The dataset to be split (`data.frame` or `matrix`).
`target`	The column index of the target variable (class label or response variable).
`n`	The scaling factor (as an integer value).
`sigma`	The baseline variance for the noise generation.
`seed`	A specified seed for random number generation.

Value

An augmented dataset.

Examples

require (datasets)
data (iris)
d = augmentation (iris, 5)
summary (iris)
summary (d)
require (datasets)
data (iris)
d = augmentation (iris, 5)
summary (iris)
summary (d)

Auto MPG dataset

Description

This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. The dataset was used in the 1983 American Statistical Association Exposition.

Usage

autompg
autompg

Format

The dataset has 392 instances described by 8 variables. The seven first variables are numeric variables. The last variable is qualitative (car origin).

Source

https://archive.ics.uci.edu/ml/datasets/auto+mpg

Classification using Bagging

Description

Ensemble learning, through Bagging Algorithm.

Usage

BAGGING(
  x,
  y,
  learningmethod,
  nsamples = 100,
  bag.size = nrow(x),
  seed = NULL,
  ...
)
BAGGING(
  x,
  y,
  learningmethod,
  nsamples = 100,
  bag.size = nrow(x),
  seed = NULL,
  ...
)

Arguments

`x`	The dataset (description/predictors), a `matrix` or `data.frame`.
`y`	The target (class labels or numeric values), a `factor` or `vector`.
`learningmethod`	The boosted method.
`nsamples`	The number of samplings.
`bag.size`	The size of the samples.
`seed`	A specified seed for random number generation.
`...`	Other specific parameters for the leaning method.

Value

The classification model.

Examples

## Not run: 
require (datasets)
data (iris)
BAGGING (iris [, -5], iris [, 5], NB)

## End(Not run)
## Not run: 
require (datasets)
data (iris)
BAGGING (iris [, -5], iris [, 5], NB)

## End(Not run)

Data were collected on the genus of flea beetle Chaetocnema, which contains three species: concinna, heikertingeri, and heptapotamica. Measurements were made on the width and angle of the aedeagus of each beetle. The goal of the original study was to form a classification rule to distinguish the three species.

Usage

beetles
beetles

Format

The dataset has 74 instances described by 3 variables. The variables are as follows:

Width: The maximal width of aedeagus in the forpart (in microns).
Angle: The front angle of the aedeagus (1 unit = 7.5 degrees).
Shot.put: Species of flea beetle from the genus Chaetocnema.

Source

Lubischew, A.A. (1962) On the use of discriminant functions in taxonomy. Biometrics, 18, 455-477.

Birth dataset

Description

Tutorial data set (vector).

Usage

birth
birth

Format

The dataset is a names vector of nine values (birth years).

Boosting methods model

Description

This class contains the classification model obtained by the CDA method.

Slots

models: List of models.
x: The learning set.
y: The target values.

Clustering Box Plots

Description

Produce a box-and-whisker plot for clustering results.

Usage

boxclus(d, clusters, legendpos = "topleft", ...)
boxclus(d, clusters, legendpos = "topleft", ...)

Arguments

`d`	The dataset (`matrix` or `data.frame`).
`clusters`	Cluster labels of the training set (`vector` or `factor`).
`legendpos`	Position of the legend
`...`	Other parameters.

Examples

require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
boxclus (iris [, -5], km$cluster)
require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
boxclus (iris [, -5], km$cluster)

Population and location of 18 major british cities.

Description

Longitude and latitude and population of 18 major cities in the Great Britain.

Usage

britpop
britpop

Format

The dataset has 18 instances described by 3 variables.

Correspondence Analysis (CA)

Description

Performs Correspondence Analysis (CA) including supplementary row and/or column points.

Usage

CA(
  d,
  ncp = 5,
  row.sup = NULL,
  col.sup = NULL,
  quanti.sup = NULL,
  quali.sup = NULL,
  row.w = NULL
)
CA(
  d,
  ncp = 5,
  row.sup = NULL,
  col.sup = NULL,
  quanti.sup = NULL,
  quali.sup = NULL,
  row.w = NULL
)

Arguments

`d`	A ddata frame or a table with n rows and p columns, i.e. a contingency table.
`ncp`	The number of dimensions kept in the results (by default 5).
`row.sup`	A vector indicating the indexes of the supplementary rows.
`col.sup`	A vector indicating the indexes of the supplementary columns.
`quanti.sup`	A vector indicating the indexes of the supplementary continuous variables.
`quali.sup`	A vector indicating the indexes of the categorical supplementary variables.
`row.w`	An optional row weights (by default, a vector of 1 for uniform row weights); the weights are given only for the active individuals.

Value

The CA on the dataset.

Examples

data (children, package = "FactoMineR")
CA (children, row.sup = 15:18, col.sup = 6:8)
data (children, package = "FactoMineR")
CA (children, row.sup = 15:18, col.sup = 6:8)

Classification using CART

Description

This function builds a classification model using CART.

Usage

CART(
  train,
  labels,
  minsplit = 1,
  maxdepth = log2(length(labels)),
  cp = NULL,
  tune = FALSE,
  ...
)
CART(
  train,
  labels,
  minsplit = 1,
  maxdepth = log2(length(labels)),
  cp = NULL,
  tune = FALSE,
  ...
)

Arguments

`train`	The training set (description), as a `data.frame`.
`labels`	Class labels of the training set (`vector` or `factor`).
`minsplit`	The minimum leaf size during the learning.
`maxdepth`	Set the maximum depth of any node of the final tree, with the root node counted as depth 0.
`cp`	The complexity parameter of the tree. Cross-validation is used to determine optimal cp if NULL.
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other parameters.

Value

The classification model.

Examples

require (datasets)
data (iris)
CART (iris [, -5], iris [, 5])
require (datasets)
data (iris)
CART (iris [, -5], iris [, 5])

Depth

Description

Return the dept of a decision tree.

Usage

cartdepth(model)
cartdepth(model)

Arguments

model

The decision tree.

Value

The depth.

Examples

require (datasets)
data (iris)
model = CART (iris [, -5], iris [, 5])
cartdepth (model)
require (datasets)
data (iris)
model = CART (iris [, -5], iris [, 5])
cartdepth (model)

CART information

Description

Return various information on a CART model.

Usage

cartinfo(model)
cartinfo(model)

Arguments

model

The decision tree.

Value

Various information organized into a vector.

Examples

require (datasets)
data (iris)
model = CART (iris [, -5], iris [, 5])
cartinfo (model)
require (datasets)
data (iris)
model = CART (iris [, -5], iris [, 5])
cartinfo (model)

Number of Leafs

Description

Return the number of leafs of a decision tree.

Usage

cartleafs(model)
cartleafs(model)

Arguments

model

The decision tree.

Value

The number of leafs.

Examples

require (datasets)
data (iris)
model = CART (iris [, -5], iris [, 5])
cartleafs (model)
require (datasets)
data (iris)
model = CART (iris [, -5], iris [, 5])
cartleafs (model)

Number of Nodes

Description

Return the number of nodes of a decision tree.

Usage

cartnodes(model)
cartnodes(model)

Arguments

model

The decision tree.

Value

The number of nodes.

Examples

require (datasets)
data (iris)
model = CART (iris [, -5], iris [, 5])
cartnodes (model)
require (datasets)
data (iris)
model = CART (iris [, -5], iris [, 5])
cartnodes (model)

CART Plot

Description

Plot a decision tree obtained by CART.

Usage

cartplot(model, ...)
cartplot(model, ...)

Arguments

`model`	The decision tree.
`...`	Other parameters.

Examples

require (datasets)
data (iris)
model = CART (iris [, -5], iris [, 5])
cartplot (model)
require (datasets)
data (iris)
model = CART (iris [, -5], iris [, 5])
cartplot (model)

Classification using Canonical Discriminant Analysis

Description

This function builds a classification model using Canonical Discriminant Analysis.

Usage

CDA(train, labels, tune = FALSE, ...)
CDA(train, labels, tune = FALSE, ...)

Arguments

`train`	The training set (description), as a `data.frame`.
`labels`	Class labels of the training set (`vector` or `factor`).
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other parameters.

Value

The classification model, as an object of class glmnet.

Examples

require (datasets)
data (iris)
CDA (iris [, -5], iris [, 5])
require (datasets)
data (iris)
CDA (iris [, -5], iris [, 5])

Canonical Disciminant Analysis model

Description

This class contains the classification model obtained by the CDA method.

Slots

proj: The projection of the dataset into the canonical base. A data.frame.
transform: The transformation matrix between. A matrix.
centers: Coordinates of the class centers. A matrix.
within: The intr-class covarianc matrix. A matrix.
eig: The eigen-values. A matrix.
dim: The number of dimensions of the canonical base (numeric value).
nb.classes: The number of clusters (numeric value).
train: The training set (description). A data.frame.
labels: Class labels of the training set. Either a factor or an integer vector.
model: The prediction model.

Close a graphics device

Description

Close the graphics device driver

Usage

closegraphics()
closegraphics()

Examples

## Not run: 
data (iris)
exportgraphics ("export.pdf")
plotdata (iris [, -5], iris [, 5])
closegraphics()

## End(Not run)
## Not run: 
data (iris)
exportgraphics ("export.pdf")
plotdata (iris [, -5], iris [, 5])
closegraphics()

## End(Not run)

Comparison of two sets of clusters

Description

Comparison of two sets of clusters

Usage

compare(clus, gt, eval = "accuracy", comp = c("max", "pairwise", "cluster"))
compare(clus, gt, eval = "accuracy", comp = c("max", "pairwise", "cluster"))

Arguments

`clus`	The extracted clusters.
`gt`	The real clusters.
`eval`	The evluation criterion.
`comp`	Indicates whether a "max" or a "pairwise" evaluation should be used, or the evaluation for each individual "cluster".

Value

A numeric value indicating how much the two sets of clusters are similar.

Examples

require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
compare (km$cluster, iris [, 5])
## Not run: 
compare (km$cluster, iris [, 5], eval = c ("accuracy", "kappa"), comp = "pairwise")

## End(Not run)
require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
compare (km$cluster, iris [, 5])
## Not run: 
compare (km$cluster, iris [, 5], eval = c ("accuracy", "kappa"), comp = "pairwise")

## End(Not run)

Comparison of two sets of clusters, using accuracy

Description

Comparison of two sets of clusters, using accuracy

Usage

compare.accuracy(clus, gt, comp = c("max", "pairwise", "cluster"))
compare.accuracy(clus, gt, comp = c("max", "pairwise", "cluster"))

Arguments

`clus`	The extracted clusters.
`gt`	The real clusters.
`comp`	Indicates whether a "max" or a "pairwise" evaluation should be used, or the evaluation for each individual "cluster".

Value

A numeric value indicating how much the two sets of clusters are similar.

Examples

require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
compare.accuracy (km$cluster, iris [, 5])
require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
compare.accuracy (km$cluster, iris [, 5])

Comparison of two sets of clusters, using Jaccard index

Description

Comparison of two sets of clusters, using Jaccard index

Usage

compare.jaccard(clus, gt, comp = c("max", "pairwise", "cluster"))
compare.jaccard(clus, gt, comp = c("max", "pairwise", "cluster"))

Arguments

`clus`	The extracted clusters.
`gt`	The real clusters.
`comp`	Indicates whether a "max" or a "pairwise" evaluation should be used, or the evaluation for each individual "cluster".

Value

A numeric value indicating how much the two sets of clusters are similar.

Examples

require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
compare.jaccard (km$cluster, iris [, 5])
require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
compare.jaccard (km$cluster, iris [, 5])

Comparison of two sets of clusters, using kappa

Description

Comparison of two sets of clusters, using kappa

Usage

compare.kappa(clus, gt, comp = c("max", "pairwise", "cluster"))
compare.kappa(clus, gt, comp = c("max", "pairwise", "cluster"))

Arguments

`clus`	The extracted clusters.
`gt`	The real clusters.
`comp`	Indicates whether a "max" or a "pairwise" evaluation should be used, or the evaluation for each individual "cluster".

Value

A numeric value indicating how much the two sets of clusters are similar.

Examples

require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
compare.kappa (km$cluster, iris [, 5])
require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
compare.kappa (km$cluster, iris [, 5])

Confuion matrix

Description

Plot a confusion matrix.

Usage

confusion(predictions, gt, norm = TRUE, graph = TRUE)
confusion(predictions, gt, norm = TRUE, graph = TRUE)

Arguments

`predictions`	The prediction.
`gt`	The ground truth.
`norm`	Whether or not the confusion matrix is normalized
`graph`	Whether or not a graphic is displayed.

Value

The confusion matrix.

Examples

require ("datasets")
data (iris)
d = splitdata (iris, 5)
model = NB (d$train.x, d$train.y)
pred = predict (model, d$test.x)
confusion (d$test.y, pred)
require ("datasets")
data (iris)
d = splitdata (iris, 5)
model = NB (d$train.x, d$train.y)
pred = predict (model, d$test.x)
confusion (d$test.y, pred)

Cookies dataset

Description

This data set contains measurements from quantitative NIR spectroscopy. The example studied arises from an experiment done to test the feasibility of NIR spectroscopy to measure the composition of biscuit dough pieces (formed but unbaked biscuits). Two similar sample sets were made up, with the standard recipe varied to provide a large range for each of the four constituents under investigation: fat, sucrose, dry flour, and water. The calculated percentages of these four ingredients represent the 4 responses. There are 40 samples in the calibration or training set (with sample 23 being an outlier). There are a further 32 samples in the separate prediction or validation set (with example 21 considered as an outlier). An NIR reflectance spectrum is available for each dough piece. The spectral data consist of 700 points measured from 1100 to 2498 nanometers (nm) in steps of 2 nm.

Usage

cookies
cookies.desc.train
cookies.desc.test
cookies.y.train
cookies.y.test
cookies
cookies.desc.train
cookies.desc.test
cookies.y.train
cookies.y.test

Format

The cookies.desc.* datasets contains the 700 columns that correspond to the NIR reflectance spectrum. The cookies.y.* datasets contains four columns that correspond to the four constituents fat, sucrose, dry flour, and water. The cookies.*.train contains 40 rows that correspond to the calibration data. The cookies.*.test contains 32 rows that correspond to the prediction data.

Source

P. J. Brown and T. Fearn and M. Vannucci (2001) "Bayesian wavelet regression on curves with applications to a spectroscopic calibration problem", Journal of the American Statistical Association, 96(454), pp. 398-408.

Plot the Cook's distance of a linear regression model

Description

Plot the Cook's distance of a linear regression model.

Usage

cookplot(model, index = NULL, labels = NULL)
cookplot(model, index = NULL, labels = NULL)

Arguments

`model`	The model to be plotted.
`index`	The index of the variable used for for the x-axis.
`labels`	The labels of the instances.

Examples

require (datasets)
data (trees)
model = LINREG (trees [, -3], trees [, 3])
cookplot (model)
require (datasets)
data (trees)
model = LINREG (trees [, -3], trees [, 3])
cookplot (model)

Correlated variables

Description

Return the list of correlated variables

Usage

correlated(d, threshold = 0.8)
correlated(d, threshold = 0.8)

Arguments

`d`	A data matrix.
`threshold`	The threshold on the (absolute) Pearson coefficient. If NULL, return the most correlated variables.

Value

The list of correlated variables (as a matrix of column names).

Examples

data (iris)
correlated (iris)
data (iris)
correlated (iris)

Plot Cost Curves

Description

This function plots Cost Curves of several classification predictions.

Usage

cost.curves(predictions, gt, methods.names = NULL)
cost.curves(predictions, gt, methods.names = NULL)

Arguments

`predictions`	The predictions of a classification model (`factor` or `vector`).
`gt`	Actual labels of the dataset (`factor` or `vector`).
`methods.names`	The name of the compared methods (`vector`).

Value

The evaluation of the predictions (numeric value).

Examples

require (datasets)
data (iris)
d = iris
levels (d [, 5]) = c ("+", "+", "-") # Building a two classes dataset
model.nb = NB (d [, -5], d [, 5])
model.lda = LDA (d [, -5], d [, 5])
pred.nb = predict (model.nb, d [, -5])
pred.lda = predict (model.lda, d [, -5])
cost.curves (cbind (pred.nb, pred.lda), d [, 5], c ("NB", "LDA"))
require (datasets)
data (iris)
d = iris
levels (d [, 5]) = c ("+", "+", "-") # Building a two classes dataset
model.nb = NB (d [, -5], d [, 5])
model.lda = LDA (d [, -5], d [, 5])
pred.nb = predict (model.nb, d [, -5])
pred.lda = predict (model.lda, d [, -5])
cost.curves (cbind (pred.nb, pred.lda), d [, 5], c ("NB", "LDA"))

Credit dataset

Description

This is a fake dataset simulating a bank database about loan clients.

Usage

credit
credit

Format

The dataset has 66 instances described by 11 qualitative variables.

Square dataset

Description

Generate a random dataset shaped like a square divided by a custom function

Usage

data.diag(
  n = 200,
  min = 0,
  max = 1,
  f = function(x) x,
  levels = NULL,
  graph = TRUE,
  seed = NULL
)
data.diag(
  n = 200,
  min = 0,
  max = 1,
  f = function(x) x,
  levels = NULL,
  graph = TRUE,
  seed = NULL
)

Arguments

`n`	Number of observations in the dataset.
`min`	Minimum value on each variables.
`max`	Maximum value on each variables.
`f`	The fucntion that separate the classes.
`levels`	Name of each class.
`graph`	A logical indicating whether or not a graphic should be plotted.
`seed`	A specified seed for random number generation.

Value

A randomly generated dataset.

Examples

data.diag ()
data.diag ()

Gaussian mixture dataset

Description

Generate a random multidimentional gaussian mixture.

Usage

data.gauss(
  n = 1000,
  k = 2,
  prob = rep(1/k, k),
  mu = cbind(rep(0, k), seq(from = 0, by = 3, length.out = k)),
  cov = rep(list(matrix(c(6, 0.9, 0.9, 0.3), ncol = 2, nrow = 2)), k),
  levels = NULL,
  graph = TRUE,
  seed = NULL
)
data.gauss(
  n = 1000,
  k = 2,
  prob = rep(1/k, k),
  mu = cbind(rep(0, k), seq(from = 0, by = 3, length.out = k)),
  cov = rep(list(matrix(c(6, 0.9, 0.9, 0.3), ncol = 2, nrow = 2)), k),
  levels = NULL,
  graph = TRUE,
  seed = NULL
)

Arguments

`n`	Number of observations.
`k`	The number of classes.
`prob`	The a priori probability of each class.
`mu`	The means of the gaussian distributions.
`cov`	The covariance of the gaussian distributions.
`levels`	Name of each class.
`graph`	A logical indicating whether or not a graphic should be plotted.
`seed`	A specified seed for random number generation.

Value

A randomly generated dataset.

Examples

data.gauss ()
data.gauss ()

Parabol dataset

Description

Generate a random dataset shaped like a parabol and a gaussian distribution

Usage

data.parabol(
  n = c(500, 100),
  xlim = c(-3, 3),
  center = c(0, 4),
  coeff = 0.5,
  sigma = c(0.5, 0.5),
  levels = NULL,
  graph = TRUE,
  seed = NULL
)
data.parabol(
  n = c(500, 100),
  xlim = c(-3, 3),
  center = c(0, 4),
  coeff = 0.5,
  sigma = c(0.5, 0.5),
  levels = NULL,
  graph = TRUE,
  seed = NULL
)

Arguments

`n`	Number of observations in each class.
`xlim`	Minimum and maximum on the x axis.
`center`	Coordinates of the center of the gaussian distribution.
`coeff`	Coefficient of the parabol.
`sigma`	Variance in each class.
`levels`	Name of each class.
`graph`	A logical indicating whether or not a graphic should be plotted.
`seed`	A specified seed for random number generation.

Value

A randomly generated dataset.

Examples

data.parabol ()
data.parabol ()

Target1 dataset

Description

Generate a random dataset shaped like a target.

Usage

data.target1(
  r = 1:3,
  n = 200,
  sigma = 0.1,
  levels = NULL,
  graph = TRUE,
  seed = NULL
)
data.target1(
  r = 1:3,
  n = 200,
  sigma = 0.1,
  levels = NULL,
  graph = TRUE,
  seed = NULL
)

Arguments

`r`	Radius of each class.
`n`	Number of observations in each class.
`sigma`	Variance in each class.
`levels`	Name of each class.
`graph`	A logical indicating whether or not a graphic should be plotted.
`seed`	A specified seed for random number generation.

Value

A randomly generated dataset.

Examples

data.target1 ()
data.target1 ()

Target2 dataset

Description

Generate a random dataset shaped like a target.

Usage

data.target2(
  minr = c(0, 2),
  maxr = minr + 1,
  initn = 1000,
  levels = NULL,
  graph = TRUE,
  seed = NULL
)
data.target2(
  minr = c(0, 2),
  maxr = minr + 1,
  initn = 1000,
  levels = NULL,
  graph = TRUE,
  seed = NULL
)

Arguments

`minr`	Minimum radius of each class.
`maxr`	Maximum radius of each class.
`initn`	Number of observations at the beginning of the generation process.
`levels`	Name of each class.
`graph`	A logical indicating whether or not a graphic should be plotted.
`seed`	A specified seed for random number generation.

Value

A randomly generated dataset.

Examples

data.target2 ()
data.target2 ()

Two moons dataset

Description

Generate a random dataset shaped like two moons.

Usage

data.twomoons(
  r = 1,
  n = 200,
  sigma = 0.1,
  levels = NULL,
  graph = TRUE,
  seed = NULL
)
data.twomoons(
  r = 1,
  n = 200,
  sigma = 0.1,
  levels = NULL,
  graph = TRUE,
  seed = NULL
)

Arguments

`r`	Radius of each class.
`n`	Number of observations in each class.
`sigma`	Variance in each class.
`levels`	Name of each class.
`graph`	A logical indicating whether or not a graphic should be plotted.
`seed`	A specified seed for random number generation.

Value

A randomly generated dataset.

Examples

data.twomoons ()
data.twomoons ()

XOR dataset

Description

Generate "XOR" dataset.

Usage

data.xor(
  n = 100,
  ndim = 2,
  sigma = 0.25,
  levels = NULL,
  graph = TRUE,
  seed = NULL
)
data.xor(
  n = 100,
  ndim = 2,
  sigma = 0.25,
  levels = NULL,
  graph = TRUE,
  seed = NULL
)

Arguments

`n`	Number of observations in each cluster.
`ndim`	The number of dimensions (2^ndim clusters are formed, grouped into two classes).
`sigma`	The variance.
`levels`	Name of each class.
`graph`	A logical indicating whether or not a graphic should be plotted.
`seed`	A specified seed for random number generation.

Value

A randomly generated dataset.

Examples

data.xor ()
data.xor ()

"data1" dataset

Description

Synthetic dataset.

Usage

data1
data1

Format

240 observations described by 4 variables and grouped into 16 classes.

Author(s)

Alexandre Blansché [email protected]

"data2" dataset

Description

Synthetic dataset.

Usage

data2
data2

Format

500 observations described by 10 variables and grouped into 3 classes.

Author(s)

Alexandre Blansché [email protected]

"data3" dataset

Description

Synthetic dataset.

Usage

data3
data3

Format

300 observations described by 3 variables and grouped into 3 classes.

Author(s)

Alexandre Blansché [email protected]

Training set and test set

Description

This class contains a dataset divided into four parts: the training set and test set, description and class labels.

Slots

train.x: the training set (description), as a data.frame or a matrix.
train.y: the training set (target), as a vector or a factor.
test.x: the training set (description), as a data.frame or a matrix.
test.y: the training set (target), as a vector or a factor.

DBSCAN model

Description

This class contains the model obtained by the DBSCAN method.

Slots

cluster: A vector of integers indicating the cluster to which each point is allocated.
eps: Reachability distance (parameter).
MinPts: Reachability minimum no. of points (parameter).
isseed: A logical vector indicating whether a point is a seed (not border, not noise).
data: The dataset that has been used to fit the map (as a matrix).

DBSCAN clustering method

Description

Run the DBSCAN algorithm for clustering.

Usage

DBSCAN(d, minpts, epsilonDist, ...)
DBSCAN(d, minpts, epsilonDist, ...)

Arguments

`d`	The dataset (`matrix` or `data.frame`).
`minpts`	Reachability minimum no. of points.
`epsilonDist`	Reachability distance.
`...`	Other parameters.

Value

A clustering model obtained by DBSCAN.

Examples

require (datasets)
data (iris)
DBSCAN (iris [, -5], minpts = 5, epsilonDist = 1)
require (datasets)
data (iris)
DBSCAN (iris [, -5], minpts = 5, epsilonDist = 1)

Decathlon dataset

Description

The dataset contains results from two athletics competitions. The 2004 Olympic Games in Athens and the 2004 Decastar.

Usage

decathlon
decathlon

Format

The dataset has 41 instances described by 13 variables. The variables are as follows:

100m: In seconds.
Long.jump: In meters.
Shot.put: In meters.
High.jump: In meters.
400m: In seconds.
110m.h: In seconds.
Discus.throw: In meters.
Pole.vault: In meters.
Javelin.throw: In meters.
1500m: In seconds.
Rank: The rank at the competition.
Points: The number of points obtained by the athlete.
Competition: Olympics or Decastar.

Source

https://husson.github.io/data.html

Plot a k-distance graphic

Description

Plot the distance to the k's nearest neighbours of each object in decreasing order. Mostly used to determine the eps parameter for the dbscan function.

Usage

distplot(k, d, h = -1)
distplot(k, d, h = -1)

Arguments

`k`	The `k` parameter.
`d`	The dataset (`matrix` or `data.frame`).
`h`	The y-coordinate at which a horizontal line should be drawn.

Examples

require (datasets)
data (iris)
distplot (5, iris [, -5], h = .65)
require (datasets)
data (iris)
distplot (5, iris [, -5], h = .65)

Expectation-Maximization clustering method

Description

Run the EM algorithm for clustering.

Usage

EM(d, clusters, model = "VVV", ...)
EM(d, clusters, model = "VVV", ...)

Arguments

`d`	The dataset (`matrix` or `data.frame`).
`clusters`	Either an integer (the number of clusters) or a (`vector`) indicating the cluster to which each point is initially allocated.
`model`	A character string indicating the model. The help file for `mclustModelNames` describes the available models.
`...`	Other parameters.

Value

A clustering model obtained by EM.

Examples

require (datasets)
data (iris)
EM (iris [, -5], 3) # Default initialization
km = KMEANS (iris [, -5], k = 3)
EM (iris [, -5], km$cluster) # Initialization with another clustering method
require (datasets)
data (iris)
EM (iris [, -5], 3) # Default initialization
km = KMEANS (iris [, -5], k = 3)
EM (iris [, -5], km$cluster) # Initialization with another clustering method

Expectation-Maximization model

Description

This class contains the model obtained by the EM method.

Slots

modelName: A character string indicating the model. The help file for mclustModelNames describes the available models.
prior: Specification of a conjugate prior on the means and variances.
n: The number of observations in the dataset.
d: The number of variables in the dataset.
G: The number of components of the mixture.
z: A matrix whose [i,k]th entry is the conditional probability of the ith observation belonging to the kth component of the mixture.
parameters: A names list giving the parameters of the model.
control: A list of control parameters for EM.
loglik: The log likelihood for the data in the mixture model.
cluster: A vector of integers (from 1:k) indicating the cluster to which each point is allocated.

Eucalyptus dataset

Description

Measuring the height of a tree is not an easy task. Is it possible to estimate the height as a function of the circumference of the trunk?

Usage

eucalyptus
eucalyptus

Format

The dataset has 1429 instances (eucalyptus trees) with 2 measurements: the height and the circumference.

Source

http://www.cmap.polytechnique.fr/~lepennec/fr/teaching/

Evaluation of classification or regression predictions

Description

Evaluation predictions of a classification or a regression model.

Usage

evaluation(
  predictions,
  gt,
  eval = ifelse(is.factor(gt), "accuracy", "r2"),
  ...
)
evaluation(
  predictions,
  gt,
  eval = ifelse(is.factor(gt), "accuracy", "r2"),
  ...
)

Arguments

`predictions`	The predictions of a classification model (`factor` or `vector`).
`gt`	The ground truth of the dataset (`factor` or `vector`).
`eval`	The evaluation method.
`...`	Other parameters.

Value

The evaluation of the predictions (numeric value).

Examples

require (datasets)
data (iris)
d = splitdata (iris, 5)
model.nb = NB (d$train.x, d$train.y)
pred.nb = predict (model.nb, d$test.x)
# Default evaluation for classification
evaluation (pred.nb, d$test.y)
# Evaluation with two criteria
evaluation (pred.nb, d$test.y, eval = c ("accuracy", "kappa"))
data (trees)
d = splitdata (trees, 3)
model.linreg = LINREG (d$train.x, d$train.y)
pred.linreg = predict (model.linreg, d$test.x)
# Default evaluation for regression
evaluation (pred.linreg, d$test.y)
require (datasets)
data (iris)
d = splitdata (iris, 5)
model.nb = NB (d$train.x, d$train.y)
pred.nb = predict (model.nb, d$test.x)
# Default evaluation for classification
evaluation (pred.nb, d$test.y)
# Evaluation with two criteria
evaluation (pred.nb, d$test.y, eval = c ("accuracy", "kappa"))
data (trees)
d = splitdata (trees, 3)
model.linreg = LINREG (d$train.x, d$train.y)
pred.linreg = predict (model.linreg, d$test.x)
# Default evaluation for regression
evaluation (pred.linreg, d$test.y)

Accuracy of classification predictions

Description

Evaluation predictions of a classification model according to accuracy.

Usage

evaluation.accuracy(predictions, gt, ...)
evaluation.accuracy(predictions, gt, ...)

Arguments

`predictions`	The predictions of a classification model (`factor` or `vector`).
`gt`	The ground truth (`factor` or `vector`).
`...`	Other parameters.

Value

The evaluation of the predictions (numeric value).

Examples

require (datasets)
data (iris)
d = splitdata (iris, 5)
model.nb = NB (d$train.x, d$train.y)
pred.nb = predict (model.nb, d$test.x)
evaluation.accuracy (pred.nb, d$test.y)
require (datasets)
data (iris)
d = splitdata (iris, 5)
model.nb = NB (d$train.x, d$train.y)
pred.nb = predict (model.nb, d$test.x)
evaluation.accuracy (pred.nb, d$test.y)

Adjusted R2 evaluation of regression predictions

Description

Evaluation predictions of a regression model according to R2

Usage

evaluation.adjr2(predictions, gt, nrow = length(predictions), ncol, ...)
evaluation.adjr2(predictions, gt, nrow = length(predictions), ncol, ...)

Arguments

`predictions`	The predictions of a regression model (`vector`).
`gt`	The ground truth (`vector`).
`nrow`	Number of observations.
`ncol`	Number of variables
`...`	Other parameters.

Value

The evaluation of the predictions (numeric value).

Examples

require (datasets)
data (trees)
d = splitdata (trees, 3)
model.linreg = LINREG (d$train.x, d$train.y)
pred.linreg = predict (model.linreg, d$test.x)
evaluation.r2 (pred.linreg, d$test.y)
require (datasets)
data (trees)
d = splitdata (trees, 3)
model.linreg = LINREG (d$train.x, d$train.y)
pred.linreg = predict (model.linreg, d$test.x)
evaluation.r2 (pred.linreg, d$test.y)

F-measure

Description

Evaluation predictions of a classification model according to the F-measure index.

Usage

evaluation.fmeasure(predictions, gt, beta = 1, positive = levels(gt)[1], ...)
evaluation.fmeasure(predictions, gt, beta = 1, positive = levels(gt)[1], ...)

Arguments

`predictions`	The predictions of a classification model (`factor` or `vector`).
`gt`	The ground truth (`factor` or `vector`).
`beta`	The weight given to precision.
`positive`	The label of the positive class.
`...`	Other parameters.

Value

The evaluation of the predictions (numeric value).

Examples

require (datasets)
data (iris)
d = iris
levels (d [, 5]) = c ("+", "+", "-") # Building a two classes dataset
d = splitdata (d, 5)
model.nb = NB (d$train.x, d$train.y)
pred.nb = predict (model.nb, d$test.x)
evaluation.fmeasure (pred.nb, d$test.y)
require (datasets)
data (iris)
d = iris
levels (d [, 5]) = c ("+", "+", "-") # Building a two classes dataset
d = splitdata (d, 5)
model.nb = NB (d$train.x, d$train.y)
pred.nb = predict (model.nb, d$test.x)
evaluation.fmeasure (pred.nb, d$test.y)

Fowlkes–Mallows index

Description

Evaluation predictions of a classification model according to the Fowlkes–Mallows index.

Usage

evaluation.fowlkesmallows(predictions, gt, positive = levels(gt)[1], ...)
evaluation.fowlkesmallows(predictions, gt, positive = levels(gt)[1], ...)

Arguments

`predictions`	The predictions of a classification model (`factor` or `vector`).
`gt`	The ground truth (`factor` or `vector`).
`positive`	The label of the positive class.
`...`	Other parameters.

Value

The evaluation of the predictions (numeric value).

Examples

require (datasets)
data (iris)
d = iris
levels (d [, 5]) = c ("+", "+", "-") # Building a two classes dataset
d = splitdata (d, 5)
model.nb = NB (d$train.x, d$train.y)
pred.nb = predict (model.nb, d$test.x)
evaluation.fowlkesmallows (pred.nb, d$test.y)
require (datasets)
data (iris)
d = iris
levels (d [, 5]) = c ("+", "+", "-") # Building a two classes dataset
d = splitdata (d, 5)
model.nb = NB (d$train.x, d$train.y)
pred.nb = predict (model.nb, d$test.x)
evaluation.fowlkesmallows (pred.nb, d$test.y)

Goodness

Description

Evaluation predictions of a classification model according to Goodness index.

Usage

evaluation.goodness(predictions, gt, beta = 1, positive = levels(gt)[1], ...)
evaluation.goodness(predictions, gt, beta = 1, positive = levels(gt)[1], ...)

Arguments

`predictions`	The predictions of a classification model (`factor` or `vector`).
`gt`	The ground truth (`factor` or `vector`).
`beta`	The weight given to precision.
`positive`	The label of the positive class.
`...`	Other parameters.

Value

The evaluation of the predictions (numeric value).

Examples

require (datasets)
data (iris)
d = iris
levels (d [, 5]) = c ("+", "+", "-") # Building a two classes dataset
d = splitdata (d, 5)
model.nb = NB (d$train.x, d$train.y)
pred.nb = predict (model.nb, d$test.x)
evaluation.goodness (pred.nb, d$test.y)
require (datasets)
data (iris)
d = iris
levels (d [, 5]) = c ("+", "+", "-") # Building a two classes dataset
d = splitdata (d, 5)
model.nb = NB (d$train.x, d$train.y)
pred.nb = predict (model.nb, d$test.x)
evaluation.goodness (pred.nb, d$test.y)

Jaccard index

Description

Evaluation predictions of a classification model according to Jaccard index.

Usage

evaluation.jaccard(predictions, gt, positive = levels(gt)[1], ...)
evaluation.jaccard(predictions, gt, positive = levels(gt)[1], ...)

Arguments

`predictions`	The predictions of a classification model (`factor` or `vector`).
`gt`	The ground truth (`factor` or `vector`).
`positive`	The label of the positive class.
`...`	Other parameters.

Value

The evaluation of the predictions (numeric value).

Examples

require (datasets)
data (iris)
d = iris
levels (d [, 5]) = c ("+", "+", "-") # Building a two classes dataset
d = splitdata (d, 5)
model.nb = NB (d$train.x, d$train.y)
pred.nb = predict (model.nb, d$test.x)
evaluation.jaccard (pred.nb, d$test.y)
require (datasets)
data (iris)
d = iris
levels (d [, 5]) = c ("+", "+", "-") # Building a two classes dataset
d = splitdata (d, 5)
model.nb = NB (d$train.x, d$train.y)
pred.nb = predict (model.nb, d$test.x)
evaluation.jaccard (pred.nb, d$test.y)

Kappa evaluation of classification predictions

Description

Evaluation predictions of a classification model according to kappa.

Usage

evaluation.kappa(predictions, gt, ...)
evaluation.kappa(predictions, gt, ...)

Arguments

`predictions`	The predictions of a classification model (`factor` or `vector`).
`gt`	The ground truth (`factor` or `vector`).
`...`	Other parameters.

Value

The evaluation of the predictions (numeric value).

Examples

require (datasets)
data (iris)
d = splitdata (iris, 5)
model.nb = NB (d$train.x, d$train.y)
pred.nb = predict (model.nb, d$test.x)
evaluation.kappa (pred.nb, d$test.y)
require (datasets)
data (iris)
d = splitdata (iris, 5)
model.nb = NB (d$train.x, d$train.y)
pred.nb = predict (model.nb, d$test.x)
evaluation.kappa (pred.nb, d$test.y)

MSEP evaluation of regression predictions

Description

Evaluation predictions of a regression model according to MSEP

Usage

evaluation.msep(predictions, gt, ...)
evaluation.msep(predictions, gt, ...)

Arguments

`predictions`	The predictions of a regression model (`vector`).
`gt`	The ground truth (`vector`).
`...`	Other parameters.

Value

The evaluation of the predictions (numeric value).

Examples

require (datasets)
data (trees)
d = splitdata (trees, 3)
model.lin = LINREG (d$train.x, d$train.y)
pred.lin = predict (model.lin, d$test.x)
evaluation.msep (pred.lin, d$test.y)
require (datasets)
data (trees)
d = splitdata (trees, 3)
model.lin = LINREG (d$train.x, d$train.y)
pred.lin = predict (model.lin, d$test.x)
evaluation.msep (pred.lin, d$test.y)

Precision of classification predictions

Description

Evaluation predictions of a classification model according to precision. Works only for two classes problems.

Usage

evaluation.precision(predictions, gt, positive = levels(gt)[1], ...)
evaluation.precision(predictions, gt, positive = levels(gt)[1], ...)

Arguments

`predictions`	The predictions of a classification model (`factor` or `vector`).
`gt`	The ground truth (`factor` or `vector`).
`positive`	The label of the positive class.
`...`	Other parameters.

Value

The evaluation of the predictions (numeric value).

Examples

require (datasets)
data (iris)
d = iris
levels (d [, 5]) = c ("+", "+", "-") # Building a two classes dataset
d = splitdata (d, 5)
model.nb = NB (d$train.x, d$train.y)
pred.nb = predict (model.nb, d$test.x)
evaluation.precision (pred.nb, d$test.y)
require (datasets)
data (iris)
d = iris
levels (d [, 5]) = c ("+", "+", "-") # Building a two classes dataset
d = splitdata (d, 5)
model.nb = NB (d$train.x, d$train.y)
pred.nb = predict (model.nb, d$test.x)
evaluation.precision (pred.nb, d$test.y)

R2 evaluation of regression predictions

Description

Evaluation predictions of a regression model according to R2

Usage

evaluation.r2(predictions, gt, ...)
evaluation.r2(predictions, gt, ...)

Arguments

`predictions`	The predictions of a regression model (`vector`).
`gt`	The ground truth (`vector`).
`...`	Other parameters.

Value

The evaluation of the predictions (numeric value).

Examples

require (datasets)
data (trees)
d = splitdata (trees, 3)
model.linreg = LINREG (d$train.x, d$train.y)
pred.linreg = predict (model.linreg, d$test.x)
evaluation.r2 (pred.linreg, d$test.y)
require (datasets)
data (trees)
d = splitdata (trees, 3)
model.linreg = LINREG (d$train.x, d$train.y)
pred.linreg = predict (model.linreg, d$test.x)
evaluation.r2 (pred.linreg, d$test.y)

Recall of classification predictions

Description

Evaluation predictions of a classification model according to recall. Works only for two classes problems.

Usage

evaluation.recall(predictions, gt, positive = levels(gt)[1], ...)
evaluation.recall(predictions, gt, positive = levels(gt)[1], ...)

Arguments

`predictions`	The predictions of a classification model (`factor` or `vector`).
`gt`	The ground truth (`factor` or `vector`).
`positive`	The label of the positive class.
`...`	Other parameters.

Value

The evaluation of the predictions (numeric value).

Examples

require (datasets)
data (iris)
d = iris
levels (d [, 5]) = c ("+", "+", "-") # Building a two classes dataset
d = splitdata (d, 5)
model.nb = NB (d$train.x, d$train.y)
pred.nb = predict (model.nb, d$test.x)
evaluation.recall (pred.nb, d$test.y)
require (datasets)
data (iris)
d = iris
levels (d [, 5]) = c ("+", "+", "-") # Building a two classes dataset
d = splitdata (d, 5)
model.nb = NB (d$train.x, d$train.y)
pred.nb = predict (model.nb, d$test.x)
evaluation.recall (pred.nb, d$test.y)

Open a graphics device

Description

Starts the graphics device driver

Usage

exportgraphics(file, type = tail(strsplit(file, split = "\\.")[[1]], 1), ...)
exportgraphics(file, type = tail(strsplit(file, split = "\\.")[[1]], 1), ...)

Arguments

`file`	A character string giving the name of the file.
`type`	The type of graphics device.
`...`	Other parameters.

Examples

## Not run: 
data (iris)
exportgraphics ("export.pdf")
plotdata (iris [, -5], iris [, 5])
closegraphics()

## End(Not run)
## Not run: 
data (iris)
exportgraphics ("export.pdf")
plotdata (iris [, -5], iris [, 5])
closegraphics()

## End(Not run)

Toggle graphic exports

Description

Toggle graphic exports on and off

Usage

exportgraphics.off()

exportgraphics.on()

toggleexport(export = NULL)

toggleexport.off()

toggleexport.on()
exportgraphics.off()

exportgraphics.on()

toggleexport(export = NULL)

toggleexport.off()

toggleexport.on()

Arguments

export

If TRUE, exports are activated, if FALSE, exports are deactivated. If null, switches on and off.

Examples

## Not run: 
data (iris)
toggleexport (FALSE)
exportgraphics ("export.pdf")
plotdata (iris [, -5], iris [, 5])
closegraphics()
toggleexport (TRUE)
exportgraphics ("export.pdf")
plotdata (iris [, -5], iris [, 5])
closegraphics()

## End(Not run)
## Not run: 
data (iris)
toggleexport (FALSE)
exportgraphics ("export.pdf")
plotdata (iris [, -5], iris [, 5])
closegraphics()
toggleexport (TRUE)
exportgraphics ("export.pdf")
plotdata (iris [, -5], iris [, 5])
closegraphics()

## End(Not run)

Factorial analysis results

Description

This class contains the classification model obtained by the CDA method.

Classification with Feature selection

Description

Apply a classification method after a subset of features has been selected.

Usage

FEATURESELECTION(
  train,
  labels,
  algorithm = c("ranking", "forward", "backward", "exhaustive"),
  unieval = if (algorithm[1] == "ranking") c("fisher", "fstat", "relief", "inertiaratio")
    else NULL,
  uninb = NULL,
  unithreshold = NULL,
  multieval = if (algorithm[1] == "ranking") NULL else c("cfs", "fstat", "inertiaratio",
    "wrapper"),
  wrapmethod = NULL,
  mainmethod = wrapmethod,
  tune = FALSE,
  ...
)
FEATURESELECTION(
  train,
  labels,
  algorithm = c("ranking", "forward", "backward", "exhaustive"),
  unieval = if (algorithm[1] == "ranking") c("fisher", "fstat", "relief", "inertiaratio")
    else NULL,
  uninb = NULL,
  unithreshold = NULL,
  multieval = if (algorithm[1] == "ranking") NULL else c("cfs", "fstat", "inertiaratio",
    "wrapper"),
  wrapmethod = NULL,
  mainmethod = wrapmethod,
  tune = FALSE,
  ...
)

Arguments

`train`	The training set (description), as a `data.frame`.
`labels`	Class labels of the training set (`vector` or `factor`).
`algorithm`	The feature selection algorithm.
`unieval`	The (univariate) evaluation criterion. `uninb`, `unithreshold` or `multieval` must be specified.
`uninb`	The number of selected feature (univariate evaluation).
`unithreshold`	The threshold for selecting feature (univariate evaluation).
`multieval`	The (multivariate) evaluation criterion.
`wrapmethod`	The classification method used for the wrapper evaluation.
`mainmethod`	The final method used for data classification. If a wrapper evaluation is used, the same classification method should be used.
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other parameters.

Examples

## Not run: 
require (datasets)
data (iris)
FEATURESELECTION (iris [, -5], iris [, 5], uninb = 2, mainmethod = LDA)

## End(Not run)
## Not run: 
require (datasets)
data (iris)
FEATURESELECTION (iris [, -5], iris [, 5], uninb = 2, mainmethod = LDA)

## End(Not run)

Filtering a set of rules

Description

This function facilitate the selection of a subset from a set of rules.

Usage

filter.rules(
  rules,
  pattern = NULL,
  left = pattern,
  right = pattern,
  removeMatches = FALSE
)
filter.rules(
  rules,
  pattern = NULL,
  left = pattern,
  right = pattern,
  removeMatches = FALSE
)

Arguments

`rules`	A set of rules.
`pattern`	A pattern to match (antecedent and consequent): a character string.
`left`	A pattern to match (antecedent only): a character string.
`right`	A pattern to match (consequent only): a character string.
`removeMatches`	A logical indicating whether to remove matching rules (`TRUE`) or to keep those (`FALSE`).

Value

The filtered set of rules.

Examples

require ("arules")
data ("Adult")
r = apriori (Adult)
filter.rules (r, right = "marital-status=")
subset (r, subset = rhs %pin% "marital-status=")
require ("arules")
data ("Adult")
r = apriori (Adult)
filter.rules (r, right = "marital-status=")
subset (r, subset = rhs %pin% "marital-status=")

Frequent words

Description

Most frequent words of the corpus.

Usage

frequentwords(
  corpus,
  nb,
  mincount = 5,
  minphrasecount = NULL,
  ngram = 1,
  lang = "en",
  stopwords = lang
)
frequentwords(
  corpus,
  nb,
  mincount = 5,
  minphrasecount = NULL,
  ngram = 1,
  lang = "en",
  stopwords = lang
)

Arguments

`corpus`	The corpus of documents (a vector of characters) or the vocabulary of the documents (result of function `getvocab`).
`nb`	The number of words to be returned.
`mincount`	Minimum word count to be considered as frequent.
`minphrasecount`	Minimum collocation of words count to be considered as frequent.
`ngram`	maximum size of n-grams.
`lang`	The language of the documents (NULL if no stemming).
`stopwords`	Stopwords, or the language of the documents. NULL if stop words should not be removed.

Value

The most frequent words of the corpus.

Examples

## Not run: 
text = loadtext ("http://mattmahoney.net/dc/text8.zip")
frequentwords (text, 100)
vocab = getvocab (text)
frequentwords (vocab, 100)

## End(Not run)
## Not run: 
text = loadtext ("http://mattmahoney.net/dc/text8.zip")
frequentwords (text, 100)
vocab = getvocab (text)
frequentwords (vocab, 100)

## End(Not run)

Remove redundancy in a set of rules

Description

This function remove every redundant rules, keeping only the most general ones.

Usage

general.rules(r)
general.rules(r)

Arguments

`r`	A set of rules.

Value

A set of rules, without redundancy.

Examples

require ("arules")
data ("Adult")
r = apriori (Adult)
inspect (general.rules (r))
require ("arules")
data ("Adult")
r = apriori (Adult)
inspect (general.rules (r))

Extract words and phrases from a corpus

Description

Extract words and phrases from a corpus of documents.

Usage

getvocab(
  corpus,
  mincount = 5,
  minphrasecount = NULL,
  ngram = 1,
  lang = "en",
  stopwords = lang,
  ...
)
getvocab(
  corpus,
  mincount = 5,
  minphrasecount = NULL,
  ngram = 1,
  lang = "en",
  stopwords = lang,
  ...
)

Arguments

`corpus`	The corpus of documents (a vector of characters).
`mincount`	Minimum word count to be considered as frequent.
`minphrasecount`	Minimum collocation of words count to be considered as frequent.
`ngram`	maximum size of n-grams.
`lang`	The language of the documents (NULL if no stemming).
`stopwords`	Stopwords, or the language of the documents. NULL if stop words should not be removed.
`...`	Other parameters.

Value

The vocabulary used in the corpus of documents.

Examples

## Not run: 
text = loadtext ("http://mattmahoney.net/dc/text8.zip")
vocab1 = getvocab (text) # With stemming
nrow (vocab1)
vocab2 = getvocab (text, lang = NULL) # Without stemming
nrow (vocab2)

## End(Not run)
## Not run: 
text = loadtext ("http://mattmahoney.net/dc/text8.zip")
vocab1 = getvocab (text) # With stemming
nrow (vocab1)
vocab2 = getvocab (text, lang = NULL) # Without stemming
nrow (vocab2)

## End(Not run)

Classification using Gradient Boosting

Description

This function builds a classification model using Gradient Boosting

Usage

GRADIENTBOOSTING(
  train,
  labels,
  ntree = 500,
  learningrate = 0.3,
  tune = FALSE,
  ...
)
GRADIENTBOOSTING(
  train,
  labels,
  ntree = 500,
  learningrate = 0.3,
  tune = FALSE,
  ...
)

Arguments

`train`	The training set (description), as a `data.frame`.
`labels`	Class labels of the training set (`vector` or `factor`).
`ntree`	The number of trees in the forest.
`learningrate`	The learning rate (between 0 and 1).
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other parameters.

Value

The classification model.

Examples

## Not run: 
require (datasets)
data (iris)
GRADIENTBOOSTING (iris [, -5], iris [, 5])

## End(Not run)
## Not run: 
require (datasets)
data (iris)
GRADIENTBOOSTING (iris [, -5], iris [, 5])

## End(Not run)

Hierarchical Cluster Analysis method

Description

Run the HCA method for clustering.

Usage

HCA(d, method = c("ward", "single"), k = NULL, ...)
HCA(d, method = c("ward", "single"), k = NULL, ...)

Arguments

`d`	The dataset (`matrix` or `data.frame`).
`method`	Character string defining the clustering method.
`k`	The number of cluster.
`...`	Other parameters.

Value

The cluster hierarchy (hca object).

Examples

require (datasets)
data (iris)
HCA (iris [, -5], method = "ward", k = 3)
require (datasets)
data (iris)
HCA (iris [, -5], method = "ward", k = 3)

Clustering evaluation through internal criteria

Description

Evaluation a clustering algorithm according to internal criteria.

Usage

intern(clus, d, eval = "intraclass", type = c("global", "cluster"))
intern(clus, d, eval = "intraclass", type = c("global", "cluster"))

Arguments

`clus`	The extracted clusters.
`d`	The dataset.
`eval`	The evaluation criteria.
`type`	Indicates whether a "global" or a "cluster"-wise evaluation should be used.

Value

The evaluation of the clustering.

Examples

require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
intern (km$clus, iris [, -5])
intern (km$clus, iris [, -5], type = "cluster")
intern (km$clus, iris [, -5], eval = c ("intraclass", "interclass"))
intern (km$clus, iris [, -5], eval = c ("intraclass", "interclass"), type = "cluster")
require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
intern (km$clus, iris [, -5])
intern (km$clus, iris [, -5], type = "cluster")
intern (km$clus, iris [, -5], eval = c ("intraclass", "interclass"))
intern (km$clus, iris [, -5], eval = c ("intraclass", "interclass"), type = "cluster")

Clustering evaluation through Dunn's index

Description

Evaluation a clustering algorithm according to Dunn's index.

Usage

intern.dunn(clus, d, type = c("global"))
intern.dunn(clus, d, type = c("global"))

Arguments

`clus`	The extracted clusters.
`d`	The dataset.
`type`	Indicates whether a "global" or a "cluster"-wise evaluation should be used.

Value

The evaluation of the clustering.

Examples

require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
intern.dunn (km$clus, iris [, -5])
require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
intern.dunn (km$clus, iris [, -5])

Clustering evaluation through interclass inertia

Description

Evaluation a clustering algorithm according to interclass inertia.

Usage

intern.interclass(clus, d, type = c("global", "cluster"))
intern.interclass(clus, d, type = c("global", "cluster"))

Arguments

`clus`	The extracted clusters.
`d`	The dataset.
`type`	Indicates whether a "global" or a "cluster"-wise evaluation should be used.

Value

The evaluation of the clustering.

Examples

require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
intern.interclass (km$clus, iris [, -5])
require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
intern.interclass (km$clus, iris [, -5])

Clustering evaluation through intraclass inertia

Description

Evaluation a clustering algorithm according to intraclass inertia.

Usage

intern.intraclass(clus, d, type = c("global", "cluster"))
intern.intraclass(clus, d, type = c("global", "cluster"))

Arguments

`clus`	The extracted clusters.
`d`	The dataset.
`type`	Indicates whether a "global" or a "cluster"-wise evaluation should be used.

Value

The evaluation of the clustering.

Examples

require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
intern.intraclass (km$clus, iris [, -5])
require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
intern.intraclass (km$clus, iris [, -5])

Ionosphere dataset

Description

This is a dataset from the UCI repository. This radar data was collected by a system in Goose Bay, Labrador. This system consists of a phased array of 16 high-frequency antennas with a total transmitted power on the order of 6.4 kilowatts. See the paper for more details. The targets were free electrons in the ionosphere. "Good" radar returns are those showing evidence of some type of structure in the ionosphere. "Bad" returns are those that do not; their signals pass through the ionosphere. Received signals were processed using an autocorrelation function whose arguments are the time of a pulse and the pulse number. There were 17 pulse numbers for the Goose Bay system. Instances in this databse are described by 2 attributes per pulse number, corresponding to the complex values returned by the function resulting from the complex electromagnetic signal. One attribute with constant value has been removed.

Usage

ionosphere
ionosphere

Format

The dataset has 351 instances described by 34. The last variable is the class.

Source

https://archive.ics.uci.edu/ml/datasets/ionosphere

Kaiser rule

Description

Apply the Kaiser rule to determine the appropriate number of PCA axes.

Usage

kaiser(pca)
kaiser(pca)

Arguments

pca

The PCA result (object of class factorial-class).

Examples

require (datasets)
data (iris)
pca = PCA (iris, quali.sup = 5)
kaiser (pca)
require (datasets)
data (iris)
pca = PCA (iris, quali.sup = 5)
kaiser (pca)

Kernel Regression

Description

This function builds a kernel regression model.

Usage

KERREG(x, y, bandwidth = 1, tune = FALSE, ...)
KERREG(x, y, bandwidth = 1, tune = FALSE, ...)

Arguments

`x`	Predictor `matrix`.
`y`	Response `vector`.
`bandwidth`	The bandwidth parameter.
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other parameters.

Value

The classification model, as an object of class model-class.

Examples

require (datasets)
data (trees)
KERREG (trees [, -3], trees [, 3])
require (datasets)
data (trees)
KERREG (trees [, -3], trees [, 3])

K-means method

Description

Run K-means for clustering.

Usage

KMEANS(
  d,
  k = 9,
  criterion = c("none", "pseudo-F"),
  graph = FALSE,
  nstart = 10,
  ...
)
KMEANS(
  d,
  k = 9,
  criterion = c("none", "pseudo-F"),
  graph = FALSE,
  nstart = 10,
  ...
)

Arguments

`d`	The dataset (`matrix` or `data.frame`).
`k`	The number of cluster.
`criterion`	The criterion for cluster number selection. If `none`, `k` is used, if not the number of cluster is selected between 2 and `k`.
`graph`	A logical indicating whether or not a graphic should be plotted (cluster number selection).
`nstart`	Define how many random sets should be chosen.
`...`	Other parameters.

Value

The clustering (kmeans object).

Examples

require (datasets)
data (iris)
KMEANS (iris [, -5], k = 3)
KMEANS (iris [, -5], criterion = "pseudo-F") # With automatic detection of the nmber of clusters
require (datasets)
data (iris)
KMEANS (iris [, -5], k = 3)
KMEANS (iris [, -5], criterion = "pseudo-F") # With automatic detection of the nmber of clusters

Estimation of the number of clusters for K-means

Description

Estimate the optimal number of cluster of the K-means clustering method.

Usage

kmeans.getk(
  d,
  max = 9,
  criterion = "pseudo-F",
  graph = TRUE,
  nstart = 10,
  seed = NULL
)
kmeans.getk(
  d,
  max = 9,
  criterion = "pseudo-F",
  graph = TRUE,
  nstart = 10,
  seed = NULL
)

Arguments

`d`	The dataset (`matrix` or `data.frame`).
`max`	The maximum number of clusters. Values from 2 to `max` are evaluated.
`criterion`	The criterion to be optimized. `"pseudo-F"` is the only criterion implemented in the current version.
`graph`	A logical indicating whether or not a graphic should be plotted.
`nstart`	The number of random sets chosen for `kmeans` initialization.
`seed`	A specified seed for random number generation.

Value

The optimal number of cluster of the K-means clustering method according to the chosen criterion.

Examples

require (datasets)
data (iris)
kmeans.getk (iris [, -5])
require (datasets)
data (iris)
kmeans.getk (iris [, -5])

Classification using k-NN

Description

This function builds a classification model using Logistic Regression.

Usage

KNN(train, labels, k = 1:10, tune = FALSE, ...)
KNN(train, labels, k = 1:10, tune = FALSE, ...)

Arguments

`train`	The training set (description), as a `data.frame`.
`labels`	Class labels of the training set (`vector` or `factor`).
`k`	The k parameter.
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other parameters.

Value

The classification model.

Examples

require (datasets)
data (iris)
KNN (iris [, -5], iris [, 5])
require (datasets)
data (iris)
KNN (iris [, -5], iris [, 5])

K Nearest Neighbours model

Description

This class contains the classification model obtained by the k-NN method.

Slots

train: The training set (description). A data.frame.
labels: Class labels of the training set. Either a factor or an integer vector.
k: The k parameter.

Classification using Linear Discriminant Analysis

Description

This function builds a classification model using Linear Discriminant Analysis.

Usage

LDA(train, labels, tune = FALSE, ...)
LDA(train, labels, tune = FALSE, ...)

Arguments

`train`	The training set (description), as a `data.frame`.
`labels`	Class labels of the training set (`vector` or `factor`).
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other parameters.

Value

The classification model.

Examples

require (datasets)
data (iris)
LDA (iris [, -5], iris [, 5])
require (datasets)
data (iris)
LDA (iris [, -5], iris [, 5])

Plot the leverage points of a linear regression model

Description

Plot the leverage points of a linear regression model.

Usage

leverageplot(model, index = NULL, labels = NULL)
leverageplot(model, index = NULL, labels = NULL)

Arguments

`model`	The model to be plotted.
`index`	The index of the variable used for for the x-axis.
`labels`	The labels of the instances.

Examples

require (datasets)
data (trees)
model = LINREG (trees [, -3], trees [, 3])
leverageplot (model)
require (datasets)
data (trees)
model = LINREG (trees [, -3], trees [, 3])
leverageplot (model)

Linear Regression

Description

This function builds a linear regression model. Standard least square method, variable selection, factorial methods are available.

Usage

LINREG(
  x,
  y,
  quali = c("none", "intercept", "slope", "both"),
  reg = c("linear", "subset", "ridge", "lasso", "elastic", "pcr", "plsr"),
  regeval = c("r2", "bic", "adjr2", "cp", "msep"),
  scale = TRUE,
  lambda = 10^seq(-5, 5, length.out = 101),
  alpha = 0.5,
  graph = TRUE,
  tune = FALSE,
  ...
)
LINREG(
  x,
  y,
  quali = c("none", "intercept", "slope", "both"),
  reg = c("linear", "subset", "ridge", "lasso", "elastic", "pcr", "plsr"),
  regeval = c("r2", "bic", "adjr2", "cp", "msep"),
  scale = TRUE,
  lambda = 10^seq(-5, 5, length.out = 101),
  alpha = 0.5,
  graph = TRUE,
  tune = FALSE,
  ...
)

Arguments

`x`	Predictor `matrix`.
`y`	Response `vector`.
`quali`	Indicates how to use the qualitative variables.
`reg`	The algorithm.
`regeval`	The evaluation criterion for subset selection.
`scale`	If true, PCR and PLS use scaled dataset.
`lambda`	The lambda parameter of Ridge, Lasso and Elastic net regression.
`alpha`	The elasticnet mixing parameter.
`graph`	A logical indicating whether or not graphics should be plotted (ridge, LASSO and elastic net).
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other parameters.

Value

The classification model, as an object of class model-class.

Examples

## Not run: 
require (datasets)
# With one independant variable
data (cars)
LINREG (cars [, -2], cars [, 2])
# With two independant variables
data (trees)
LINREG (trees [, -3], trees [, 3])
# With non numeric variables
data (ToothGrowth)
LINREG (ToothGrowth [, -1], ToothGrowth [, 1], quali = "intercept") # Different intersept
LINREG (ToothGrowth [, -1], ToothGrowth [, 1], quali = "slope") # Different slope
LINREG (ToothGrowth [, -1], ToothGrowth [, 1], quali = "both") # Complete model
# With multiple numeric variables
data (mtcars)
LINREG (mtcars [, -1], mtcars [, 1])
LINREG (mtcars [, -1], mtcars [, 1], reg = "subset", regeval = "adjr2")
LINREG (mtcars [, -1], mtcars [, 1], reg = "ridge")
LINREG (mtcars [, -1], mtcars [, 1], reg = "lasso")
LINREG (mtcars [, -1], mtcars [, 1], reg = "elastic")
LINREG (mtcars [, -1], mtcars [, 1], reg = "pcr")
LINREG (mtcars [, -1], mtcars [, 1], reg = "plsr")

## End(Not run)
## Not run: 
require (datasets)
# With one independant variable
data (cars)
LINREG (cars [, -2], cars [, 2])
# With two independant variables
data (trees)
LINREG (trees [, -3], trees [, 3])
# With non numeric variables
data (ToothGrowth)
LINREG (ToothGrowth [, -1], ToothGrowth [, 1], quali = "intercept") # Different intersept
LINREG (ToothGrowth [, -1], ToothGrowth [, 1], quali = "slope") # Different slope
LINREG (ToothGrowth [, -1], ToothGrowth [, 1], quali = "both") # Complete model
# With multiple numeric variables
data (mtcars)
LINREG (mtcars [, -1], mtcars [, 1])
LINREG (mtcars [, -1], mtcars [, 1], reg = "subset", regeval = "adjr2")
LINREG (mtcars [, -1], mtcars [, 1], reg = "ridge")
LINREG (mtcars [, -1], mtcars [, 1], reg = "lasso")
LINREG (mtcars [, -1], mtcars [, 1], reg = "elastic")
LINREG (mtcars [, -1], mtcars [, 1], reg = "pcr")
LINREG (mtcars [, -1], mtcars [, 1], reg = "plsr")

## End(Not run)

Linsep dataset

Description

Synthetic dataset.

Usage

linsep
linsep

Format

Class A contains 50 observations and class B contains 500 observations. There are two numeric variables: X and Y.

Author(s)

Alexandre Blansché [email protected]

load a text file

Description

(Down)Load a text file (and extract it if it is in a zip file).

Usage

loadtext(
  file = file.choose(),
  dir = "~/",
  collapse = TRUE,
  sep = NULL,
  categories = NULL
)
loadtext(
  file = file.choose(),
  dir = "~/",
  collapse = TRUE,
  sep = NULL,
  categories = NULL
)

Arguments

`file`	The path or URL of the text file.
`dir`	The (temporary) directory, where the file is downloaded. The file is deleted at the end of this function.
`collapse`	Indicates whether or not lines of each documents should collapse together or not.
`sep`	Separator between text fields.
`categories`	Columns that should be considered as categorial data.

Value

The text contained in the dowloaded file.

Examples

## Not run: 
text = loadtext ("http://mattmahoney.net/dc/text8.zip")

## End(Not run)
## Not run: 
text = loadtext ("http://mattmahoney.net/dc/text8.zip")

## End(Not run)

Classification using Logistic Regression

Description

This function builds a classification model using Logistic Regression.

Usage

LR(train, labels, tune = FALSE, ...)
LR(train, labels, tune = FALSE, ...)

Arguments

`train`	The training set (description), as a `data.frame`.
`labels`	Class labels of the training set (`vector` or `factor`).
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other parameters.

Value

The classification model.

Examples

require (datasets)
data (iris)
LR (iris [, -5], iris [, 5])
require (datasets)
data (iris)
LR (iris [, -5], iris [, 5])

Multiple Correspondence Analysis (MCA)

Description

Performs Multiple Correspondence Analysis (MCA) with supplementary individuals, supplementary quantitative variables and supplementary categorical variables. Performs also Specific Multiple Correspondence Analysis with supplementary categories and supplementary categorical variables. Missing values are treated as an additional level, categories which are rare can be ventilated.

Usage

MCA(
  d,
  ncp = 5,
  ind.sup = NULL,
  quanti.sup = NULL,
  quali.sup = NULL,
  row.w = NULL
)
MCA(
  d,
  ncp = 5,
  ind.sup = NULL,
  quanti.sup = NULL,
  quali.sup = NULL,
  row.w = NULL
)

Arguments

`d`	A ddata frame or a table with n rows and p columns, i.e. a contingency table.
`ncp`	The number of dimensions kept in the results (by default 5).
`ind.sup`	A vector indicating the indexes of the supplementary individuals.
`quanti.sup`	A vector indicating the indexes of the quantitative supplementary variables.
`quali.sup`	A vector indicating the indexes of the categorical supplementary variables.
`row.w`	An optional row weights (by default, a vector of 1 for uniform row weights); the weights are given only for the active individuals.

Value

The MCA on the dataset.

Examples

data (tea, package = "FactoMineR")
MCA (tea, quanti.sup = 19, quali.sup = 20:36)
data (tea, package = "FactoMineR")
MCA (tea, quanti.sup = 19, quali.sup = 20:36)

MeanShift method

Description

Run MeanShift for clustering.

Usage

MEANSHIFT(
  d,
  mskernel = "NORMAL",
  bandwidth = rep(1, ncol(d)),
  alpha = 0,
  iterations = 10,
  epsilon = 1e-08,
  epsilonCluster = 1e-04,
  ...
)
MEANSHIFT(
  d,
  mskernel = "NORMAL",
  bandwidth = rep(1, ncol(d)),
  alpha = 0,
  iterations = 10,
  epsilon = 1e-08,
  epsilonCluster = 1e-04,
  ...
)

Arguments

`d`	The dataset (`matrix` or `data.frame`).
`mskernel`	A string indicating the kernel associated with the kernel density estimate that the mean shift is optimizing over.
`bandwidth`	Used in the kernel density estimate for steepest ascent classification.
`alpha`	A scalar tuning parameter for normal kernels.
`iterations`	The number of iterations to perform mean shift.
`epsilon`	A scalar used to determine when to terminate the iteration of a individual query point.
`epsilonCluster`	A scalar used to determine the minimum distance between distinct clusters.
`...`	Other parameters.

Value

The clustering (meanshift object).

Examples

## Not run: 
require (datasets)
data (iris)
MEANSHIFT (iris [, -5], bandwidth = .75)

## End(Not run)
## Not run: 
require (datasets)
data (iris)
MEANSHIFT (iris [, -5], bandwidth = .75)

## End(Not run)

MeanShift model

Description

This class contains the model obtained by the MEANSHIFT method.

Slots

cluster: A vector of integers indicating the cluster to which each point is allocated.
value: A vector or matrix containing the location of the classified local maxima in the support.
data: The leaning set.
kernel: A string indicating the kernel associated with the kernel density estimate that the mean shift is optimizing over.
bandwidth: Used in the kernel density estimate for steepest ascent classification.
alpha: A scalar tuning parameter for normal kernels.
iterations: The number of iterations to perform mean shift.
epsilon: A scalar used to determine when to terminate the iteration of a individual query point.
epsilonCluster: A scalar used to determine the minimum distance between distinct clusters.

Classification using Multilayer Perceptron

Description

This function builds a classification model using Multilayer Perceptron.

Usage

MLP(
  train,
  labels,
  hidden = ifelse(is.vector(train), 2:(1 + nlevels(labels)), 2:(ncol(train) +
    nlevels(labels))),
  decay = 10^(-3:-1),
  methodparameters = NULL,
  tune = FALSE,
  ...
)
MLP(
  train,
  labels,
  hidden = ifelse(is.vector(train), 2:(1 + nlevels(labels)), 2:(ncol(train) +
    nlevels(labels))),
  decay = 10^(-3:-1),
  methodparameters = NULL,
  tune = FALSE,
  ...
)

Arguments

`train`	The training set (description), as a `data.frame`.
`labels`	Class labels of the training set (`vector` or `factor`).
`hidden`	The size of the hidden layer (if a vector, cross-over validation is used to chose the best size).
`decay`	The decay (between 0 and 1) of the backpropagation algorithm (if a vector, cross-over validation is used to chose the best size).
`methodparameters`	Object containing the parameters. If given, it replaces `size` and `decay`.
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other parameters.

Value

The classification model.

Examples

## Not run: 
require (datasets)
data (iris)
MLP (iris [, -5], iris [, 5], hidden = 4, decay = .1)

## End(Not run)
## Not run: 
require (datasets)
data (iris)
MLP (iris [, -5], iris [, 5], hidden = 4, decay = .1)

## End(Not run)

Multi-Layer Perceptron Regression

Description

This function builds a regression model using MLP.

Usage

MLPREG(
  x,
  y,
  size = 2:(ifelse(is.vector(x), 2, ncol(x))),
  decay = 10^(-3:-1),
  params = NULL,
  tune = FALSE,
  ...
)
MLPREG(
  x,
  y,
  size = 2:(ifelse(is.vector(x), 2, ncol(x))),
  decay = 10^(-3:-1),
  params = NULL,
  tune = FALSE,
  ...
)

Arguments

`x`	Predictor `matrix`.
`y`	Response `vector`.
`size`	The size of the hidden layer (if a vector, cross-over validation is used to chose the best size).
`decay`	The decay (between 0 and 1) of the backpropagation algorithm (if a vector, cross-over validation is used to chose the best size).
`params`	Object containing the parameters. If given, it replaces `size` and `decay`.
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other parameters.

Value

The classification model, as an object of class model-class.

Examples

## Not run: 
require (datasets)
data (trees)
MLPREG (trees [, -3], trees [, 3])

## End(Not run)
## Not run: 
require (datasets)
data (trees)
MLPREG (trees [, -3], trees [, 3])

## End(Not run)

Generic classification or regression model

Description

This is a wrapper class containing the classification model obtained by any classification or regression method.

Slots

model: The wrapped model.
method: The name of the method.

Movies dataset

Description

Extract from the movie lens dataset. Missing values have been imputed.

Usage

movies
movies

Format

A set of 49 movies, rated by 55 users.

Source

https://grouplens.org/datasets/movielens/

Classification using Naive Bayes

Description

This function builds a classification model using Naive Bayes.

Usage

NB(train, labels, tune = FALSE, ...)
NB(train, labels, tune = FALSE, ...)

Arguments

`train`	The training set (description), as a `data.frame`.
`labels`	Class labels of the training set (`vector` or `factor`).
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other parameters.

Value

The classification model.

Examples

require (datasets)
data (iris)
NB (iris [, -5], iris [, 5])
require (datasets)
data (iris)
NB (iris [, -5], iris [, 5])

Non-negative Matrix Factorization

Description

Return the NMF decomposition.

Usage

NMF(x, rank = 2, nstart = 10, ...)
NMF(x, rank = 2, nstart = 10, ...)

Arguments

`x`	A numeric dataset (data.frame or matrix).
`rank`	Specification of the factorization rank.
`nstart`	How many random sets should be chosen?
`...`	Other parameters.

Examples

## Not run: 
install.packages ("BiocManager")
BiocManager::install ("Biobase")
install.packages ("NMF")
require (datasets)
data (iris)
NMF (iris [, -5])

## End(Not run)
## Not run: 
install.packages ("BiocManager")
BiocManager::install ("Biobase")
install.packages ("NMF")
require (datasets)
data (iris)
NMF (iris [, -5])

## End(Not run)

Ozone dataset

Description

This dataset constains measurements on ozone level.

Usage

ozone
ozone

Format

Each instance is described by the maximum level of ozone measured during the day. Temperature, clouds, and wind are also recorded.

Source

https://r-stat-sc-donnees.github.io/ozone.txt

Learning Parameters

Description

This class contains main parameters for various learning methods.

Slots

decay: The decay parameter.
hidden: The number of hidden nodes.
epsilon: The epsilon parameter.
gamma: The gamma parameter.
cost: The cost parameter.

Principal Component Analysis (PCA)

Description

Performs Principal Component Analysis (PCA) with supplementary individuals, supplementary quantitative variables and supplementary categorical variables. Missing values are replaced by the column mean.

Usage

PCA(
  d,
  scale.unit = TRUE,
  ncp = ncol(d) - length(quanti.sup) - length(quali.sup),
  ind.sup = NULL,
  quanti.sup = NULL,
  quali.sup = NULL,
  row.w = NULL,
  col.w = NULL
)
PCA(
  d,
  scale.unit = TRUE,
  ncp = ncol(d) - length(quanti.sup) - length(quali.sup),
  ind.sup = NULL,
  quanti.sup = NULL,
  quali.sup = NULL,
  row.w = NULL,
  col.w = NULL
)

Arguments

`d`	A data frame with n rows (individuals) and p columns (numeric variables).
`scale.unit`	A boolean, if TRUE (value set by default) then data are scaled to unit variance.
`ncp`	The number of dimensions kept in the results (by default 5).
`ind.sup`	A vector indicating the indexes of the supplementary individuals.
`quanti.sup`	A vector indicating the indexes of the quantitative supplementary variables.
`quali.sup`	A vector indicating the indexes of the categorical supplementary variables.
`row.w`	An optional row weights (by default, a vector of 1 for uniform row weights); the weights are given only for the active individuals.
`col.w`	An optional column weights (by default, uniform column weights); the weights are given only for the active variables.

Value

The PCA on the dataset.

Examples

require (datasets)
data (iris)
PCA (iris, quali.sup = 5)
require (datasets)
data (iris)
PCA (iris, quali.sup = 5)

Performance estimation

Description

Estimate the performance of classification or regression methods using bootstrap or crossvalidation (accuracy, ROC curves, confusion matrices, ...)

Usage

performance(
  methods,
  train.x,
  train.y,
  test.x = NULL,
  test.y = NULL,
  train.size = round(0.7 * nrow(train.x)),
  type = c("evaluation", "confusion", "roc", "cost", "scatter", "avsp"),
  protocol = c("bootstrap", "crossvalidation", "loocv", "holdout", "train"),
  eval = ifelse(is.factor(train.y), "accuracy", "r2"),
  nruns = 10,
  nfolds = 10,
  new = TRUE,
  lty = 1,
  seed = NULL,
  methodparameters = NULL,
  names = NULL,
  ...
)
performance(
  methods,
  train.x,
  train.y,
  test.x = NULL,
  test.y = NULL,
  train.size = round(0.7 * nrow(train.x)),
  type = c("evaluation", "confusion", "roc", "cost", "scatter", "avsp"),
  protocol = c("bootstrap", "crossvalidation", "loocv", "holdout", "train"),
  eval = ifelse(is.factor(train.y), "accuracy", "r2"),
  nruns = 10,
  nfolds = 10,
  new = TRUE,
  lty = 1,
  seed = NULL,
  methodparameters = NULL,
  names = NULL,
  ...
)

Arguments

`methods`	The classification or regression methods to be evaluated.
`train.x`	The dataset (description/predictors), a `matrix` or `data.frame`.
`train.y`	The target (class labels or numeric values), a `factor` or `vector`.
`test.x`	The test dataset (description/predictors), a `matrix` or `data.frame`.
`test.y`	The (test) target (class labels or numeric values), a `factor` or `vector`.
`train.size`	The size of the training set (holdout estimation).
`type`	The type of evaluation (confusion matrix, ROC curve, ...)
`protocol`	The evaluation protocol (crossvalidation, bootstrap, ...)
`eval`	The evaluation functions.
`nruns`	The number of bootstrap runs.
`nfolds`	The number of folds (crossvalidation estimation).
`new`	A logical value indicating whether a new plot should be be created or not (cost curves or ROC curves).
`lty`	The line type (and color) specified as an integer (cost curves or ROC curves).
`seed`	A specified seed for random number generation (useful for testing different method with the same bootstap samplings).
`methodparameters`	Method parameters (if null tuning is done by cross-validation).
`names`	Method names.
`...`	Other specific parameters for the leaning method.

Value

The evaluation of the predictions (numeric value).

Examples

## Not run: 
require ("datasets")
data (iris)
# One method, one evaluation criterion, bootstrap estimation
performance (NB, iris [, -5], iris [, 5], seed = 0)
# One method, two evaluation criteria, train set estimation
performance (NB, iris [, -5], iris [, 5], eval = c ("accuracy", "kappa"),
             protocol = "train", seed = 0)
# Three methods, ROC curves, LOOCV estimation
performance (c (NB, LDA, LR), linsep [, -3], linsep [, 3], type = "roc",
             protocol = "loocv", seed = 0)
# List of methods in a variable, confusion matrix, hodout estimation
classif = c (NB, LDA, LR)
performance (classif, iris [, -5], iris [, 5], type = "confusion",
             protocol = "holdout", seed = 0, names = c ("NB", "LDA", "LR"))
# List of strings (method names), scatterplot evaluation, crossvalidation estimation
classif = c ("NB", "LDA", "LR")
performance (classif, iris [, -5], iris [, 5], type = "scatter",
             protocol = "crossvalidation", seed = 0)
# Actual vs. predicted
data (trees)
performance (LINREG, trees [, -3], trees [, 3], type = "avsp")

## End(Not run)
## Not run: 
require ("datasets")
data (iris)
# One method, one evaluation criterion, bootstrap estimation
performance (NB, iris [, -5], iris [, 5], seed = 0)
# One method, two evaluation criteria, train set estimation
performance (NB, iris [, -5], iris [, 5], eval = c ("accuracy", "kappa"),
             protocol = "train", seed = 0)
# Three methods, ROC curves, LOOCV estimation
performance (c (NB, LDA, LR), linsep [, -3], linsep [, 3], type = "roc",
             protocol = "loocv", seed = 0)
# List of methods in a variable, confusion matrix, hodout estimation
classif = c (NB, LDA, LR)
performance (classif, iris [, -5], iris [, 5], type = "confusion",
             protocol = "holdout", seed = 0, names = c ("NB", "LDA", "LR"))
# List of strings (method names), scatterplot evaluation, crossvalidation estimation
classif = c ("NB", "LDA", "LR")
performance (classif, iris [, -5], iris [, 5], type = "scatter",
             protocol = "crossvalidation", seed = 0)
# Actual vs. predicted
data (trees)
performance (LINREG, trees [, -3], trees [, 3], type = "avsp")

## End(Not run)

Plot function for cda-class

Description

Plot the learning set (and test set) on the canonical axes obtained by Canonical Discriminant Analysis (function CDA).

Usage

## S3 method for class 'cda'
plot(x, newdata = NULL, axes = 1:2, ...)
## S3 method for class 'cda'
plot(x, newdata = NULL, axes = 1:2, ...)

Arguments

`x`	The classification model (object of class `cda-class`).
`newdata`	The test set (`matrix` or `data.frame`).
`axes`	The canonical axes to be printed (numeric `vector`).
`...`	Other parameters.

Examples

require (datasets)
data (iris)
model = CDA (iris [, -5], iris [, 5])
plot (model)
require (datasets)
data (iris)
model = CDA (iris [, -5], iris [, 5])
plot (model)

Plot function for factorial-class

Description

Plot PCA, CA or MCA.

Usage

## S3 method for class 'factorial'
plot(x, type = c("ind", "cor", "eig"), axes = c(1, 2), ...)
## S3 method for class 'factorial'
plot(x, type = c("ind", "cor", "eig"), axes = c(1, 2), ...)

Arguments

`x`	The PCA, CA or MCA result (object of class `factorial-class`).
`type`	The graph to plot.
`axes`	The factorial axes to be printed (numeric `vector`).
`...`	Other parameters.

Examples

require (datasets)
data (iris)
pca = PCA (iris, quali.sup = 5)
plot (pca)
plot (pca, type = "cor")
plot (pca, type = "eig")
require (datasets)
data (iris)
pca = PCA (iris, quali.sup = 5)
plot (pca)
plot (pca, type = "cor")
plot (pca, type = "eig")

Plot function for som-class

Description

Plot Kohonen's self-organizing maps.

Usage

## S3 method for class 'som'
plot(x, type = c("scatter", "mapping"), col = NULL, labels = FALSE, ...)
## S3 method for class 'som'
plot(x, type = c("scatter", "mapping"), col = NULL, labels = FALSE, ...)

Arguments

`x`	The Kohonen's map (object of class `som-class`).
`type`	The type of plot.
`col`	Color of the data points
`labels`	A `vector` of character strings to be printed instead of points in the plot.
`...`	Other parameters.

Examples

require (datasets)
data (iris)
som = SOM (iris [, -5], xdim = 5, ydim = 5, post = "ward", k = 3)
plot (som) # Scatter plot (default)
plot (som, type = "mapping") # Kohonen map
require (datasets)
data (iris)
som = SOM (iris [, -5], xdim = 5, ydim = 5, post = "ward", k = 3)
plot (som) # Scatter plot (default)
plot (som, type = "mapping") # Kohonen map

Plot actual vs. predictions

Description

Plot actual vs. predictions of a regression model.

Usage

plotavsp(predictions, gt)
plotavsp(predictions, gt)

Arguments

`predictions`	The predictions of a classification model (`vector`).
`gt`	The ground truth of the dataset (`vector`).

Examples

require (datasets)
data (trees)
model = LINREG (trees [, -3], trees [, 3])
pred = predict (model, trees [, -3])
plotavsp (pred, trees [, 3])
require (datasets)
data (trees)
model = LINREG (trees [, -3], trees [, 3])
pred = predict (model, trees [, -3])
plotavsp (pred, trees [, 3])

Plot word cloud

Description

Plot a word cloud based on the word frequencies in the documents.

Usage

plotcloud(corpus, k = NULL, stopwords = "en", ...)
plotcloud(corpus, k = NULL, stopwords = "en", ...)

Arguments

`corpus`	The corpus of documents (a vector of characters) or the vocabulary of the documents (result of function `getvocab`).
`k`	A categorial variable (vector or factor).
`stopwords`	Stopwords, or the language of the documents. NULL if stop words should not be removed.
`...`	Other parameters.

Examples

## Not run: 
text = loadtext ("http://mattmahoney.net/dc/text8.zip")
plotcloud (text)
vocab = getvocab (text, mincount = 1, lang = NULL, stopwords = "en")
plotcloud (vocab)

## End(Not run)
## Not run: 
text = loadtext ("http://mattmahoney.net/dc/text8.zip")
plotcloud (text)
vocab = getvocab (text, mincount = 1, lang = NULL, stopwords = "en")
plotcloud (vocab)

## End(Not run)

Generic Plot Method for Clustering

Description

Plot a clustering according to various parameters

Usage

plotclus(
  clustering,
  d = NULL,
  type = c("scatter", "boxplot", "tree", "height", "mapping", "words"),
  centers = FALSE,
  k = NULL,
  tailsize = 9,
  ...
)
plotclus(
  clustering,
  d = NULL,
  type = c("scatter", "boxplot", "tree", "height", "mapping", "words"),
  centers = FALSE,
  k = NULL,
  tailsize = 9,
  ...
)

Arguments

`clustering`	The clustering to be plotted.
`d`	The dataset (`matrix` or `data.frame`), mandatory for some of the graphics.
`type`	The type of plot.
`centers`	Indicates whether or not cluster centers should be plotted (used only in scatter plots).
`k`	Number of clusters (used only for hierarchical methods). If not specified an "optimal" value is determined.
`tailsize`	Number of clusters showned (used only for height plots).
`...`	Other parameters.

Examples

## Not run: 
require (datasets)
data (iris)
ward = HCA (iris [, -5], method = "ward", k = 3)
plotclus (ward, iris [, -5], type = "scatter") # Scatter plot
plotclus (ward, iris [, -5], type = "boxplot") # Boxplot
plotclus (ward, iris [, -5], type = "tree") # Dendrogram
plotclus (ward, iris [, -5], type = "height") # Distances between merging clusters
som = SOM (iris [, -5], xdim = 5, ydim = 5, post = "ward", k = 3)
plotclus (som, iris [, -5], type = "scatter") # Scatter plot for SOM
plotclus (som, iris [, -5], type = "mapping") # Kohonen map

## End(Not run)
## Not run: 
require (datasets)
data (iris)
ward = HCA (iris [, -5], method = "ward", k = 3)
plotclus (ward, iris [, -5], type = "scatter") # Scatter plot
plotclus (ward, iris [, -5], type = "boxplot") # Boxplot
plotclus (ward, iris [, -5], type = "tree") # Dendrogram
plotclus (ward, iris [, -5], type = "height") # Distances between merging clusters
som = SOM (iris [, -5], xdim = 5, ydim = 5, post = "ward", k = 3)
plotclus (som, iris [, -5], type = "scatter") # Scatter plot for SOM
plotclus (som, iris [, -5], type = "mapping") # Kohonen map

## End(Not run)

Advanced plot function

Description

Plot a dataset.

Usage

plotdata(
  d,
  k = NULL,
  type = c("pairs", "scatter", "parallel", "boxplot", "histogram", "barplot", "pie",
    "heatmap", "heatmapc", "pca", "cda", "svd", "nmf", "tsne", "som", "words"),
  legendpos = "topleft",
  alpha = 200,
  asp = 1,
  labels = FALSE,
  ...
)
plotdata(
  d,
  k = NULL,
  type = c("pairs", "scatter", "parallel", "boxplot", "histogram", "barplot", "pie",
    "heatmap", "heatmapc", "pca", "cda", "svd", "nmf", "tsne", "som", "words"),
  legendpos = "topleft",
  alpha = 200,
  asp = 1,
  labels = FALSE,
  ...
)

Arguments

`d`	A numeric dataset (data.frame or matrix).
`k`	A categorial variable (vector or factor).
`type`	The type of graphic to be plotted.
`legendpos`	Position of the legend
`alpha`	Color opacity (0-255).
`asp`	Aspect ratio (default: 1).
`labels`	Indicates whether or not labels (row names) should be showned on the (scatter) plot.
`...`	Other parameters.

Examples

require (datasets)
data (iris)
# Without classification
plotdata (iris [, -5]) # Défault (pairs)
# With classification
plotdata (iris [, -5], iris [, 5]) # Défault (pairs)
plotdata (iris, 5) # Column number
plotdata (iris) # Automatic detection of the classification (if only one factor column)
plotdata (iris, type = "scatter") # Scatter plot (PCA axis)
plotdata (iris, type = "parallel") # Parallel coordinates
plotdata (iris, type = "boxplot") # Boxplot
plotdata (iris, type = "histogram") # Histograms
plotdata (iris, type = "heatmap") # Heatmap
plotdata (iris, type = "heatmapc") # Heatmap (and hierarchalcal clustering)
plotdata (iris, type = "pca") # Scatter plot (PCA axis)
plotdata (iris, type = "cda") # Scatter plot (CDA axis)
plotdata (iris, type = "svd") # Scatter plot (SVD axis)
plotdata (iris, type = "som") # Kohonen map
# With only one variable
plotdata (iris [, 1], iris [, 5]) # Défault (data vs. index)
plotdata (iris [, 1], iris [, 5], type = "scatter") # Scatter plot (data vs. index)
plotdata (iris [, 1], iris [, 5], type = "boxplot") # Boxplot
# With two variables
plotdata (iris [, 3:4], iris [, 5]) # Défault (scatter plot)
plotdata (iris [, 3:4], iris [, 5], type = "scatter") # Scatter plot
data (titanic)
plotdata (titanic, type = "barplot") # Barplots
plotdata (titanic, type = "pie") # Pie charts
require (datasets)
data (iris)
# Without classification
plotdata (iris [, -5]) # Défault (pairs)
# With classification
plotdata (iris [, -5], iris [, 5]) # Défault (pairs)
plotdata (iris, 5) # Column number
plotdata (iris) # Automatic detection of the classification (if only one factor column)
plotdata (iris, type = "scatter") # Scatter plot (PCA axis)
plotdata (iris, type = "parallel") # Parallel coordinates
plotdata (iris, type = "boxplot") # Boxplot
plotdata (iris, type = "histogram") # Histograms
plotdata (iris, type = "heatmap") # Heatmap
plotdata (iris, type = "heatmapc") # Heatmap (and hierarchalcal clustering)
plotdata (iris, type = "pca") # Scatter plot (PCA axis)
plotdata (iris, type = "cda") # Scatter plot (CDA axis)
plotdata (iris, type = "svd") # Scatter plot (SVD axis)
plotdata (iris, type = "som") # Kohonen map
# With only one variable
plotdata (iris [, 1], iris [, 5]) # Défault (data vs. index)
plotdata (iris [, 1], iris [, 5], type = "scatter") # Scatter plot (data vs. index)
plotdata (iris [, 1], iris [, 5], type = "boxplot") # Boxplot
# With two variables
plotdata (iris [, 3:4], iris [, 5]) # Défault (scatter plot)
plotdata (iris [, 3:4], iris [, 5], type = "scatter") # Scatter plot
data (titanic)
plotdata (titanic, type = "barplot") # Barplots
plotdata (titanic, type = "pie") # Pie charts

Plot rank versus frequency

Description

Plot the frequency of words in a document agains the ranks of those words. It also plot the Zipf law.

Usage

plotzipf(corpus)
plotzipf(corpus)

Arguments

corpus

The corpus of documents (a vector of characters) or the vocabulary of the documents (result of function getvocab).

Examples

## Not run: 
text = loadtext ("http://mattmahoney.net/dc/text8.zip")
plotzipf (text)
vocab = getvocab (text, mincount = 1, lang = NULL)
plotzipf (vocab)

## End(Not run)
## Not run: 
text = loadtext ("http://mattmahoney.net/dc/text8.zip")
plotzipf (text)
vocab = getvocab (text, mincount = 1, lang = NULL)
plotzipf (vocab)

## End(Not run)

Polynomial Regression

Description

This function builds a polynomial regression model.

Usage

POLYREG(x, y, degree = 2, tune = FALSE, ...)
POLYREG(x, y, degree = 2, tune = FALSE, ...)

Arguments

`x`	Predictor `matrix`.
`y`	Response `vector`.
`degree`	The polynom degree.
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other parameters.

Value

The classification model, as an object of class model-class.

Examples

## Not run: 
require (datasets)
data (trees)
POLYREG (trees [, -3], trees [, 3])

## End(Not run)
## Not run: 
require (datasets)
data (trees)
POLYREG (trees [, -3], trees [, 3])

## End(Not run)

Model predictions

Description

This function predicts values based upon a model trained by apriori.classif. Observations that do not match any of the rules are labelled as "unmatched".

Usage

## S3 method for class 'apriori'
predict(object, test, unmatched = "Unknown", ...)
## S3 method for class 'apriori'
predict(object, test, unmatched = "Unknown", ...)

Arguments

`object`	The classification model (of class `apriori`, created by `apriori.classif`).
`test`	The test set (a `data.frame`)
`unmatched`	The class label given to the unmatched observations (a character string).
`...`	Other parameters.

Value

A vector of predicted values (factor).

Examples

require ("datasets")
data (iris)
d = discretizeDF (iris,
    default = list (method = "interval", breaks = 3, labels = c ("small", "medium", "large")))
model = APRIORI (d [, -5], d [, 5], supp = .1, conf = .9, prune = TRUE)
predict (model, d [, -5])
require ("datasets")
data (iris)
d = discretizeDF (iris,
    default = list (method = "interval", breaks = 3, labels = c ("small", "medium", "large")))
model = APRIORI (d [, -5], d [, 5], supp = .1, conf = .9, prune = TRUE)
predict (model, d [, -5])

Model predictions

Description

This function predicts values based upon a model trained by a boosting method.

Usage

## S3 method for class 'boosting'
predict(object, test, fuzzy = FALSE, ...)
## S3 method for class 'boosting'
predict(object, test, fuzzy = FALSE, ...)

Arguments

`object`	The classification model (of class `boosting-class`, created by `ADABOOST` or `BAGGING`).
`test`	The test set (a `data.frame`)
`fuzzy`	A boolean indicating whether fuzzy classification is used or not.
`...`	Other parameters.

Value

A vector of predicted values (factor).

Examples

## Not run: 
require (datasets)
data (iris)
d = splitdata (iris, 5)
model = BAGGING (d$train.x, d$train.y, NB)
predict (model, d$test.x)
model = ADABOOST (d$train.x, d$train.y, NB)
predict (model, d$test.x)

## End(Not run)
## Not run: 
require (datasets)
data (iris)
d = splitdata (iris, 5)
model = BAGGING (d$train.x, d$train.y, NB)
predict (model, d$test.x)
model = ADABOOST (d$train.x, d$train.y, NB)
predict (model, d$test.x)

## End(Not run)

Model predictions

Description

This function predicts values based upon a model trained by CDA.

Usage

## S3 method for class 'cda'
predict(object, test, fuzzy = FALSE, ...)
## S3 method for class 'cda'
predict(object, test, fuzzy = FALSE, ...)

Arguments

`object`	The classification model (of class `cda-class`, created by `CDA`).
`test`	The test set (a `data.frame`)
`fuzzy`	A boolean indicating whether fuzzy classification is used or not.
`...`	Other parameters.

Value

A vector of predicted values (factor).

Examples

require (datasets)
data (iris)
d = splitdata (iris, 5)
model = CDA (d$train.x, d$train.y)
predict (model, d$test.x)
require (datasets)
data (iris)
d = splitdata (iris, 5)
model = CDA (d$train.x, d$train.y)
predict (model, d$test.x)

Predict function for DBSCAN

Description

Return the closest DBSCAN cluster for a new dataset.

Usage

## S3 method for class 'dbs'
predict(object, newdata, ...)
## S3 method for class 'dbs'
predict(object, newdata, ...)

Arguments

`object`	The classification model (of class `dbs-class`, created by `DBSCAN`).
`newdata`	A new dataset (a `data.frame`), with same variables as the learning dataset.
`...`	Other parameters.

Examples

require (datasets)
data (iris)
d = splitdata (iris, 5)
model = DBSCAN (d$train.x, minpts = 5, eps = 0.65)
predict (model, d$test.x)
require (datasets)
data (iris)
d = splitdata (iris, 5)
model = DBSCAN (d$train.x, minpts = 5, eps = 0.65)
predict (model, d$test.x)

Predict function for EM

Description

Return the closest EM cluster for a new dataset.

Usage

## S3 method for class 'em'
predict(object, newdata, ...)
## S3 method for class 'em'
predict(object, newdata, ...)

Arguments

`object`	The classification model (of class `em-class`, created by `EM`).
`newdata`	A new dataset (a `data.frame`), with same variables as the learning dataset.
`...`	Other parameters.

Examples

require (datasets)
data (iris)
d = splitdata (iris, 5)
model = EM (d$train.x, 3)
predict (model, d$test.x)
require (datasets)
data (iris)
d = splitdata (iris, 5)
model = EM (d$train.x, 3)
predict (model, d$test.x)

Predict function for K-means

Description

Return the closest K-means cluster for a new dataset.

Usage

## S3 method for class 'kmeans'
predict(object, newdata, ...)
## S3 method for class 'kmeans'
predict(object, newdata, ...)

Arguments

`object`	The classification model (created by `KMEANS`).
`newdata`	A new dataset (a `data.frame`), with same variables as the learning dataset.
`...`	Other parameters.

Examples

require (datasets)
data (iris)
d = splitdata (iris, 5)
model = KMEANS (d$train.x, k = 3)
predict (model, d$test.x)
require (datasets)
data (iris)
d = splitdata (iris, 5)
model = KMEANS (d$train.x, k = 3)
predict (model, d$test.x)

Model predictions

Description

This function predicts values based upon a model trained by KNN.

Usage

## S3 method for class 'knn'
predict(object, test, fuzzy = FALSE, ...)
## S3 method for class 'knn'
predict(object, test, fuzzy = FALSE, ...)

Arguments

`object`	The classification model (of class `knn`).
`test`	The test set (a `data.frame`).
`fuzzy`	A boolean indicating whether fuzzy classification is used or not.
`...`	Other parameters.

Value

A vector of predicted values (factor).

Examples

require (datasets)
data (iris)
d = splitdata (iris, 5)
model = KNN (d$train.x, d$train.y)
predict (model, d$test.x)
require (datasets)
data (iris)
d = splitdata (iris, 5)
model = KNN (d$train.x, d$train.y)
predict (model, d$test.x)

Predict function for MeanShift

Description

Return the closest MeanShift cluster for a new dataset.

Usage

## S3 method for class 'meanshift'
predict(object, newdata, ...)
## S3 method for class 'meanshift'
predict(object, newdata, ...)

Arguments

`object`	The classification model (created by `MEANSHIFT`).
`newdata`	A new dataset (a `data.frame`), with same variables as the learning dataset.
`...`	Other parameters.

Examples

## Not run: 
require (datasets)
data (iris)
d = splitdata (iris, 5)
model = MEANSHIFT (d$train.x, bandwidth = .75)
predict (model, d$test.x)

## End(Not run)
## Not run: 
require (datasets)
data (iris)
d = splitdata (iris, 5)
model = MEANSHIFT (d$train.x, bandwidth = .75)
predict (model, d$test.x)

## End(Not run)

Model predictions

Description

This function predicts values based upon a model trained by any classification or regression model.

Usage

## S3 method for class 'model'
predict(object, test, fuzzy = FALSE, ...)
## S3 method for class 'model'
predict(object, test, fuzzy = FALSE, ...)

Arguments

`object`	The classification model (of class `cda-class`, created by `CDA`).
`test`	The test set (a `data.frame`).
`fuzzy`	A boolean indicating whether fuzzy classification is used or not.
`...`	Other parameters.

Value

A vector of predicted values (factor).

Examples

require (datasets)
data (iris)
d = splitdata (iris, 5)
model = LDA (d$train.x, d$train.y)
predict (model, d$test.x)
require (datasets)
data (iris)
d = splitdata (iris, 5)
model = LDA (d$train.x, d$train.y)
predict (model, d$test.x)

Model predictions

Description

This function predicts values based upon a model trained by any classification or regression model.

Usage

## S3 method for class 'selection'
predict(object, test, fuzzy = FALSE, ...)
## S3 method for class 'selection'
predict(object, test, fuzzy = FALSE, ...)

Arguments

`object`	The classification model (of class `cda-class`, created by `CDA`).
`test`	The test set (a `data.frame`).
`fuzzy`	A boolean indicating whether fuzzy classification is used or not.
`...`	Other parameters.

Value

A vector of predicted values (factor).

Examples

## Not run: 
require (datasets)
data (iris)
d = splitdata (iris, 5)
model = FEATURESELECTION (d$train.x, d$train.y, uninb = 2, mainmethod = LDA)
predict (model, d$test.x)

## End(Not run)
## Not run: 
require (datasets)
data (iris)
d = splitdata (iris, 5)
model = FEATURESELECTION (d$train.x, d$train.y, uninb = 2, mainmethod = LDA)
predict (model, d$test.x)

## End(Not run)

Model predictions

Description

This function predicts values based upon a model trained for text mining.

Usage

## S3 method for class 'textmining'
predict(object, test, fuzzy = FALSE, ...)
## S3 method for class 'textmining'
predict(object, test, fuzzy = FALSE, ...)

Arguments

`object`	The classification model (of class `textmining-class`, created by `TEXTMINING`.
`test`	The test set (a `data.frame`)
`fuzzy`	A boolean indicating whether fuzzy classification is used or not.
`...`	Other parameters.

Value

A vector of predicted values (factor).

Examples

## Not run: 
require (text2vec)
data ("movie_review")
d = movie_review [, 2:3]
d [, 1] = factor (d [, 1])
d = splitdata (d, 1)
model = TEXTMINING (d$train.x, NB, labels = d$train.y, mincount = 50)
pred = predict (model, d$test.x)
evaluation (pred, d$test.y)

## End(Not run)
## Not run: 
require (text2vec)
data ("movie_review")
d = movie_review [, 2:3]
d [, 1] = factor (d [, 1])
d = splitdata (d, 1)
model = TEXTMINING (d$train.x, NB, labels = d$train.y, mincount = 50)
pred = predict (model, d$test.x)
evaluation (pred, d$test.y)

## End(Not run)

Print a classification model obtained by APRIORI

Description

Print the set of rules in the classification model.

Usage

## S3 method for class 'apriori'
print(x, ...)
## S3 method for class 'apriori'
print(x, ...)

Arguments

`x`	The model to be printed.
`...`	Other parameters.

Examples

require ("datasets")
data (iris)
d = discretizeDF (iris,
    default = list (method = "interval", breaks = 3, labels = c ("small", "medium", "large")))
model = APRIORI (d [, -5], d [, 5], supp = .1, conf = .9, prune = TRUE)
print (model)
require ("datasets")
data (iris)
d = discretizeDF (iris,
    default = list (method = "interval", breaks = 3, labels = c ("small", "medium", "large")))
model = APRIORI (d [, -5], d [, 5], supp = .1, conf = .9, prune = TRUE)
print (model)

Plot function for factorial-class

Description

Print PCA, CA or MCA.

Usage

## S3 method for class 'factorial'
print(x, ...)
## S3 method for class 'factorial'
print(x, ...)

Arguments

`x`	The PCA, CA or MCA result (object of class `factorial-class`).
`...`	Other parameters.

Examples

require (datasets)
data (iris)
pca = PCA (iris, quali.sup = 5)
print (pca)
require (datasets)
data (iris)
pca = PCA (iris, quali.sup = 5)
print (pca)

Pseudo-F

Description

Compute the pseudo-F of a clustering result obtained by the K-means method.

Usage

pseudoF(clustering)
pseudoF(clustering)

Arguments

clustering

The clustering result (obtained by the function kmeans).

Value

The pseudo-F of the clustering result.

Examples

require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
pseudoF (km)
require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
pseudoF (km)

Classification using Quadratic Discriminant Analysis

Description

This function builds a classification model using Quadratic Discriminant Analysis.

Usage

QDA(train, labels, tune = FALSE, ...)
QDA(train, labels, tune = FALSE, ...)

Arguments

`train`	The training set (description), as a `data.frame`.
`labels`	Class labels of the training set (`vector` or `factor`).
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other parameters.

Value

The classification model.

Examples

require (datasets)
data (iris)
QDA (iris [, -5], iris [, 5])
require (datasets)
data (iris)
QDA (iris [, -5], iris [, 5])

Document query

Description

Search for documents similar to the query.

Usage

query.docs(docvectors, query, vectorizer, nres = 5)
query.docs(docvectors, query, vectorizer, nres = 5)

Arguments

`docvectors`	The vectorized documents.
`query`	The query (vectorized or raw text).
`vectorizer`	The vectorizer taht has been used to vectorize the documents.
`nres`	The number of results.

Value

The indices of the documents the most similar to the query.

Examples

## Not run: 
require (text2vec)
data (movie_review)
vectorizer = vectorize.docs (corpus = movie_review$review,
                             minphrasecount = 50, returndata = FALSE)
docs = vectorize.docs (corpus = movie_review$review, vectorizer = vectorizer)
query.docs (docs, movie_review$review [1], vectorizer)
query.docs (docs, docs [1, ], vectorizer)

## End(Not run)
## Not run: 
require (text2vec)
data (movie_review)
vectorizer = vectorize.docs (corpus = movie_review$review,
                             minphrasecount = 50, returndata = FALSE)
docs = vectorize.docs (corpus = movie_review$review, vectorizer = vectorizer)
query.docs (docs, movie_review$review [1], vectorizer)
query.docs (docs, docs [1, ], vectorizer)

## End(Not run)

Word query

Description

Search for words similar to the query.

Usage

query.words(wordvectors, origin, sub = NULL, add = NULL, nres = 5, lang = "en")
query.words(wordvectors, origin, sub = NULL, add = NULL, nres = 5, lang = "en")

Arguments

`wordvectors`	The vectorized words
`origin`	The query (character).
`sub`	Words to be substrated to the origin.
`add`	Words to be Added to the origin.
`nres`	The number of results.
`lang`	The language of the words (NULL if no stemming).

Value

The Words the most similar to the query.

Examples

## Not run: 
text = loadtext ("http://mattmahoney.net/dc/text8.zip")
words = vectorize.words (text, minphrasecount = 50)
query.words (words, origin = "paris", sub = "france", add = "germany")
query.words (words, origin = "berlin", sub = "germany", add = "france")
query.words (words, origin = "new_zealand")

## End(Not run)
## Not run: 
text = loadtext ("http://mattmahoney.net/dc/text8.zip")
words = vectorize.words (text, minphrasecount = 50)
query.words (words, origin = "paris", sub = "france", add = "germany")
query.words (words, origin = "berlin", sub = "germany", add = "france")
query.words (words, origin = "new_zealand")

## End(Not run)

Classification using Random Forest

Description

This function builds a classification model using Random Forest

Usage

RANDOMFOREST(
  train,
  labels,
  ntree = 500,
  nvar = if (!is.null(labels) && !is.factor(labels)) max(floor(ncol(train)/3), 1) else
    floor(sqrt(ncol(train))),
  tune = FALSE,
  ...
)
RANDOMFOREST(
  train,
  labels,
  ntree = 500,
  nvar = if (!is.null(labels) && !is.factor(labels)) max(floor(ncol(train)/3), 1) else
    floor(sqrt(ncol(train))),
  tune = FALSE,
  ...
)

Arguments

`train`	The training set (description), as a `data.frame`.
`labels`	Class labels of the training set (`vector` or `factor`).
`ntree`	The number of trees in the forest.
`nvar`	Number of variables randomly sampled as candidates at each split.
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other parameters.

Value

The classification model.

Examples

## Not run: 
require (datasets)
data (iris)
RANDOMFOREST (iris [, -5], iris [, 5])

## End(Not run)
## Not run: 
require (datasets)
data (iris)
RANDOMFOREST (iris [, -5], iris [, 5])

## End(Not run)

reg1 dataset

Description

Artificial dataset for simple regression tasks.

Usage

reg1
reg1.train
reg1.test
reg1
reg1.train
reg1.test

Format

50 instances and 3 variables. X, a numeric, K, a factor, and Y, a numeric (the target variable).

Author(s)

Alexandre Blansché [email protected]

reg2 dataset

Description

Artificial dataset for simple regression tasks.

Usage

reg2
reg2.train
reg2.test
reg2
reg2.train
reg2.test

Format

50 instances and 2 variables. X and Y (the target variable) are both numeric variables.

Author(s)

Alexandre Blansché [email protected]

Plot function for a regression model

Description

Plot a regresion model on a 2-D plot. The predictor x should be one-dimensional.

Usage

regplot(model, x, y, margin = 0.1, ...)
regplot(model, x, y, margin = 0.1, ...)

Arguments

`model`	The model to be plotted.
`x`	The predictor `vector`.
`y`	The response `vector`.
`margin`	A margin parameter.
`...`	Other graphical parameters

Examples

require (datasets)
data (cars)
model = POLYREG (cars [, -2], cars [, 2])
regplot (model, cars [, -2], cars [, 2])
require (datasets)
data (cars)
model = POLYREG (cars [, -2], cars [, 2])
regplot (model, cars [, -2], cars [, 2])

Plot the studentized residuals of a linear regression model

Description

Plot the studentized residuals of a linear regression model.

Usage

resplot(model, index = NULL, labels = NULL)
resplot(model, index = NULL, labels = NULL)

Arguments

`model`	The model to be plotted.
`index`	The index of the variable used for for the x-axis.
`labels`	The labels of the instances.

Examples

require (datasets)
data (trees)
model = LINREG (trees [, -3], trees [, 3])
resplot (model) # Ordered by index
resplot (model, index = 0) # Ordered by variable "Volume" (dependant variable)
resplot (model, index = 1) # Ordered by variable "Girth" (independant variable)
resplot (model, index = 2) # Ordered by variable "Height" (independant variable)
require (datasets)
data (trees)
model = LINREG (trees [, -3], trees [, 3])
resplot (model) # Ordered by index
resplot (model, index = 0) # Ordered by variable "Volume" (dependant variable)
resplot (model, index = 1) # Ordered by variable "Girth" (independant variable)
resplot (model, index = 2) # Ordered by variable "Height" (independant variable)

Plot ROC Curves

Description

This function plots ROC Curves of several classification predictions.

Usage

roc.curves(predictions, gt, methods.names = NULL)
roc.curves(predictions, gt, methods.names = NULL)

Arguments

`predictions`	The predictions of a classification model (`factor` or `vector`).
`gt`	Actual labels of the dataset (`factor` or `vector`).
`methods.names`	The name of the compared methods (`vector`).

Value

The evaluation of the predictions (numeric value).

Examples

require (datasets)
data (iris)
d = iris
levels (d [, 5]) = c ("+", "+", "-") # Building a two classes dataset
model.nb = NB (d [, -5], d [, 5])
model.lda = LDA (d [, -5], d [, 5])
pred.nb = predict (model.nb, d [, -5])
pred.lda = predict (model.lda, d [, -5])
roc.curves (cbind (pred.nb, pred.lda), d [, 5], c ("NB", "LDA"))
require (datasets)
data (iris)
d = iris
levels (d [, 5]) = c ("+", "+", "-") # Building a two classes dataset
model.nb = NB (d [, -5], d [, 5])
model.lda = LDA (d [, -5], d [, 5])
pred.nb = predict (model.nb, d [, -5])
pred.lda = predict (model.lda, d [, -5])
roc.curves (cbind (pred.nb, pred.lda), d [, 5], c ("NB", "LDA"))

Rotation

Description

Rotation on two variables of a numeric dataset

Usage

rotation(d, angle, axis = 1:2, range = 2 * pi)
rotation(d, angle, axis = 1:2, range = 2 * pi)

Arguments

`d`	The dataset.
`angle`	The angle of the rotation.
`axis`	The axis.
`range`	The range of the angle (360, 2*pi, 100, ...)

Value

A rotated data matrix.

Examples

d = data.parabol ()
d [, -3] = rotation (d [, -3], 45, range = 360)
plotdata (d [, -3], d [, 3])
d = data.parabol ()
d [, -3] = rotation (d [, -3], 45, range = 360)
plotdata (d [, -3], d [, 3])

Running time

Description

Return the running time of a function

Usage

runningtime(FUN, ...)
runningtime(FUN, ...)

Arguments

`FUN`	The function to be evaluated.
`...`	The parameters to be passes to function `FUN`.

Value

The running time of function FUN.

Examples

sqrt (x = 1:100)
runningtime (sqrt, x = 1:100)
sqrt (x = 1:100)
runningtime (sqrt, x = 1:100)

Clustering Scatter Plots

Description

Produce a scatter plot for clustering results. If the dataset has more than two dimensions, the scatter plot will show the two first PCA axes.

Usage

scatterplot(
  d,
  clusters,
  centers = NULL,
  labels = FALSE,
  ellipses = FALSE,
  legend = c("auto1", "auto2"),
  ...
)
scatterplot(
  d,
  clusters,
  centers = NULL,
  labels = FALSE,
  ellipses = FALSE,
  legend = c("auto1", "auto2"),
  ...
)

Arguments

`d`	The dataset (`matrix` or `data.frame`).
`clusters`	Cluster labels of the training set (`vector` or `factor`).
`centers`	Coordinates of the cluster centers.
`labels`	Indicates whether or not labels (row names) should be showned on the plot.
`ellipses`	Indicates whether or not ellipses should be drawned around clusters.
`legend`	Indicates where the legend is placed on the graphics.
`...`	Other parameters.

Examples

require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
scatterplot (iris [, -5], km$cluster)
require (datasets)
data (iris)
km = KMEANS (iris [, -5], k = 3)
scatterplot (iris [, -5], km$cluster)

Feature selection for classification

Description

Select a subset of features for a classification task.

Usage

selectfeatures(
  train,
  labels,
  algorithm = c("ranking", "forward", "backward", "exhaustive"),
  unieval = if (algorithm[1] == "ranking") c("fisher", "fstat", "relief", "inertiaratio")
    else NULL,
  uninb = NULL,
  unithreshold = NULL,
  multieval = if (algorithm[1] == "ranking") NULL else c("mrmr", "cfs", "fstat",
    "inertiaratio", "wrapper"),
  wrapmethod = NULL,
  keep = FALSE,
  ...
)
selectfeatures(
  train,
  labels,
  algorithm = c("ranking", "forward", "backward", "exhaustive"),
  unieval = if (algorithm[1] == "ranking") c("fisher", "fstat", "relief", "inertiaratio")
    else NULL,
  uninb = NULL,
  unithreshold = NULL,
  multieval = if (algorithm[1] == "ranking") NULL else c("mrmr", "cfs", "fstat",
    "inertiaratio", "wrapper"),
  wrapmethod = NULL,
  keep = FALSE,
  ...
)

Arguments

`train`	The training set (description), as a `data.frame`.
`labels`	Class labels of the training set (`vector` or `factor`).
`algorithm`	The feature selection algorithm.
`unieval`	The (univariate) evaluation criterion. `uninb`, `unithreshold` or `multieval` must be specified.
`uninb`	The number of selected feature (univariate evaluation).
`unithreshold`	The threshold for selecting feature (univariate evaluation).
`multieval`	The (multivariate) evaluation criterion.
`wrapmethod`	The classification method used for the wrapper evaluation.
`keep`	If true, the dataset is kept in the returned result.
`...`	Other parameters.

Examples

## Not run: 
require (datasets)
data (iris)
selectfeatures (iris [, -5], iris [, 5], algorithm = "forward", multieval = "fstat")
selectfeatures (iris [, -5], iris [, 5], algorithm = "ranking", uninb = 2)
selectfeatures (iris [, -5], iris [, 5], algorithm = "ranking",
                multieval = "wrapper", wrapmethod = LDA)

## End(Not run)
## Not run: 
require (datasets)
data (iris)
selectfeatures (iris [, -5], iris [, 5], algorithm = "forward", multieval = "fstat")
selectfeatures (iris [, -5], iris [, 5], algorithm = "ranking", uninb = 2)
selectfeatures (iris [, -5], iris [, 5], algorithm = "ranking",
                multieval = "wrapper", wrapmethod = LDA)

## End(Not run)

Feature selection

Description

This class contains the result of feature selection algorithms.

Slots

selection: A vector of integers indicating the selected features.
unieval: The evaluation of the features (univariate).
multieval: The evaluation of the selected features (multivariate).
algorithm: The algorithm used to select features.
univariate: The evaluation criterion (univariate).
nbfeatures: The number of features to be kept.
threshold: The threshold to decide whether a feature is kept or not..
multivariate: The evaluation criterion (multivariate).
dataset: The dataset described by the selected features only.
model: The classification model.

Snore dataset

Description

This dataset has been used in a study on snoring in Angers hospital.

Usage

snore
snore

Format

The dataset has 100 instances described by 7 variables. The variables are as follows:

Age: In years.
Weights: In kg.
Height: In cm.
Alcool: Number of glass of alcool per day.
Sex: M for male or F for female.
Snore: Snoring diagnosis (Y or N).
Tobacco: Y or N.

Source

http://forge.info.univ-angers.fr/~gh/Datasets/datasets.htm

Self-Organizing Maps clustering method

Description

Run the SOM algorithm for clustering.

Usage

SOM(
  d,
  xdim = floor(sqrt(nrow(d))),
  ydim = floor(sqrt(nrow(d))),
  rlen = 10000,
  post = c("none", "single", "ward"),
  k = NULL,
  ...
)
SOM(
  d,
  xdim = floor(sqrt(nrow(d))),
  ydim = floor(sqrt(nrow(d))),
  rlen = 10000,
  post = c("none", "single", "ward"),
  k = NULL,
  ...
)

Arguments

`d`	The dataset (`matrix` or `data.frame`).
`xdim`, `ydim`	The dimensions of the grid.
`rlen`	The number of iterations.
`post`	The post-treatement method: `"none"` (None), `"single"` (Single link) or `"ward"` (Ward clustering).
`k`	The number of cluster (only used if `post` is different from `"none"`).
`...`	Other parameters.

Value

The fitted Kohonen's map as an object of class som.

Examples

require (datasets)
data (iris)
SOM (iris [, -5], xdim = 5, ydim = 5, post = "ward", k = 3)
require (datasets)
data (iris)
SOM (iris [, -5], xdim = 5, ydim = 5, post = "ward", k = 3)

Self-Organizing Maps model

Description

This class contains the model obtained by the SOM method.

Slots

som: An object of class kohonen representing the fitted map.
nodes: A vector of integer indicating the cluster to which each node is allocated.
cluster: A vector of integer indicating the cluster to which each observation is allocated.
data: The dataset that has been used to fit the map (as a matrix).

Spectral clustering method

Description

Run a Spectral clustering algorithm.

Usage

SPECTRAL(d, k, sigma = 1, graph = TRUE, ...)
SPECTRAL(d, k, sigma = 1, graph = TRUE, ...)

Arguments

`d`	The dataset (`matrix` or `data.frame`).
`k`	The number of cluster.
`sigma`	Width of the gaussian used to build the affinity matrix.
`graph`	A logical indicating whether or not a graphic should be plotted (projection on the spectral space of the affinity matrix).
`...`	Other parameters.

Examples

## Not run: 
require (datasets)
data (iris)
SPECTRAL (iris [, -5], k = 3)

## End(Not run)
## Not run: 
require (datasets)
data (iris)
SPECTRAL (iris [, -5], k = 3)

## End(Not run)

Spectral clustering model

Description

This class contains the model obtained by Spectral clustering.

Slots

cluster: A vector of integer indicating the cluster to which each observation is allocated.
proj: The projection of the dataset in the spectral space.
centers: The cluster centers (on the spectral space).

Spine dataset

Description

The data have been organized in two different but related classification tasks. The first task consists in classifying patients as belonging to one out of three categories: Normal, Disk Hernia or Spondylolisthesis. For the second task, the categories Disk Hernia and Spondylolisthesis were merged into a single category labelled as 'abnormal'. Thus, the second task consists in classifying patients as belonging to one out of two categories: Normal or Abnormal.

Usage

spine
spine.train
spine.test
spine
spine.train
spine.test

Format

The dataset has 310 instances described by 8 variables. Variables V1 to V6 are biomechanical attributes derived from the shape and orientation of the pelvis and lumbar spine. The variable Classif2 is the classification into two classes AB and NO. The variable Classif3 is the classification into 3 classes DH, SL and NO. spine.train contains 217 instances and spine.test contains 93.

Source

http://archive.ics.uci.edu/ml/datasets/vertebral+column

Splits a dataset into training set and test set

Description

This function splits a dataset into training set and test set. Return an object of class dataset-class.

Usage

splitdata(dataset, target, size = round(0.7 * nrow(dataset)), seed = NULL)
splitdata(dataset, target, size = round(0.7 * nrow(dataset)), seed = NULL)

Arguments

`dataset`	The dataset to be split (`data.frame` or `matrix`).
`target`	The column index of the target variable (class label or response variable).
`size`	The size of the training set (as an integer value).
`seed`	A specified seed for random number generation.

Value

An object of class dataset-class.

Examples

require (datasets)
data (iris)
d = splitdata (iris, 5)
str (d)
require (datasets)
data (iris)
d = splitdata (iris, 5)
str (d)

Clustering evaluation through stability

Description

Evaluation a clustering algorithm according to stability, through a bootstrap procedure.

Usage

stability(
  clusteringmethods,
  d,
  originals = NULL,
  eval = "jaccard",
  type = c("cluster", "global"),
  nsampling = 10,
  seed = NULL,
  names = NULL,
  graph = FALSE,
  ...
)
stability(
  clusteringmethods,
  d,
  originals = NULL,
  eval = "jaccard",
  type = c("cluster", "global"),
  nsampling = 10,
  seed = NULL,
  names = NULL,
  graph = FALSE,
  ...
)

Arguments

`clusteringmethods`	The clustering methods to be evaluated.
`d`	The dataset.
`originals`	The original clustering.
`eval`	The evaluation criteria.
`type`	The comparison method.
`nsampling`	The number of bootstrap runs.
`seed`	A specified seed for random number generation (useful for testing different method with the same bootstap samplings).
`names`	Method names.
`graph`	Indicates wether or not a graphic is potted for each sample.
`...`	Parameters to be passed to the clustering algorithms.

Value

The evaluation of the clustering algorithm(s) (numeric values).

Examples

## Not run: 
require (datasets)
data (iris)
stability (KMEANS, iris [, -5], seed = 0, k = 3)
stability (KMEANS, iris [, -5], seed = 0, k = 3, eval = c ("jaccard", "accuracy"), type = "global")
stability (KMEANS, iris [, -5], seed = 0, k = 3, type = "cluster")
stability (KMEANS, iris [, -5], seed = 0, k = 3, eval = c ("jaccard", "accuracy"), type = "cluster")
stability (c (KMEANS, HCA), iris [, -5], seed = 0, k = 3)
stability (c (KMEANS, HCA), iris [, -5], seed = 0, k = 3,
eval = c ("jaccard", "accuracy"), type = "global")
stability (c (KMEANS, HCA), iris [, -5], seed = 0, k = 3, type = "cluster")
stability (c (KMEANS, HCA), iris [, -5], seed = 0, k = 3,
eval = c ("jaccard", "accuracy"), type = "cluster")
stability (KMEANS, iris [, -5], originals = KMEANS (iris [, -5], k = 3)$cluster, seed = 0, k = 3)
stability (KMEANS, iris [, -5], originals = KMEANS (iris [, -5], k = 3), seed = 0, k = 3)

## End(Not run)
## Not run: 
require (datasets)
data (iris)
stability (KMEANS, iris [, -5], seed = 0, k = 3)
stability (KMEANS, iris [, -5], seed = 0, k = 3, eval = c ("jaccard", "accuracy"), type = "global")
stability (KMEANS, iris [, -5], seed = 0, k = 3, type = "cluster")
stability (KMEANS, iris [, -5], seed = 0, k = 3, eval = c ("jaccard", "accuracy"), type = "cluster")
stability (c (KMEANS, HCA), iris [, -5], seed = 0, k = 3)
stability (c (KMEANS, HCA), iris [, -5], seed = 0, k = 3,
eval = c ("jaccard", "accuracy"), type = "global")
stability (c (KMEANS, HCA), iris [, -5], seed = 0, k = 3, type = "cluster")
stability (c (KMEANS, HCA), iris [, -5], seed = 0, k = 3,
eval = c ("jaccard", "accuracy"), type = "cluster")
stability (KMEANS, iris [, -5], originals = KMEANS (iris [, -5], k = 3)$cluster, seed = 0, k = 3)
stability (KMEANS, iris [, -5], originals = KMEANS (iris [, -5], k = 3), seed = 0, k = 3)

## End(Not run)

Classification using one-level decision tree

Description

This function builds a classification model using CART with maxdepth = 1.

Usage

STUMP(train, labels, randomvar = TRUE, tune = FALSE, ...)
STUMP(train, labels, randomvar = TRUE, tune = FALSE, ...)

Arguments

`train`	The training set (description), as a `data.frame`.
`labels`	Class labels of the training set (`vector` or `factor`).
`randomvar`	If true, the model uses a random variable.
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other parameters.

Value

The classification model.

Examples

require (datasets)
data (iris)
STUMP (iris [, -5], iris [, 5])
require (datasets)
data (iris)
STUMP (iris [, -5], iris [, 5])

Print summary of a classification model obtained by APRIORI

Description

Print summary of the set of rules in the classification model obtained by APRIORI.

Usage

## S3 method for class 'apriori'
summary(object, ...)
## S3 method for class 'apriori'
summary(object, ...)

Arguments

`object`	The model to be printed.
`...`	Other parameters.

Examples

require ("datasets")
data (iris)
d = discretizeDF (iris,
    default = list (method = "interval", breaks = 3, labels = c ("small", "medium", "large")))
model = APRIORI (d [, -5], d [, 5], supp = .1, conf = .9, prune = TRUE)
summary (model)
require ("datasets")
data (iris)
d = discretizeDF (iris,
    default = list (method = "interval", breaks = 3, labels = c ("small", "medium", "large")))
model = APRIORI (d [, -5], d [, 5], supp = .1, conf = .9, prune = TRUE)
summary (model)

Singular Value Decomposition

Description

Return the SVD decomposition.

Usage

SVD(x, ndim = min(nrow(x), ncol(x)), ...)
SVD(x, ndim = min(nrow(x), ncol(x)), ...)

Arguments

`x`	A numeric dataset (data.frame or matrix).
`ndim`	The number of dimensions.
`...`	Other parameters.

Examples

require (datasets)
data (iris)
SVD (iris [, -5])
require (datasets)
data (iris)
SVD (iris [, -5])

Classification using Support Vector Machine

Description

This function builds a classification model using Support Vector Machine.

Usage

SVM(
  train,
  labels,
  gamma = 2^(-3:3),
  cost = 2^(-3:3),
  kernel = c("radial", "linear"),
  methodparameters = NULL,
  tune = FALSE,
  ...
)
SVM(
  train,
  labels,
  gamma = 2^(-3:3),
  cost = 2^(-3:3),
  kernel = c("radial", "linear"),
  methodparameters = NULL,
  tune = FALSE,
  ...
)

Arguments

`train`	The training set (description), as a `data.frame`.
`labels`	Class labels of the training set (`vector` or `factor`).
`gamma`	The gamma parameter (if a vector, cross-over validation is used to chose the best size).
`cost`	The cost parameter (if a vector, cross-over validation is used to chose the best size).
`kernel`	The kernel type.
`methodparameters`	Object containing the parameters. If given, it replaces `gamma` and `cost`.
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other arguments.

Value

The classification model.

Examples

## Not run: 
require (datasets)
data (iris)
SVM (iris [, -5], iris [, 5], kernel = "linear", cost = 1)
SVM (iris [, -5], iris [, 5], kernel = "radial", gamma = 1, cost = 1)

## End(Not run)
## Not run: 
require (datasets)
data (iris)
SVM (iris [, -5], iris [, 5], kernel = "linear", cost = 1)
SVM (iris [, -5], iris [, 5], kernel = "radial", gamma = 1, cost = 1)

## End(Not run)

Classification using Support Vector Machine with a linear kernel

Description

This function builds a classification model using Support Vector Machine with a linear kernel.

Usage

SVMl(
  train,
  labels,
  cost = 2^(-3:3),
  methodparameters = NULL,
  tune = FALSE,
  ...
)
SVMl(
  train,
  labels,
  cost = 2^(-3:3),
  methodparameters = NULL,
  tune = FALSE,
  ...
)

Arguments

`train`	The training set (description), as a `data.frame`.
`labels`	Class labels of the training set (`vector` or `factor`).
`cost`	The cost parameter (if a vector, cross-over validation is used to chose the best size).
`methodparameters`	Object containing the parameters. If given, it replaces `gamma` and `cost`.
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other arguments.

Value

The classification model.

Examples

## Not run: 
require (datasets)
data (iris)
SVMl (iris [, -5], iris [, 5], cost = 1)

## End(Not run)
## Not run: 
require (datasets)
data (iris)
SVMl (iris [, -5], iris [, 5], cost = 1)

## End(Not run)

Classification using Support Vector Machine with a radial kernel

Description

This function builds a classification model using Support Vector Machine with a radial kernel.

Usage

SVMr(
  train,
  labels,
  gamma = 2^(-3:3),
  cost = 2^(-3:3),
  methodparameters = NULL,
  tune = FALSE,
  ...
)
SVMr(
  train,
  labels,
  gamma = 2^(-3:3),
  cost = 2^(-3:3),
  methodparameters = NULL,
  tune = FALSE,
  ...
)

Arguments

`train`	The training set (description), as a `data.frame`.
`labels`	Class labels of the training set (`vector` or `factor`).
`gamma`	The gamma parameter (if a vector, cross-over validation is used to chose the best size).
`cost`	The cost parameter (if a vector, cross-over validation is used to chose the best size).
`methodparameters`	Object containing the parameters. If given, it replaces `gamma` and `cost`.
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other arguments.

Value

The classification model.

Examples

## Not run: 
require (datasets)
data (iris)
SVMr (iris [, -5], iris [, 5], gamma = 1, cost = 1)

## End(Not run)
## Not run: 
require (datasets)
data (iris)
SVMr (iris [, -5], iris [, 5], gamma = 1, cost = 1)

## End(Not run)

Regression using Support Vector Machine

Description

This function builds a regression model using Support Vector Machine.

Usage

SVR(
  x,
  y,
  gamma = 2^(-3:3),
  cost = 2^(-3:3),
  kernel = c("radial", "linear"),
  epsilon = c(0.1, 0.5, 1),
  params = NULL,
  tune = FALSE,
  ...
)
SVR(
  x,
  y,
  gamma = 2^(-3:3),
  cost = 2^(-3:3),
  kernel = c("radial", "linear"),
  epsilon = c(0.1, 0.5, 1),
  params = NULL,
  tune = FALSE,
  ...
)

Arguments

`x`	Predictor `matrix`.
`y`	Response `vector`.
`gamma`	The gamma parameter (if a vector, cross-over validation is used to chose the best size).
`cost`	The cost parameter (if a vector, cross-over validation is used to chose the best size).
`kernel`	The kernel type.
`epsilon`	The epsilon parameter (if a vector, cross-over validation is used to chose the best size).
`params`	Object containing the parameters. If given, it replaces `epsilon`, `gamma` and `cost`.
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other arguments.

Value

The classification model.

Examples

## Not run: 
require (datasets)
data (trees)
SVR (trees [, -3], trees [, 3], kernel = "linear", cost = 1)
SVR (trees [, -3], trees [, 3], kernel = "radial", gamma = 1, cost = 1)

## End(Not run)
## Not run: 
require (datasets)
data (trees)
SVR (trees [, -3], trees [, 3], kernel = "linear", cost = 1)
SVR (trees [, -3], trees [, 3], kernel = "radial", gamma = 1, cost = 1)

## End(Not run)

Regression using Support Vector Machine with a linear kernel

Description

This function builds a regression model using Support Vector Machine with a linear kernel.

Usage

SVRl(
  x,
  y,
  cost = 2^(-3:3),
  epsilon = c(0.1, 0.5, 1),
  params = NULL,
  tune = FALSE,
  ...
)
SVRl(
  x,
  y,
  cost = 2^(-3:3),
  epsilon = c(0.1, 0.5, 1),
  params = NULL,
  tune = FALSE,
  ...
)

Arguments

`x`	Predictor `matrix`.
`y`	Response `vector`.
`cost`	The cost parameter (if a vector, cross-over validation is used to chose the best size).
`epsilon`	The epsilon parameter (if a vector, cross-over validation is used to chose the best size).
`params`	Object containing the parameters. If given, it replaces `epsilon`, `gamma` and `cost`.
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other arguments.

Value

The classification model.

Examples

## Not run: 
require (datasets)
data (trees)
SVRl (trees [, -3], trees [, 3], cost = 1)

## End(Not run)
## Not run: 
require (datasets)
data (trees)
SVRl (trees [, -3], trees [, 3], cost = 1)

## End(Not run)

Regression using Support Vector Machine with a radial kernel

Description

This function builds a regression model using Support Vector Machine with a radial kernel.

Usage

SVRr(
  x,
  y,
  gamma = 2^(-3:3),
  cost = 2^(-3:3),
  epsilon = c(0.1, 0.5, 1),
  params = NULL,
  tune = FALSE,
  ...
)
SVRr(
  x,
  y,
  gamma = 2^(-3:3),
  cost = 2^(-3:3),
  epsilon = c(0.1, 0.5, 1),
  params = NULL,
  tune = FALSE,
  ...
)

Arguments

`x`	Predictor `matrix`.
`y`	Response `vector`.
`gamma`	The gamma parameter (if a vector, cross-over validation is used to chose the best size).
`cost`	The cost parameter (if a vector, cross-over validation is used to chose the best size).
`epsilon`	The epsilon parameter (if a vector, cross-over validation is used to chose the best size).
`params`	Object containing the parameters. If given, it replaces `epsilon`, `gamma` and `cost`.
`tune`	If true, the function returns paramters instead of a classification model.
`...`	Other arguments.

Value

The classification model.

Examples

## Not run: 
require (datasets)
data (trees)
SVRr (trees [, -3], trees [, 3], gamma = 1, cost = 1)

## End(Not run)
## Not run: 
require (datasets)
data (trees)
SVRr (trees [, -3], trees [, 3], gamma = 1, cost = 1)

## End(Not run)

Temperature dataset

Description

The data contains temperature measurement and geographic coordinates of 35 european cities.

Usage

temperature
temperature

Format

The dataset has 35 instances described by 17 variables. Average temperature of the 12 month. Mean and amplitude of the temperature. Latitude and longitude of the city. Localisation in Europe.

Text mining

Description

Apply data mining function on vectorized text

Usage

TEXTMINING(corpus, miningmethod, vector = c("docs", "words"), ...)
TEXTMINING(corpus, miningmethod, vector = c("docs", "words"), ...)

Arguments

`corpus`	The corpus.
`miningmethod`	The data mining method.
`vector`	Indicates the type of vectorization, documents (TF-IDF) or words (GloVe).
`...`	Parameters passed to the vectorisation and to the data mining method.

Value

The result of the data mining method.

Examples

## Not run: 
require (text2vec)
data ("movie_review")
d = movie_review [, 2:3]
d [, 1] = factor (d [, 1])
d = splitdata (d, 1)
model = TEXTMINING (d$train.x, NB, labels = d$train.y, mincount = 50)
pred = predict (model, d$test.x)
evaluation (pred, d$test.y)
text = loadtext ("http://mattmahoney.net/dc/text8.zip")
clusters = TEXTMINING (text, HCA, vector = "words", k = 9, maxwords = 100)
plotclus (clusters$res, text, type = "tree", labels = TRUE)

## End(Not run)
## Not run: 
require (text2vec)
data ("movie_review")
d = movie_review [, 2:3]
d [, 1] = factor (d [, 1])
d = splitdata (d, 1)
model = TEXTMINING (d$train.x, NB, labels = d$train.y, mincount = 50)
pred = predict (model, d$test.x)
evaluation (pred, d$test.y)
text = loadtext ("http://mattmahoney.net/dc/text8.zip")
clusters = TEXTMINING (text, HCA, vector = "words", k = 9, maxwords = 100)
plotclus (clusters$res, text, type = "tree", labels = TRUE)

## End(Not run)

Text mining object

Description

Object used for text mining.

Slots

vectorizer: The vectorizer.
vectors: The vectorized dataset.
res: The result of the text mining method.

Titanic dataset

Description

This dataset from the British Board of Trade depict the fate of the passengers and crew during the RMS Titanic disaster.

Usage

titanic
titanic

Format

The dataset has 2201 instances described by 4 variables. The variables are as follows:

Category: 1st, 2nd, 3rd Class or Crew.
Age: Adult or Child.
Sex: Female or Male.
Fate: Casualty or Survivor.

Source

British Board of Trade (1990), Report on the Loss of the ‘Titanic’ (S.S.). British Board of Trade Inquiry Report (reprint). Gloucester, UK: Allan Sutton Publishing.

Dendrogram Plots

Description

Draws a dendrogram.

Usage

treeplot(
  clustering,
  labels = FALSE,
  k = NULL,
  split = TRUE,
  horiz = FALSE,
  ...
)
treeplot(
  clustering,
  labels = FALSE,
  k = NULL,
  split = TRUE,
  horiz = FALSE,
  ...
)

Arguments

`clustering`	The dendrogram to be plotted (result of `hclust`, `agnes` or `HCA`).
`labels`	Indicates whether or not labels (row names) should be showned on the plot.
`k`	Number of clusters. If not specified an "optimal" value is determined.
`split`	Indicates wheather or not the clusters should be highlighted in the graphics.
`horiz`	Indicates if the dendrogram should be drawn horizontally or not.
`...`	Other parameters.

Examples

require (datasets)
data (iris)
hca = HCA (iris [, -5], method = "ward", k = 3)
treeplot (hca)
require (datasets)
data (iris)
hca = HCA (iris [, -5], method = "ward", k = 3)
treeplot (hca)

t-distributed Stochastic Neighbor Embedding

Description

Return the t-SNE dimensionality reduction.

Usage

TSNE(x, perplexity = 30, nstart = 10, ...)
TSNE(x, perplexity = 30, nstart = 10, ...)

Arguments

`x`	A numeric dataset (data.frame or matrix).
`perplexity`	Specification of the perplexity.
`nstart`	How many random sets should be chosen?
`...`	Other parameters.

Examples

require (datasets)
data (iris)
TSNE (iris [, -5])
require (datasets)
data (iris)
TSNE (iris [, -5])

University dataset

Description

The dataset presents a french university demographics.

Usage

universite
universite

Format

The dataset has 10 instances (university departments) described by 12 variables. The fist six variables are the number of female and male student studying for bachelor degree (Licence), master degree (Master) and doctorate (Doctorat). The six last variables are obtained by combining the first ones.

Source

https://husson.github.io/data.html

Document vectorization

Description

Vectorize a corpus of documents.

Usage

vectorize.docs(
  vectorizer = NULL,
  corpus = NULL,
  lang = "en",
  stopwords = lang,
  ngram = 1,
  mincount = 10,
  minphrasecount = NULL,
  transform = c("tfidf", "lsa", "l1", "none"),
  latentdim = 50,
  returndata = TRUE,
  ...
)
vectorize.docs(
  vectorizer = NULL,
  corpus = NULL,
  lang = "en",
  stopwords = lang,
  ngram = 1,
  mincount = 10,
  minphrasecount = NULL,
  transform = c("tfidf", "lsa", "l1", "none"),
  latentdim = 50,
  returndata = TRUE,
  ...
)

Arguments

`vectorizer`	The document vectorizer.
`corpus`	The corpus of documents (a vector of characters).
`lang`	The language of the documents (NULL if no stemming).
`stopwords`	Stopwords, or the language of the documents. NULL if stop words should not be removed.
`ngram`	maximum size of n-grams.
`mincount`	Minimum word count to be considered as frequent.
`minphrasecount`	Minimum collocation of words count to be considered as frequent.
`transform`	Transformation (TF-IDF, LSA, L1 normanization, or nothing).
`latentdim`	Number of latent dimensions if LSA transformation is performed.
`returndata`	If true, the vectorized documents are returned. If false, a "vectorizer" is returned.
`...`	Other parameters.

Value

The vectorized documents.

Examples

## Not run: 
require (text2vec)
data ("movie_review")
# Clustering
docs = vectorize.docs (corpus = movie_review$review, transform = "tfidf")
km = KMEANS (docs [sample (nrow (docs), 100), ], k = 10)
# Classification
d = movie_review [, 2:3]
d [, 1] = factor (d [, 1])
d = splitdata (d, 1)
vectorizer = vectorize.docs (corpus = d$train.x,
                             returndata = FALSE, mincount = 50)
train = vectorize.docs (corpus = d$train.x, vectorizer = vectorizer)
test = vectorize.docs (corpus = d$test.x, vectorizer = vectorizer)
model = NB (as.matrix (train), d$train.y)
pred = predict (model, as.matrix (test))
evaluation (pred, d$test.y)

## End(Not run)
## Not run: 
require (text2vec)
data ("movie_review")
# Clustering
docs = vectorize.docs (corpus = movie_review$review, transform = "tfidf")
km = KMEANS (docs [sample (nrow (docs), 100), ], k = 10)
# Classification
d = movie_review [, 2:3]
d [, 1] = factor (d [, 1])
d = splitdata (d, 1)
vectorizer = vectorize.docs (corpus = d$train.x,
                             returndata = FALSE, mincount = 50)
train = vectorize.docs (corpus = d$train.x, vectorizer = vectorizer)
test = vectorize.docs (corpus = d$test.x, vectorizer = vectorizer)
model = NB (as.matrix (train), d$train.y)
pred = predict (model, as.matrix (test))
evaluation (pred, d$test.y)

## End(Not run)

Word vectorization

Description

Vectorize words from a corpus of documents.

Usage

vectorize.words(
  corpus = NULL,
  ndim = 50,
  maxwords = NULL,
  mincount = 5,
  minphrasecount = NULL,
  window = 5,
  maxcooc = 10,
  maxiter = 10,
  epsilon = 0.01,
  lang = "en",
  stopwords = lang,
  ...
)
vectorize.words(
  corpus = NULL,
  ndim = 50,
  maxwords = NULL,
  mincount = 5,
  minphrasecount = NULL,
  window = 5,
  maxcooc = 10,
  maxiter = 10,
  epsilon = 0.01,
  lang = "en",
  stopwords = lang,
  ...
)

Arguments

`corpus`	The corpus of documents (a vector of characters).
`ndim`	The number of dimensions of the vector space.
`maxwords`	The maximum number of words.
`mincount`	Minimum word count to be considered as frequent.
`minphrasecount`	Minimum collocation of words count to be considered as frequent.
`window`	Window for term-co-occurence matrix construction.
`maxcooc`	Maximum number of co-occurrences to use in the weighting function.
`maxiter`	The maximum number of iteration to fit the GloVe model.
`epsilon`	Defines early stopping strategy when fit the GloVe model.
`lang`	The language of the documents (NULL if no stemming).
`stopwords`	Stopwords, or the language of the documents. NULL if stop words should not be removed.
`...`	Other parameters.

Value

The vectorized words.

Examples

## Not run: 
text = loadtext ("http://mattmahoney.net/dc/text8.zip")
words = vectorize.words (text, minphrasecount = 50)
query.words (words, origin = "paris", sub = "france", add = "germany")
query.words (words, origin = "berlin", sub = "germany", add = "france")
query.words (words, origin = "new_zealand")

## End(Not run)
## Not run: 
text = loadtext ("http://mattmahoney.net/dc/text8.zip")
words = vectorize.words (text, minphrasecount = 50)
query.words (words, origin = "paris", sub = "france", add = "germany")
query.words (words, origin = "berlin", sub = "germany", add = "france")
query.words (words, origin = "new_zealand")

## End(Not run)

Document vectorization object

Description

This class contains a vectorization model for textual documents.

Slots

vectorizer: The vectorizer.
transform: The transformation to be applied after vectorization (normalization, TF-IDF).
phrases: The phrase detection method.
tfidf: The TF-IDF transformation.
lsa: The LSA transformation.
tokens: The token from the original document.

Vowels dataset

Description

Excerpt of the Letter Recognition Data Set (UCI repository).

Usage

vowels
vowels.train
vowels.test
vowels
vowels.train
vowels.test

Format

The dataset has 4664 instances described by 17 variables. The first variable is the classification into 6 classes (letter A, E, I, O, U and Y). vowels.train contains 233 instances and vowels.test contains 4431.

Source

https://archive.ics.uci.edu/ml/datasets/letter+recognition

Wheat dataset

Description

The data contains kernels belonging to three different varieties of wheat: Kama, Rosa and Canadian, 70 elements each, randomly selected. High quality visualization of the internal kernel structure was detected using a soft X-ray technique. The images were recorded on 13x18 cm X-ray KODAK plates. Source : Institute of Agrophysics of the Polish Academy of Sciences in Lublin.

Usage

wheat
wheat

Format

The dataset has 210 instances described by 8 variables: area, perimeter, compactness, length, width, asymmetry coefficient, groove length and variery.

Source

https://archive.ics.uci.edu/ml/datasets/seeds

Wine dataset

Description

These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines.

Usage

wine
wine

Format

There are 178 observations and 14 variables. The first variable is the class label (1, 2, 3).

Source

https://archive.ics.uci.edu/ml/datasets/wine

Zoo dataset

Description

Animal description based on various features.

Usage

zoo
zoo

Format

The dataset has 101 instances described by 17 qualitative variables.

Source

https://archive.ics.uci.edu/ml/datasets/zoo

Package 'fdm2id'

Help Index

Sample of car accident location in the UK during year 2014.

Description

Usage

Format

Source

Classification using AdaBoost

Description

Usage

Arguments

Value

See Also

Examples

Alcohol dataset

Description

Usage

Format

Source

Classification using APRIORI

Description

Usage

Arguments

Value

See Also

Examples

APRIORI classification model

Description

Slots

See Also

Duplicate and add noise to a dataset

Description

Usage

Arguments

Value

Examples

Auto MPG dataset

Description

Usage

Format

Source

Classification using Bagging

Description

Usage

Arguments

Value

See Also

Examples

Flea beetles dataset

Description

Usage

Format

Source

Birth dataset

Description

Usage

Format

Boosting methods model

Description

Slots

See Also

Clustering Box Plots

Description

Usage

Arguments

See Also

Examples

Population and location of 18 major british cities.

Description

Usage

Format

Correspondence Analysis (CA)

Description

Usage

Arguments

Value

See Also

Examples

Classification using CART

Description