Package {fdm2id}

Classification using AdaBoost

Description

Ensemble learning, through AdaBoost Algorithm.

Usage

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

Arguments

Value

The classification model.

See Also

BAGGING, predict.boosting

Examples

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

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,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

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),
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

Arguments

Value

The classification model.

See Also

ADABOOST, predict.boosting

Examples

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

Correspondence Analysis (CA)

Description

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

Usage

CA(
  d,
  ncp = ca.ncp(d, row.sup, col.sup, quanti.sup, quali.sup),
  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,
  xval = 10,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

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,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

Arguments

Details

The projection is computed from the class sizes, as the between-class scatter requires. The predictions, on the other hand, use equal prior probabilities – an observation goes to the nearest class centre in the canonical space, whatever the size of that class. This is the geometric reading plot.cda draws, and it is where CDA differs from LDA, which weights the classes by their observed frequencies: on an imbalanced problem the two do not predict the same thing.

Value

The classification model, as an object of class cda.

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 = 5, eps = NULL, graph = FALSE, ...)

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, eps = 1)

Expectation-Maximization clustering method

Description

Run the EM algorithm for clustering.

Usage

EM(d, k, model = "VVV", seed = NULL, ...)

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") fseval.univariate() else NULL,
  uninb = NULL,
  unithreshold = NULL,
  multieval = fseval.multivariate(),
  wrapmethod = NULL,
  mainmethod = wrapmethod,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

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)

Regression using Gradient Boosting

Description

This function builds a regression model using Gradient Boosting. It is the regression counterpart of GRADIENTBOOSTING, which classifies.

Usage

GBREG(
  x,
  y,
  ntree = 500,
  learningrate = 0.3,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

Arguments

Value

The regression model.

See Also

GRADIENTBOOSTING, LINREG, SVR, xgboost

Examples

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

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,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

Arguments

Value

The classification model.

See Also

xgboost

Examples

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

Hierarchical Cluster Analysis method

Description

Run the HCA method for clustering.

Usage

HCA(
  d,
  k = NULL,
  method = c("ward", "single"),
  engine = c("hclust", "agnes"),
  graph = FALSE,
  ...
)

Arguments

Value

The cluster hierarchy (hca object).

See Also

hclust, agnes, treeplot, predict.hca

Examples

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

Kernel Regression

Description

This function builds a kernel regression model.

Usage

KERREG(
  x,
  y,
  bandwidth = 1,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

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", "silhouette", "gap", "elbow"),
  nstart = 10,
  B = 100,
  graph = FALSE,
  seed = NULL,
  ...
)

Arguments

Details

The four criteria criterion offers, all computed between 2 clusters and k:

"pseudo-F"

the Calinski-Harabasz index, between-cluster over within-cluster variance corrected for the number of clusters. Maximised.

"silhouette"

the mean silhouette width – how much closer each observation is to its own cluster than to the nearest other one. Maximised.

"gap"

the gap statistic: the distance between the observed within-cluster dispersion and the one expected with no cluster structure at all. The retained k is the smallest whose gap is within one standard error of the next. It is the only criterion that can answer k = 1, and much the slowest, needing B bootstrap samples.

"elbow"

the bend of the total within-cluster sum of squares. That quantity decreases with k whatever the data, so there is no optimum to take: the retained k is the point furthest from the chord joining the two ends of the curve, drawn on the graphic.

The last three need the cluster package.

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,
  nfolds = 10,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

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,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

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 = if (reg[1] == "subset") c("bic", "adjr2", "cp", "r2") else c("r2", "msep"),
  scale = TRUE,
  validation = c("CV", "LOO"),
  lambda = 10^seq(-5, 5, length.out = 101),
  alpha = 0.5,
  nrep = 1,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

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 independent variable
data (cars)
LINREG (cars [, -2], cars [, 2])
# With two independent variables
data (trees)
LINREG (trees [, -3], trees [, 3])
# With non numeric variables
data (ToothGrowth)
LINREG (ToothGrowth [, -1], ToothGrowth [, 1], quali = "intercept") # Different intercept
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,
  reg = c("none", "ridge", "lasso", "elastic"),
  lambda = NULL,
  alpha = 0.5,
  nfolds = 10,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

Arguments

Value

The classification model.

See Also

multinom, glmnet, LINREG

Examples

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

# Penalized variants: same three penalties as LINREG()
d = splitdata (iris, 5, seed = 0)
model = LR (d$train.x, d$train.y, reg = "lasso")
evaluation (predict (model, d$test.x), d$test.y)

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 = mca.ncp(d, ind.sup, quanti.sup, quali.sup),
  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,
  seed = NULL,
  ...
)

Arguments

Value

The clustering (meanshift object).

See Also

meanShift, predict.meanshift

Examples

require (datasets)
data (iris)
MEANSHIFT (iris [, -5], bandwidth = .75)

Classification using Multilayer Perceptron

Description

This function builds a classification model using Multilayer Perceptron.

Usage

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

Arguments

Value

The classification model.

See Also

nnet

Examples

require (datasets)
data (iris)
MLP (iris [, -5], iris [, 5], hidden = 4, decay = .1)

Multi-Layer Perceptron Regression

Description

This function builds a regression model using MLP.

Usage

MLPREG(
  x,
  y,
  size = if (is.vector(x)) 2 else 2:ncol(x),
  decay = 10^(-3:-1),
  nfolds = 10,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

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,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

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, seed = NULL, ...)

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)

Clustering using K-medoids (PAM)

Description

Partitions the data into k clusters around medoids – actual observations of the dataset – rather than around means. Being an observation, a medoid can be shown to students as a representative example of its cluster, and the method tolerates outliers much better than K-means, which drags a mean towards them.

Usage

PAM(
  d,
  k = 9,
  criterion = c("none", "silhouette"),
  graph = FALSE,
  seed = NULL,
  ...
)

Arguments

Value

The clustering, as an object of class pam (see pam), with a cluster component holding the assignments and a medoids one holding the representative observations.

See Also

KMEANS, pam, kmeans.getk

Examples

require (datasets)
data (iris)
model = PAM (iris [, -5], 3)
model$medoids
table (model$cluster, iris [, 5])

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 = pca.ncp(d, ind.sup, quanti.sup, 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,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

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,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

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,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

Arguments

Value

The classification model.

See Also

randomForest

Examples

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

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,
  seed = 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 = FALSE, seed = NULL, ...)

Arguments

See Also

spectral-class

Examples

require (datasets)
data (iris)
SPECTRAL (iris [, -5], k = 3)

Classification using one-level decision tree

Description

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

Usage

STUMP(
  train,
  labels,
  randomvar = FALSE,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

Arguments

Value

The classification model.

See Also

CART

Examples

require (datasets)
data (iris)
STUMP (iris [, -5], iris [, 5])
STUMP (iris [, -5], iris [, 5], randomvar = TRUE, seed = 0)

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"),
  nfolds = 10,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

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),
  nfolds = 10,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

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),
  nfolds = 10,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

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),
  nfolds = 10,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

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),
  nfolds = 10,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

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),
  nfolds = 10,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

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

require (text2vec)
# A small subset of movie_review is used here so this example runs quickly.
data ("movie_review")
d = movie_review [1:300, 2:3]
d [, 1] = factor (d [, 1])
d = splitdata (d, 1)
model = TEXTMINING (d$train.x, NB, labels = d$train.y, mincount = 10)
pred = predict (model, d$test.x)
evaluation (pred, d$test.y)
data (capitals)
clusters = TEXTMINING (capitals, HCA, vector = "words", k = 5, mincount = 2, ndim = 10, maxiter = 5)
plotclus (clusters$res, capitals, type = "tree", labels = TRUE)

t-distributed Stochastic Neighbor Embedding

Description

Return the t-SNE dimensionality reduction.

Usage

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

Arguments

Value

The Rtsne result. Rtsne requires distinct observations, so duplicated rows of x are removed before fitting; the returned Y (and costs) are then expanded back to nrow (x) rows, in the order of x, so that they can be used directly alongside the original dataset – duplicated observations simply share the same coordinates.

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 depicts alcohol consumption habits in 27 European countries (in 2010).

Usage

alcohol

Format

The dataset has 27 instances described by 4 variables. The variables are the average amount of alcohol of different types consumed per year per inhabitant.

Source

https://www.who.int/

APRIORI classification model

Description

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

Details

Objects of this class are plain lists with the following components:

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)
# 'target' can also be given as a column name
d = augmentation (iris, "Species")

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/dataset/9/auto+mpg

Shared documentation for the 'average' and 'positive' parameters

Description

This function is never called: it holds the canonical documentation of the average and positive parameters, shared (via @inheritParams) by the six evaluation measures built on a precision and a recall.

Usage

average.doc(average, positive)

Arguments

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).

Species

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 ensemble of models obtained by a boosting or bagging method (ADABOOST, BAGGING).

Details

Objects of this class are plain lists with the following components:

models

List of models.

x

The learning set.

y

The target values.

nsamples

The number of models that were asked for. Boosting keeps fewer when it runs out of models better than chance; print.boosting says so.

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.

Capitals dataset

Description

A small corpus of English sentences about France, Germany and their capitals. It is deliberately tiny, so that the examples of the text mining functions run in a moment; a real corpus is loaded with loadtext.

Usage

capitals

Format

A character vector of 30 sentences, lowercase and free of punctuation.

Author(s)

Alexandre Blansché alexandre.blansche@univ-lorraine.fr

See Also

getvocab, vectorize.docs, vectorize.words, loadtext

Depth

Description

Return the depth 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.

Details

Objects of this class are plain lists with the following components:

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 intra-class covariance matrix. A matrix.

eig

One row per canonical axis and four columns: the eigenvalue (of V^{-1}B, i.e. the squared canonical correlation, between 0 and 1), its percentage of variance (share of the trace) and the cumulative one, and the discriminant power – the share of the trace of W^{-1}B, which is what lda and most other software call the proportion of trace. A matrix, or a named vector when there is a single axis.

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

Check and clean class labels

Description

Internal helper shared by the classification methods that cannot cope with empty classes. It coerces labels to a factor (which makes it work with character vectors, for which nlevels returns 0), drops the levels that are not observed (warning about them) and checks that at least two classes remain.

Usage

check.classes(labels, method = "", min.classes = 2)

Arguments

Value

labels, as a factor with no empty level.

Close a graphics device

Description

Close the graphics device driver

Usage

closegraphics(export = fdm2id.globals$export)

Arguments

See Also

exportgraphics, toggleexport, dev.off

Examples

## Not run: 
data (iris)
exportgraphics ("export.pdf")
plotdata (iris [, -5], iris [, 5])
closegraphics()
# Explicit override, ignoring the global toggle:
closegraphics (export = TRUE)

## 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.

The pairwise index

the Jaccard index on pairs – the pairs both partitions group together, over the pairs at least one of them groups. Unlike the Rand index of compare.accuracy it ignores the pairs both keep apart, a cell that dominates as soon as there are many clusters: 150 singletons out of 150 observations score 0.67 with the Rand index and 0 here.

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.

The pairwise index

Cohen's kappa on the fourfold table of pair agreements, i.e. the Rand index of compare.accuracy corrected for the agreement expected by chance. That quantity is also the Hubert-Arabie adjusted Rand index – a theorem of Warrens (2008), not a substitution.

References

Warrens, M.J. (2008). On the Equivalence of Cohen's Kappa and the Hubert-Arabie Adjusted Rand Index. Journal of Classification, 25(2), 177-183.

See Also

compare.accuracy, compare.jaccard, compare

Examples

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

Confusion matrix

Description

Plot a confusion matrix. Rows are the true labels and columns the predicted ones.

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$test.x)
confusion (pred, d$test.y)

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,
  positive = levels(factor(gt))[1],
  type = c("auto", "fuzzy", "hard"),
  ...
)

Arguments

Value

Nothing; the curves are drawn on the current graphics device.

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])
# From the estimated probabilities (the meaningful version)
cost.curves (predict (model.nb, d [, -5], fuzzy = TRUE), d [, 5])
# From hard labels, for comparison
cost.curves (cbind (predict (model.nb, d [, -5]), predict (model.lda, d [, -5])),
             d [, 5], c ("NB", "LDA"), type = "hard")

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 = FALSE,
  seed = NULL
)

Arguments

Value

A randomly generated dataset.

See Also

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

Examples

data.diag (graph = TRUE)

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 = FALSE,
  seed = NULL
)

Arguments

Value

A randomly generated dataset.

See Also

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

Examples

data.gauss (graph = TRUE)

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 = FALSE,
  seed = NULL
)

Arguments

Value

A randomly generated dataset.

See Also

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

Examples

data.parabol (graph = TRUE)

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 = FALSE,
  seed = NULL
)

Arguments

Value

A randomly generated dataset.

See Also

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

Examples

data.target1 (graph = TRUE)

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 = FALSE,
  seed = NULL
)

Arguments

Value

A randomly generated dataset.

See Also

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

Examples

data.target2 (graph = TRUE)

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 = FALSE,
  seed = NULL
)

Arguments

Value

A randomly generated dataset.

See Also

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

Examples

data.twomoons (graph = TRUE)

XOR dataset

Description

Generate "XOR" dataset.

Usage

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

Arguments

Value

A randomly generated dataset.

See Also

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

Examples

data.xor (graph = TRUE)

"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.

Details

Objects of this class are plain lists with the following components:

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.

Details

Objects of this class are plain lists with the following components:

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.

Details

Objects of this class are plain lists with the following components:

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/en/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 the adjusted R2, i.e. the R2 penalized by the number of variables used by the model.

Usage

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

Arguments

Value

The evaluation of the predictions (numeric value).

See Also

evaluation.r2, 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$test.x)
evaluation.adjr2 (pred.linreg, d$test.y, ncol = ncol (d$test.x))

F-measure

Description

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

Usage

evaluation.fmeasure(
  predictions,
  gt,
  beta = 1,
  average = NULL,
  positive = NULL,
  ...
)

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,
  average = NULL,
  positive = NULL,
  ...
)

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,
  average = NULL,
  positive = NULL,
  ...
)

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, average = NULL, positive = NULL, ...)

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 Cohen's kappa: the proportion of correct predictions, corrected for the proportion two independent labellings would get right by chance. Class labels being nominal, the kappa is the unweighted one – every mistake counts the same.

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.

Usage

evaluation.precision(predictions, gt, average = NULL, positive = NULL, ...)

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.

Usage

evaluation.recall(predictions, gt, average = NULL, positive = NULL, ...)

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),
  export = fdm2id.globals$export,
  ...
)

Arguments

See Also

closegraphics, toggleexport, Devices

Examples

## Not run: 
data (iris)
exportgraphics ("export.pdf")
plotdata (iris [, -5], iris [, 5])
closegraphics()
# Extensions that don't match an R function name directly are now handled:
exportgraphics ("export.eps")
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 result of a factorial analysis, as obtained by CA, MCA or PCA.

Details

Objects of this class are the objects returned by the corresponding FactoMineR functions (CA, MCA and PCA), with the extra class factorial prepended so that plot.factorial can provide a uniform plotting interface. The second class ("ca", "mca" or "pca") records which analysis was performed.

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, parameter = list (supp = .4, conf = .8))
inspect (filter.rules (r, right = "marital-status="))
# The equivalent call in arules itself
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,
  excludewords = NULL,
  removesinglechars = TRUE
)

Arguments

Value

The most frequent words of the corpus.

See Also

getvocab

Examples

data (capitals)
frequentwords (capitals, 10, mincount = 2)
vocab = getvocab (capitals, mincount = 2)
frequentwords (vocab, 10)

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")
# The default support (0.1) yields ~6000 rules on Adult, and general.rules() compares every
# pair of them twice: that single call took more than 8 seconds. A higher support keeps the
# example instructive (169 rules, of which 8 are general) and instantaneous.
r = apriori (Adult, parameter = list (supp = .4, conf = .8))
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,
  excludewords = NULL,
  removesinglechars = TRUE,
  ...
)

Arguments

Value

The vocabulary used in the corpus of documents.

See Also

plotzipf, stopwords, create_vocabulary

Examples

data (capitals)
vocab1 = getvocab (capitals, mincount = 2) # With stemming
nrow (vocab1)
vocab2 = getvocab (capitals, mincount = 2, lang = NULL) # Without stemming
nrow (vocab2)
# Excluding additional, corpus-specific words
vocab3 = getvocab (capitals, mincount = 2, excludewords = c ("capital", "europe"))

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$cluster, iris [, -5])
intern (km$cluster, iris [, -5], type = "cluster")
intern (km$cluster, iris [, -5], eval = c ("intraclass", "interclass"))
intern (km$cluster, 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", "cluster"))

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$cluster, iris [, -5])
intern.dunn (km$cluster, iris [, -5], type = "cluster")

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$cluster, 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$cluster, 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 variables. The last variable is the class.

Source

https://archive.ics.uci.edu/dataset/52/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 = c("pseudo-F", "silhouette", "gap", "elbow"),
  nstart = 10,
  B = 100,
  graph = FALSE,
  seed = NULL
)

Arguments

Value

The number of clusters retained by the chosen criterion.

References

Tibshirani, R., Walther, G. and Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B, 63(2), 411-423.

See Also

pseudoF, KMEANS, kmeans, silhouette, clusGap

Examples

require (datasets)
data (iris)
kmeans.getk (iris [, -5])
kmeans.getk (iris [, -5], criterion = "silhouette")
kmeans.getk (iris [, -5], criterion = "elbow")

# The gap statistic resamples, so it is much slower than the other three.
kmeans.getk (iris [, -5], criterion = "gap", B = 20, seed = 0)

K Nearest Neighbours model

Description

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

Details

Objects of this class are plain lists with the following components:

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 = NULL,
  dir = tempdir(),
  collapse = TRUE,
  sep = NULL,
  categories = NULL,
  cache = FALSE
)

Arguments

Value

The text contained in the dowloaded file.

See Also

download.file, unzip

Examples

# Not run automatically: this downloads a 31 MB archive from a third-party server, so it
# depends on both the network and that server staying up.
## Not run: 
text = loadtext ("http://mattmahoney.net/dc/text8.zip")
# Keep the archive between calls, in a directory of your choosing
text = loadtext ("http://mattmahoney.net/dc/text8.zip", dir = "~/corpora", cache = TRUE)

## End(Not run)

MeanShift model

Description

This class contains the model obtained by the MEANSHIFT method.

Details

Objects of this class are plain lists with the following components:

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 an 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.

Details

Objects of this class are plain lists with the following components:

model

The wrapped model.

method

The name of the method.

See Also

predict.model, predict

Movies dataset

Description

Simulated ratings of 49 real films by 55 imaginary viewers.

Usage

movies

Format

A matrix of 49 films by 55 viewers. Ratings are whole numbers from 1 to 10.

Source

The structure was estimated on the MovieLens 100K dataset (https://grouplens.org/datasets/movielens/100k/), whose terms do not permit redistributing the ratings themselves.

Ozone dataset

Description

This dataset constains measurements on ozone level.

Usage

ozone

Format

112 days described by 13 variables. maxO3, the maximum level of ozone measured during the day (the target variable); T9, T12, T15, temperatures; C9, C12, C15, cloud cover; W9, W12, W15, the projection of the wind on the North-South axis; maxO3v, the maximum level of ozone of the previous day; vent, the wind direction (a factor: "Est", "Nord", "Ouest", "Sud"); pluie, whether it rained (a factor: "Pluie", "Sec").

Source

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

Learning Parameters

Description

This class contains main parameters for various learning methods.

Details

Objects of this class are plain lists with the following components:

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,
  fuzzy = FALSE,
  positive = NULL,
  stratify = TRUE,
  ...
)

Arguments

Value

The evaluation of the predictions (numeric value).

See Also

confusion, evaluation, cost.curves, roc.curves

Examples

## Not run: 
require ("datasets")
data (iris)
# The simplest use: a training set, a test set, and the score of the model fitted on the
# first and evaluated on the second. Same thing as
# evaluation.accuracy (predict (NB (d$train.x, d$train.y), d$test.x), d$test.y).
d = splitdata (iris, 5, seed = 0)
performance (NB, d$train.x, d$train.y, d$test.x, d$test.y)
# Several methods and criteria at once
performance (c (NB, LDA, CART), d$train.x, d$train.y, d$test.x, d$test.y,
             eval = c ("accuracy", "kappa"))
# 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
data (linsep)
performance (c (NB, LDA, LR), linsep [, -3], linsep [, 3], type = "roc",
             protocol = "loocv", seed = 0)
# Same curves, read from the hard predicted labels instead of the class-membership
# scores: each method collapses to a single operating point.
performance (c (NB, LDA, LR), linsep [, -3], linsep [, 3], type = "roc",
             protocol = "loocv", seed = 0, fuzzy = FALSE)
# Choosing the positive class explicitly
performance (NB, linsep [, -3], linsep [, 3], type = "roc", protocol = "loocv",
             seed = 0, positive = levels (linsep [, 3]) [2])
# 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 apriori-class

Description

Plot the association rules obtained by APRIORI, using arulesViz.

Usage

## S3 method for class 'apriori'
plot(
  x,
  method = "scatterplot",
  measure = c("support", "confidence"),
  shading = "lift",
  ...
)

Arguments

See Also

APRIORI, apriori-class, plot

Examples

## Not run: 
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)
plot (model)
plot (model, method = "graph")

## 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, legendpos = "topleft", ...)

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),
  col = NULL,
  pch = NULL,
  labels = FALSE,
  legendpos = "topleft",
  ...
)

Arguments

See Also

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

Examples

require (datasets)
data (iris)
pca = PCA (iris, quali.sup = 5)
plot (pca) # Automatically colored/legended by the qualitative supplementary variable
plot (pca, type = "cor")
plot (pca, type = "eig")
# Overriding colors and point styles manually (e.g. by an external clustering)
km = KMEANS (iris [, -5], k = 3)
plot (pca, col = km$cluster + 1, pch = km$cluster + 1)

Plot a feature selection

Description

Draws the score every variable obtained, the ones that were kept apart from the ones that were not. Only a selection made by ranking the variables (algorithm = "ranking") can be drawn this way: it is the only one that scores them one by one, where the other three score whole subsets.

Usage

## S3 method for class 'selection'
plot(x, horiz = TRUE, legendpos = "bottomright", ...)

Arguments

See Also

selectfeatures, selection-class, print.selection

Examples

require (datasets)
data (iris)
# How useful a random forest finds each variable, and the two it would keep
selection = selectfeatures (iris [, -5], iris [, 5], unieval = "randomforest", uninb = 2)
selection
plot (selection)

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

data (capitals)
plotcloud (capitals)
vocab = getvocab (capitals, mincount = 1, lang = NULL, stopwords = "en")
plotcloud (vocab)

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], k = 3, method = "ward")
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,
  target = NULL,
  type = c("pairs", "scatter", "parallel", "boxplot", "histogram", "barplot", "pie",
    "heatmap", "heatmapc", "correlation", "pca", "cda", "svd", "nmf", "tsne", "som",
    "words"),
  legendpos = "topleft",
  alpha = 200,
  asp = 1,
  labels = FALSE,
  tsne = NULL,
  nmf = NULL,
  ...
)

Arguments

Details

type = "correlation" draws how strongly each variable relates to the target, sorted, strongest at the top. Two different quantities, depending on the target. Against a numeric target it is Pearson's correlation r, sign included, and the axis says so. Against a categorical one it is the correlation ratio \eta, the square root of the between-class share of the variance – not a Pearson coefficient computed on class numbers, which would depend on the order the classes happen to be in and would mean nothing beyond two classes. \eta lies in [0, 1], is defined for any number of classes and does not depend on their coding. On exactly two classes \eta is |r| with the classes coded 0/1, so the sign comes back and says which class the variable is larger in.

The projections (type = "pca", and the default "scatter"/"pairs" on more than two variables) are computed on the data as they are, unscaled – plotdata shows a dataset, whereas PCA performs a factorial analysis and centres and scales by default. So plotdata (d, type = "pca") and plot (PCA (d)) differ visibly when the variables have very different scales, and PCA is the one to use for a properly scaled projection.

Examples

require (datasets)
data (iris)
# Without classification
plotdata (iris [, -5]) # Default (pairs)
# With classification
plotdata (iris [, -5], iris [, 5]) # Default (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]) # Default (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]) # Default (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
## Not run: 
# Reusing a previously computed t-SNE embedding instead of recomputing it
res = TSNE (iris [, -5])
plotdata (iris [, -5], iris [, 5], type = "tsne", tsne = res)

## End(Not run)

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

data (capitals)
plotzipf (capitals)
vocab = getvocab (capitals, mincount = 1, lang = NULL)
plotzipf (vocab)

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)

Projection of new observations into a factorial space

Description

Projects new observations into the factorial space computed by CA, MCA or PCA – the same operation that is applied to the observations the analysis was fitted on: centering (and, for PCA with scale.unit = TRUE, scaling) with the parameters of the training data, then projection on the axes already computed. The axes are not recomputed and the new observations have no influence on them: they are supplementary individuals.

Usage

## S3 method for class 'factorial'
predict(object, test, ...)

Arguments

Details

The projection is obtained by handing the new rows back to FactoMineR as supplementary individuals of the original analysis, so the coordinates are exactly the ones PCA (rbind (train, test), ind.sup = ...) would give. The active analysis is refitted in the process, which is unnoticeable on the sizes this package is meant for.

Supplementary variables (quanti.sup, quali.sup) play no part in the axes, so test does not have to carry them: any column of the training data that is missing from test is filled in (with the training mean, or the first level) purely so that the two can be stacked.

Value

The coordinates of the new observations on the factorial axes (a matrix, one row per observation and one column per axis).

See Also

PCA, CA, MCA, factorial-class, predict.cda

Examples

require (datasets)
data (iris)
d = splitdata (iris, 5)
pca = PCA (d$train.x)
# The coordinates of unseen observations on the axes of the training analysis
head (predict (pca, d$test.x))
# An observation of the training set projects onto the coordinates the analysis gave it
pca$ind$coord [1, ]
predict (pca, d$train.x [1, ])

Predict function for hierarchical clustering