visualpred (version 0.1.2)

mcacontour: 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)

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

Arguments

dataf

data frame.

listconti

Interval variables to use, in format c("var1","var2",...).

listclass

Class variables to use, in format c("var1","var2",...).

vardep

Dependent binary classification variable.

proba

vector of probability predictions obtained externally (optional)

bins

Number of bins for categorize interval variables .

Dime1, Dime2

FAMD Dimensions to consider. Dim.1 and Dim.2 by default.

classvar

1 if dependent variable categories are plotted as supplementary

intergrid

scale of grid for contour:0 if automatic

selec

1 if stepwise logistic variable selection is required, 0 if not.

title

plot main title

title2

plot subtitle

listacol

vector of colors for labels

depcol

vector of two colors for points

alpha1

alpha transparency for majoritary class

alpha2

alpha transparency for minoritary class

alpha3

alpha transparency for fit probability plots

modelo

name of model: "glm","gbm","rf,","nnet","svm".

nodos

nnet: nodes

maxit

nnet: iterations

decay

nnet: decay

sampsize

rf: sampsize

mtry

rf: mtry

nodesize

rf: nodesize

ntree

rf: ntree

ntreegbm

gbm: ntree

shrink

gbm: shrink

bag.fraction

gbm: bag.fraction

n.minobsinnode

gbm:n.minobsinnode

C

svm Radial: C

gamma

svm Radial: gamma

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.

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)