Title:	Visualization 2D of Binary Classification Models
Version:	0.1.2
Description:	Visual contour and 2D point and contour plots for binary classification modeling under algorithms such as 'glm', 'rf', 'gbm', 'nnet' and 'svm', presented over two dimensions generated by 'famd' and 'mca' methods. Package 'FactoMineR' for multivariate reduction functions and package 'MBA' for interpolation functions are used. The package can be used to visualize the discriminant power of input variables and algorithmic modeling, explore outliers, compare algorithm behaviour, etc. It has been created initially for teaching purposes, but it has also many practical uses under the 'XAI' paradigm.
License:	GPL (≥ 3)
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.3
Imports:	gbm, randomForest, nnet (≥ 7.3.12), e1071, MASS (≥ 7.3.51.4), magrittr, FactoMineR (≥ 2.3), ggplot2 (≥ 3.3.0), mltools, dplyr, data.table, MBA, pROC, ggrepel
Suggests:	knitr, markdown,egg
VignetteBuilder:	knitr
Depends:	R (≥ 3.5.0)
NeedsCompilation:	no
Maintainer:	Javier Portela <javipgm@gmail.com>
Packaged:	2025-10-19 11:40:46 UTC; TrendingPC
Author:	Javier Portela [aut, cre]
Repository:	CRAN
Date/Publication:	2025-10-19 12:00:02 UTC

Home Mortgage Disclosure Act dataset

Description

Home Mortgage Disclosure Act dataset

Usage

data(Hmda)

Format

An object of class data.frame with 2380 rows and 13 columns.

Source

Stock, J. H. and Watson, M. W. (2007). Introduction to Econometrics, 2nd ed. Boston: Addison Wesley.

Breast Cancer Winsconsin dataset

Description

Breast Cancer Winsconsin dataset

Usage

data(breastwisconsin1)

Format

An object of class data.frame with 699 rows and 10 columns.

Source

https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)

Contour plots and FAMD function for classification modeling

Description

This function presents visual graphics by means of FAMD. FAMD function is Factorial Analysis for Mixed Data (interval and categorical) Dependent classification variable is set as supplementary variable. Machine learning algorithm predictions are presented in a filled contour setting

Usage

famdcontour(dataf=dataf,listconti,listclass,vardep,proba="",
title="",title2="",depcol="",listacol="",alpha1=0.7,alpha2=0.7,alpha3=0.7,
classvar=1,intergrid=0,selec=0,
modelo="glm",nodos=3,maxit=200,decay=0.01,
sampsize=400,mtry=2,nodesize=10,ntree=400,
ntreegbm=500,shrink=0.01,
bag.fraction=1,n.minobsinnode=10,C=100,
gamma=10,Dime1="Dim.1",Dime2="Dim.2",
offsetx=0.1,offsety=0.1)

Arguments

Details

FAMD algorithm from FactoMineR package is used to compute point coordinates on dimensions (Dim.1 and Dim.2 by default). Minority class on dependent variable category is represented as red, majority category as green. Color scheme can be altered using depcol and listacol, as well as alpha transparency values.

Predictive modeling

Logistic regression (glm) is used as default predictive model. Algorithms nnet, rf,gbm and svm-RBF can be used with basic parameter setting. A vector of fitted probabilities obtained externally from other algorithms can also be imported in parameter proba=nameofvector. Contour curves are in this particular case computed based on this vector. Before perfroming predictive modeling, selec=1 can be used to select variables with a stepwise BIC logistic regression. By default select=0 (all variables input are used). It is recommended to make variable selection process before using famdcontour, and use only useful variables as input.

Contour curves

Contour curves are build by the following process: i) the chosen algorithm model is trained and all observations are predicted-fitted. ii) A grid of points on the two chosen FAMD dimensions is built iii) package MBA is used to interpol probability estimates over the grid, based on previously fitted observations.

Variable representation

In order to represent interval variables, categories of class variables, and points in the same plot, a proportional projection of interval variables coordinates over the two dimensions range is applied. Since space of input variables is frequently larger than two dimensions, sometimes overlapping of points is produced; a frequency variable is used, and alpha values may be adjusted to avoid wrong interpretations of the presence of dependent variable category/color.

Troubleshooting

• Check missings. Missing values are not allowed.

• By default selec=0. Setting selec=1 may sometimes imply that no variables are selected; an error message is shown in this case.

• Models with only two input variables could lead to plot generation problems.

• Be sure that variables named in listconti are all numeric.

• If some numeric variable is constant at one single value, process is stopped since numeric Min-max standarization is performed, and NaN values are generated.

• Dependent variable can not be named x,y,z,x1,x2.

• When there are only categorical variables as input use mcacontour instead

Value

A list with the following objects:

graph1

plot of points on FAMD first two dimensions

graph2

plot of points and contour curves

graph3

plot of points and variables

graph4

plot of points variable and contour curves

graph5

plot of points colored by fitted probability

graph6

plot of points colored by abs difference

df1

data frame used for graph1

df2

data frame used for contour curves

df3

data frame used for variable names

df4

data frame for use in famdcontourlabel

listconti

interval variables used-selected

listclass

class variables used-selected

#'

...

color schemes and other parameters

References

Pages J. (2004). Analyse factorielle de donnees mixtes. Revue Statistique Appliquee. LII (4). pp. 93-111.

Examples

data(breastwisconsin1)
dataf<-breastwisconsin1
listconti=c( "clump_thickness","uniformity_of_cell_shape","mitosis")
listclass=c("")
vardep="classes"
result<-famdcontour(dataf=dataf,listconti,listclass,vardep)

Outliers in Contour plots and FAMD function for classification modeling

Description

This function adds outlier marks to famdcontour using ggrepel package.

Usage

famdcontourlabel(
  dataf = dataf,
  Idt = "",
  inf = 0.1,
  sup = 0.9,
  cutprob = 0.5,
  sizerepel = 3,
  ...
)

Arguments

Details

An identification variable can be set in Idt parameter. By default, number of row is used. There are two source of outliers: i) outliers in the two FAMD dimension space, where the cutpoints are set as quantiles given (inf=0.1 and sup=0.9 in both dimensions by default) and ii) outliers with respect to the fitted probability. The dependent variable is set to 1 for the minority class, and 0 for the majority class. Points considered outliers are those for which abs(vardep-fittedprob) excede parameter cutprob.

Troubleshooting

• Check missings. Missing values are not allowed.

• By default selec=0. Setting selec=1 may sometimes imply that no variables are selected; an error message is shown in this case.

• Models with only two input variables could lead to plot generation problems.

• Be sure that variables named in listconti are all numeric.

• If some numeric variable is constant at one single value, process is stopped since numeric Min-max standarization is performed, and NaN values are generated.

• Dependent variable can not be named x,y,z,x1,x2.

• When there are only categorical variables as input use mcacontour instead

Value

A list with the following objects:

graph1_graph6

plots for dimension outliers

graph7_graph12

plots for fit outliers

Examples

data(breastwisconsin1)
dataf<-breastwisconsin1
listconti=c( "clump_thickness","uniformity_of_cell_shape","mitosis")
listclass=c("")
vardep="classes"
result<-famdcontourlabel(dataf=dataf,listconti=listconti,
listclass=listclass,vardep=vardep)

Contour plots and MCA function for classification modeling

Description

This function presents visual graphics by means of Multiple correspondence Analysis projection. Interval variables are categorized to bins. Dependent classification variable is set as supplementary variable. Machine learning algorithm predictions are presented in a filled contour setting.

Usage

mcacontour(dataf=dataf,listconti,listclass,vardep,proba="",bins=8,
Dime1="Dim.1",Dime2="Dim.2",classvar=1,intergrid=0,selec=0,
title="",title2="",listacol="",depcol="",alpha1=0.8,alpha2=0.8,alpha3=0.7,modelo="glm",
nodos=3,maxit=200,decay=0.01,sampsize=400,mtry=2,nodesize=5,
ntree=400,ntreegbm=500,shrink=0.01,bag.fraction=1,n.minobsinnode=10,C=100,gamma=10)

Arguments

Details

This function applies MCA (Multiple Correspondence Analysis) in order to project points and categories of class variables in the same plot. In addition, interval variables listed in listconti are categorized to the number given in bins parameter (by default 8 bins). Further explanation about machine learning classification and contour curves, see the famdcontour function documentation.

Troubleshooting

• Check missings. Missing values are not allowed.

• By default selec=0. Setting selec=1 may sometimes imply that no variables are selected; an error message is shown in this case.

• Models with only two input variables could lead to plot generation problems.

• Be sure that variables named in listconti are all numeric.

• If some numeric variable is constant at one single value, process is stopped since numeric Min-max standarization is performed, and NaN values are generated.

• Dependent variable can not be named x,y,z,x1,x2.

Value

A list with the following objects:

graph1

plot of points on MCA two dimensions

graph2

plot of points and variables

graph3

plot of points and contour curves

graph4

plot of points, contour curves and variables

graph5

plot of points colored by fitted probability

graph6

plot of points colored by abs difference

df1

dataset used for graph1

df2

dataset used for graph2

df3

dataset used for graph3

df4

dataset used for graph4

listconti

interval variables used

listclass

class variables used

...

color schemes and other parameters

Examples

data(breastwisconsin1)
dataf<-breastwisconsin1
listconti=c( "clump_thickness","uniformity_of_cell_shape","mitosis")
listclass=c("")
vardep="classes"
result<-mcacontour(dataf=dataf,listconti,listclass,vardep)

Contour plots and MCA function for classification modeling

Description

This function is similar to mcacontour but points are jittered in every plot

Usage

mcacontourjit(dataf=dataf,jit=0.1,alpha1=0.8,alpha2=0.8,alpha3=0.7,title="",...)

Arguments

Value

A list with the following objects:

graph1

plot of points on MCA two dimensions

graph2

plot of points and variables

graph3

plot of points and contour curves

graph4

plot of points, contour curves and variables

graph5

plot of points colored by fitted probability

graph6

plot of points colored by abs difference

Examples

data(breastwisconsin1)
dataf<-breastwisconsin1
listconti=c( "clump_thickness","uniformity_of_cell_shape","mitosis")
listclass=c("")
vardep="classes"
result<-mcacontourjit(dataf=dataf,listconti=listconti,listclass=listclass,vardep=vardep,jit=0.1)

Basic MCA function for clasification

Description

This function presents visual graphics by means of Multiple correspondence Analysis projection. Interval variables are categorized to bins. Dependent classification variable is set as supplementary variable. It is used as base for mcacontour function.

Usage

mcamodelobis(dataf=dataf,listconti,listclass, vardep,bins=8,selec=0,
Dime1="Dim.1",Dime2="Dim.2")

Arguments

Value

A list with the following objects:

df1

dataset used for graph1

df2

dataset used for graph2

df3

dataset used for graph2

listconti

interval variables used

listclass

class variables used

axisx

axis definition in plot

axisy

axis definition in plot

Examples

data(breastwisconsin1)
dataf<-breastwisconsin1
listconti=c( "clump_thickness","uniformity_of_cell_shape","mitosis")
listclass=c("")
vardep="classes"
result<-mcacontour(dataf=dataf,listconti,listclass,vardep,bins=8,title="",selec=1)

nba dataset

Description

nba dataset

Usage

data(nba)

Format

An object of class data.frame with 1340 rows and 21 columns.

Source

https://data.world/exercises/logistic-regression-exercise-1

Pima indian diabetes dataset

Description

Pima indian diabetes dataset

Usage

data(pima)

Format

An object of class data.frame with 768 rows and 9 columns.

Source

https://sci2s.ugr.es/keel/dataset.php?cod=21

spiral sample data

Description

spiral sample data

Usage

data(spiral)

Format

An object of class data.frame with 803 rows and 3 columns.