Classification using AdaBoost

Description

Ensemble learning, through AdaBoost Algorithm.

Usage

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

Arguments

Value

The classification model.

See Also

BAGGING, predict.boosting

Examples

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

## End(Not run)

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,
  ...
)

Arguments

Value

The classification model, as an object of class apriori.

See Also

predict.apriori, apriori-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)

Classification using Bagging

Description

Ensemble learning, through Bagging Algorithm.

Usage

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

Arguments

Value

The classification model.

See Also

ADABOOST, predict.boosting

Examples

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

## End(Not run)

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
)

Arguments

Value

The CA on the dataset.

See Also

CA, MCA, PCA, plot.factorial, factorial-class

Examples

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,
  ...
)

Arguments

Value

The classification model.

See Also

cartdepth, cartinfo, cartleafs, cartnodes, cartplot, rpart

Examples

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

Classification using Canonical Discriminant Analysis

Description

This function builds a classification model using Canonical Discriminant Analysis.

Usage

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

Arguments

Value

The classification model, as an object of class glmnet.

See Also

plot.cda, predict.cda, cda-class

Examples

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

DBSCAN clustering method

Description

Run the DBSCAN algorithm for clustering.

Usage

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

Arguments

Value

A clustering model obtained by DBSCAN.

See Also

dbscan, dbs-class, distplot, predict.dbs

Examples

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

Expectation-Maximization clustering method

Description

Run the EM algorithm for clustering.

Usage

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

Arguments

Value

A clustering model obtained by EM.

See Also

em, mstep, mclustModelNames

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

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,
  ...
)

Arguments

See Also

selectfeatures, predict.selection, selection-class

Examples

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

## 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,
  ...
)

Arguments

Value

The classification model.

See Also

xgboost

Examples

## 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, ...)

Arguments

Value

The cluster hierarchy (hca object).

See Also

agnes

Examples

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

Kernel Regression

Description

This function builds a kernel regression model.

Usage

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

Arguments

Value

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

See Also

npregress

Examples

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,
  ...
)

Arguments

Value

The clustering (kmeans object).

See Also

kmeans, predict.kmeans

Examples

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

Classification using k-NN

Description

This function builds a classification model using Logistic Regression.

Usage

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

Arguments

Value

The classification model.

See Also

knn

Examples

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

Classification using Linear Discriminant Analysis

Description

This function builds a classification model using Linear Discriminant Analysis.

Usage

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

Arguments

Value

The classification model.

See Also

lda

Examples

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

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,
  ...
)

Arguments

Value

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

See Also

lm, regsubsets, mvr, glmnet

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)

Classification using Logistic Regression

Description

This function builds a classification model using Logistic Regression.

Usage

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

Arguments

Value

The classification model.

See Also

multinom

Examples

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
)

Arguments

Value

The MCA on the dataset.

See Also

MCA, CA, PCA, plot.factorial, factorial-class

Examples

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,
  ...
)

Arguments

Value

The clustering (meanshift object).

See Also

meanShift, predict.meanshift

Examples

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

## End(Not run)

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,
  ...
)

Arguments

Value

The classification model.

See Also

nnet

Examples

## 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,
  ...
)

Arguments

Value

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

See Also

nnet

Examples

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

## End(Not run)

Classification using Naive Bayes

Description

This function builds a classification model using Naive Bayes.

Usage

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

Arguments

Value

The classification model.

See Also

naiveBayes

Examples

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, ...)

Arguments

See Also

nmf

Examples

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

## End(Not run)

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
)

Arguments

Value

The PCA on the dataset.

See Also

PCA, CA, MCA, plot.factorial, kaiser, factorial-class

Examples

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

Polynomial Regression

Description

This function builds a polynomial regression model.

Usage

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

Arguments

Value

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

See Also

polyreg

Examples

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

## End(Not run)

Classification using Quadratic Discriminant Analysis

Description

This function builds a classification model using Quadratic Discriminant Analysis.

Usage

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

Arguments

Value

The classification model.

See Also

qda

Examples

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

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,
  ...
)

Arguments

Value

The classification model.

See Also

randomForest

Examples

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

## End(Not run)

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,
  ...
)

Arguments

Value

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

See Also

plot.som, som-class, som

Examples

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

Spectral clustering method

Description

Run a Spectral clustering algorithm.

Usage

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

Arguments

See Also

spectral-class

Examples

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

Arguments

Value

The classification model.

See Also

CART

Examples

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

Singular Value Decomposition

Description

Return the SVD decomposition.

Usage

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

Arguments

See Also

svd

Examples

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,
  ...
)

Arguments

Value

The classification model.

See Also

svm, SVMl, SVMr

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)

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,
  ...
)

Arguments

Value

The classification model.

See Also

svm, SVM

Examples

## 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,
  ...
)

Arguments

Value

The classification model.

See Also

svm, SVM

Examples

## 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,
  ...
)

Arguments

Value

The classification model.

See Also

svm, SVRl, SVRr

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)

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,
  ...
)

Arguments

Value

The classification model.

See Also

svm, SVR

Examples

## 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,
  ...
)

Arguments

Value

The classification model.

See Also

svm, SVR

Examples

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

## End(Not run)

Text mining

Description

Apply data mining function on vectorized text

Usage

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

Arguments

Value

The result of the data mining method.

See Also

predict.textmining, textmining-class, vectorize.docs, vectorize.words

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)

t-distributed Stochastic Neighbor Embedding

Description

Return the t-SNE dimensionality reduction.

Usage

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

Arguments

See Also

Rtsne

Examples

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

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

Format

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

Source

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

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

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/

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).

See Also

APRIORI, predict.apriori, print.apriori, summary.apriori, apriori

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)

Arguments

Value

An augmented dataset.

Examples

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

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

Flea beetles dataset

Description

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

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

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.

See Also

ADABOOST, BAGGING, predict.boosting

Clustering Box Plots

Description

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

Usage

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

Arguments

See Also

boxplot

Examples

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

Format

The dataset has 18 instances described by 3 variables.

Depth

Description

Return the dept of a decision tree.

Usage

cartdepth(model)

Arguments

Value

The depth.

See Also

CART, cartinfo, cartleafs, cartnodes, cartplot

Examples

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)

Arguments

Value

Various information organized into a vector.

See Also

CART, cartdepth, cartleafs, cartnodes, cartplot

Examples

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)

Arguments

Value

The number of leafs.

See Also

CART, cartdepth, cartinfo, cartnodes, cartplot

Examples

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)

Arguments

Value

The number of nodes.

See Also

CART, cartdepth, cartinfo, cartleafs, cartplot

Examples

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, ...)

Arguments

See Also

CART, cartdepth, cartinfo, cartleafs, cartnodes

Examples

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

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.

See Also

CDA, plot.cda, predict.cda

Close a graphics device

Description

Close the graphics device driver

Usage

closegraphics()

See Also

exportgraphics, toggleexport, dev.off

Examples

## 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"))

Arguments

Value

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

See Also

compare.accuracy, compare.jaccard, compare.kappa, intern, stability

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)

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"))

Arguments

Value

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

See Also

compare.jaccard, compare.kappa, compare

Examples

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"))

Arguments

Value

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

See Also

compare.accuracy, compare.kappa, compare

Examples

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"))

Arguments

Value

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

See Also

compare.accuracy, compare.jaccard, compare

Examples

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)

Arguments

Value

The confusion matrix.

See Also

evaluation, performance, splitdata

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)

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

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.

See Also

labp, labc, nirp, nirc

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)

Arguments

Examples

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)

Arguments

Value

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

See Also

cor

Examples

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)

Arguments

Value

The evaluation of the predictions (numeric value).

See Also

roc.curves, performance

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"))

Credit dataset

Description

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

Usage

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
)

Arguments

Value

A randomly generated dataset.

See Also

data.parabol, data.target1, data.target2, data.twomoons, data.xor

Examples

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
)

Arguments

Value

A randomly generated dataset.

See Also

data.diag, data.parabol, data.target2, data.twomoons, data.xor

Examples

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
)

Arguments

Value

A randomly generated dataset.

See Also

data.diag, data.target1, data.target2, data.twomoons, data.xor

Examples

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
)

Arguments

Value

A randomly generated dataset.

See Also

data.diag, data.parabol, data.target2, data.twomoons, data.xor

Examples

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
)

Arguments

Value

A randomly generated dataset.

See Also

data.diag, data.parabol, data.target1, data.twomoons, data.xor

Examples

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
)

Arguments

Value

A randomly generated dataset.

See Also

data.diag, data.parabol, data.target1, data.target2, data.xor

Examples

data.twomoons ()

XOR dataset

Description

Generate "XOR" dataset.

Usage

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

Arguments

Value

A randomly generated dataset.

See Also

data.diag, data.gauss, data.parabol, data.target2, data.twomoons

Examples

data.xor ()

"data1" dataset

Description

Synthetic dataset.

Usage

data1

Format

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

Author(s)

Alexandre Blansché alexandre.blansche@univ-lorraine.fr

"data2" dataset

Description

Synthetic dataset.

Usage

data2

Format

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

Author(s)

Alexandre Blansché alexandre.blansche@univ-lorraine.fr

"data3" dataset

Description

Synthetic dataset.

Usage

data3

Format

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

Author(s)

Alexandre Blansché alexandre.blansche@univ-lorraine.fr

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.

See Also

splitdata

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).

See Also

DBSCAN

Decathlon dataset

Description

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

Usage

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)

Arguments

See Also

DBSCAN, dbscan

Examples

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

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.

See Also

EM, mclustModelNames

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

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"),
  ...
)

Arguments

Value

The evaluation of the predictions (numeric value).

See Also

confusion, evaluation.accuracy, evaluation.fmeasure, evaluation.fowlkesmallows, evaluation.goodness, evaluation.jaccard, evaluation.kappa, evaluation.precision, evaluation.recall, evaluation.msep, evaluation.r2, performance

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)

Accuracy of classification predictions

Description

Evaluation predictions of a classification model according to accuracy.

Usage

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

Arguments

Value

The evaluation of the predictions (numeric value).

See Also

evaluation.fmeasure, evaluation.fowlkesmallows, evaluation.goodness, evaluation.jaccard, evaluation.kappa, evaluation.precision, evaluation.precision, evaluation.recall, evaluation

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)

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, ...)

Arguments

Value

The evaluation of the predictions (numeric value).

See Also

evaluation.msep, evaluation

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)

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], ...)

Arguments

Value

The evaluation of the predictions (numeric value).

See Also

evaluation.accuracy, evaluation.fowlkesmallows, evaluation.goodness, evaluation.jaccard, evaluation.kappa, evaluation.precision, evaluation.precision, evaluation.recall, evaluation

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)

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], ...)

Arguments

Value

The evaluation of the predictions (numeric value).

See Also

evaluation.accuracy, evaluation.fmeasure, evaluation.goodness, evaluation.jaccard, evaluation.kappa, evaluation.precision, evaluation.precision, evaluation.recall, evaluation

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)

Goodness

Description

Evaluation predictions of a classification model according to Goodness index.

Usage

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

Arguments

Value

The evaluation of the predictions (numeric value).

See Also

evaluation.accuracy, evaluation.fmeasure, evaluation.fowlkesmallows, evaluation.jaccard, evaluation.kappa, evaluation.precision, evaluation.precision, evaluation.recall, evaluation

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)

Jaccard index

Description

Evaluation predictions of a classification model according to Jaccard index.

Usage

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

Arguments

Value

The evaluation of the predictions (numeric value).

See Also

evaluation.accuracy, evaluation.fmeasure, evaluation.fowlkesmallows, evaluation.goodness, evaluation.kappa, evaluation.precision, evaluation.precision, evaluation.recall, evaluation

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)

Kappa evaluation of classification predictions

Description

Evaluation predictions of a classification model according to kappa.

Usage

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

Arguments

Value

The evaluation of the predictions (numeric value).

See Also

evaluation.accuracy, evaluation.fmeasure, evaluation.fowlkesmallows, evaluation.goodness, evaluation.jaccard, evaluation.kappa, evaluation.precision, evaluation.precision, evaluation.recall, evaluation

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)

MSEP evaluation of regression predictions

Description

Evaluation predictions of a regression model according to MSEP

Usage

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

Arguments

Value

The evaluation of the predictions (numeric value).

See Also

evaluation.r2, evaluation

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)

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], ...)

Arguments

Value

The evaluation of the predictions (numeric value).

See Also

evaluation.accuracy, evaluation.fmeasure, evaluation.fowlkesmallows, evaluation.goodness, evaluation.jaccard, evaluation.kappa, evaluation.recall,evaluation

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)

R2 evaluation of regression predictions

Description

Evaluation predictions of a regression model according to R2

Usage

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

Arguments

Value

The evaluation of the predictions (numeric value).

See Also

evaluation.msep, evaluation

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)

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], ...)

Arguments

Value

The evaluation of the predictions (numeric value).

See Also

evaluation.accuracy, evaluation.fmeasure, evaluation.fowlkesmallows, evaluation.goodness, evaluation.jaccard, evaluation.kappa, evaluation.precision, evaluation

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)

Open a graphics device

Description

Starts the graphics device driver

Usage

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

Arguments

See Also

closegraphics, toggleexport, Devices

Examples

## 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()

Arguments

See Also

closegraphics, exportgraphics

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)

Factorial analysis results

Description

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

See Also

CA, MCA, PCA, plot.factorial

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
)

Arguments

Value

The filtered set of rules.

See Also

apriori, subset

Examples

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
)

Arguments

Value

The most frequent words of the corpus.

See Also

getvocab

Examples

## 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)

Arguments

Value

A set of rules, without redundancy.

See Also

apriori

Examples

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,
  ...
)

Arguments

Value

The vocabulary used in the corpus of documents.

See Also

plotzipf, stopwords, create_vocabulary

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)

Clustering evaluation through internal criteria

Description

Evaluation a clustering algorithm according to internal criteria.

Usage

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

Arguments

Value

The evaluation of the clustering.

See Also

compare, stability, intern.dunn, intern.interclass, intern.intraclass

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")

Clustering evaluation through Dunn's index

Description

Evaluation a clustering algorithm according to Dunn's index.

Usage

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

Arguments

Value

The evaluation of the clustering.

See Also

intern, intern.interclass, intern.intraclass

Examples

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"))

Arguments

Value

The evaluation of the clustering.

See Also

intern, intern.dunn, intern.intraclass

Examples

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"))

Arguments

Value

The evaluation of the clustering.

See Also

intern, intern.dunn, intern.interclass

Examples

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

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)

Arguments

See Also

PCA, factorial-class

Examples

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

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
)

Arguments

Value

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

See Also

pseudoF, kmeans

Examples

require (datasets)
data (iris)
kmeans.getk (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.

See Also

KNN, predict.knn

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)

Arguments

Examples

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

Linsep dataset

Description

Synthetic dataset.

Usage

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 alexandre.blansche@univ-lorraine.fr

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
)

Arguments

Value

The text contained in the dowloaded file.

See Also

download.file, unzip

Examples

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

## 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.

See Also

MEANSHIFT

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.

See Also

predict.model, predict

Movies dataset

Description

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

Usage

movies

Format

A set of 49 movies, rated by 55 users.

Source

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

Ozone dataset

Description

This dataset constains measurements on ozone level.

Usage

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.

See Also

MLP, MLPREG, SVM, SVR

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,
  ...
)

Arguments

Value

The evaluation of the predictions (numeric value).

See Also

confusion, evaluation, cost.curves, roc.curves

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)

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, ...)

Arguments

See Also

CDA, predict.cda, cda-class

Examples

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), ...)

Arguments

See Also

CA, MCA, PCA, plot.CA, plot.MCA, plot.PCA, factorial-class

Examples

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, ...)

Arguments

See Also

SOM, som-class

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

Plot actual vs. predictions

Description

Plot actual vs. predictions of a regression model.

Usage

plotavsp(predictions, gt)

Arguments

See Also

confusion, evaluation.accuracy, evaluation.fmeasure, evaluation.fowlkesmallows, evaluation.goodness, evaluation.jaccard, evaluation.kappa, evaluation.precision, evaluation.recall, evaluation.msep, evaluation.r2, performance

Examples

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", ...)

Arguments

See Also

plotzipf, getvocab, wordcloud

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)

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,
  ...
)

Arguments

See Also

treeplot, scatterplot, plot.som, boxclus

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)

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,
  ...
)

Arguments

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

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)

Arguments

See Also

plotcloud, 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)

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", ...)

Arguments

Value

A vector of predicted values (factor).

See Also

APRIORI, apriori-class, apriori

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])

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, ...)

Arguments

Value

A vector of predicted values (factor).

See Also

ADABOOST, BAGGING, boosting-class

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)

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, ...)

Arguments

Value

A vector of predicted values (factor).

See Also

CDA, plot.cda, cda-class

Examples

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, ...)

Arguments

See Also

DBSCAN

Examples

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, ...)

Arguments

See Also

EM

Examples

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, ...)

Arguments

See Also

KMEANS

Examples

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, ...)

Arguments

Value

A vector of predicted values (factor).

See Also

KNN, knn-class

Examples

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, ...)

Arguments

See Also

MEANSHIFT

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)

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, ...)

Arguments

Value

A vector of predicted values (factor).

See Also

model-class

Examples

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, ...)

Arguments

Value

A vector of predicted values (factor).

See Also

FEATURESELECTION, selection-class

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)

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, ...)

Arguments

Value

A vector of predicted values (factor).

See Also

TEXTMINING, textmining-class

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)

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, ...)

Arguments

See Also

APRIORI, predict.apriori, summary.apriori, apriori-class, apriori

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)

Plot function for factorial-class

Description

Print PCA, CA or MCA.

Usage

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

Arguments

See Also

CA, MCA, PCA, print.CA, print.MCA, print.PCA, factorial-class

Examples

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)

Arguments

Value

The pseudo-F of the clustering result.

See Also

kmeans.getk, KMEANS, kmeans

Examples

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

Document query

Description

Search for documents similar to the query.

Usage

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

Arguments

Value

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

See Also

vectorize.docs, sim2

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)

Word query

Description

Search for words similar to the query.

Usage

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

Arguments

Value

The Words the most similar to the query.

See Also

vectorize.words, sim2

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)

reg1 dataset

Description

Artificial dataset for simple regression tasks.

Usage

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 alexandre.blansche@univ-lorraine.fr

reg2 dataset

Description

Artificial dataset for simple regression tasks.

Usage

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 alexandre.blansche@univ-lorraine.fr

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, ...)

Arguments

Examples

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)

Arguments

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)

Plot ROC Curves

Description

This function plots ROC Curves of several classification predictions.

Usage

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

Arguments

Value

The evaluation of the predictions (numeric value).

See Also

cost.curves, performance

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"))

Rotation

Description

Rotation on two variables of a numeric dataset

Usage

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

Arguments

Value

A rotated data matrix.

Examples

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, ...)

Arguments

Value

The running time of function FUN.

See Also

difftime

Examples

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"),
  ...
)

Arguments

Examples

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,
  ...
)

Arguments

See Also

FEATURESELECTION, selection-class

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)

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.

See Also

FEATURESELECTION, predict.selection, selectfeatures

Snore dataset

Description

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

Usage

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 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).

See Also

plot.som, SOM, som

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).

See Also

SPECTRAL

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

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)

Arguments

Value

An object of class dataset-class.

See Also

dataset-class

Examples

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,
  ...
)

Arguments

Value

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

See Also

compare, intern

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)

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, ...)

Arguments

See Also

APRIORI, predict.apriori, print.apriori, apriori-class, apriori

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)

Temperature dataset

Description

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

Usage

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 object

Description

Object used for text mining.

Slots

vectorizer

The vectorizer.

vectors

The vectorized dataset.

res

The result of the text mining method.

See Also

TEXTMINING, vectorize.docs

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

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.

See Also

Titanic

Dendrogram Plots

Description

Draws a dendrogram.

Usage

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

Arguments

See Also

dendrogram, HCA, hclust, agnes

Examples

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

University dataset

Description

The dataset presents a french university demographics.

Usage

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,
  ...
)

Arguments

Value

The vectorized documents.

See Also

query.docs, stopwords, vectorizers

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)

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,
  ...
)

Arguments

Value

The vectorized words.

See Also

query.words, stopwords, vectorizers

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)

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.

See Also

vectorize.docs, query.docs

Vowels dataset

Description

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

Usage

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

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

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

Format

The dataset has 101 instances described by 17 qualitative variables.

Source

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