visualpred (version 0.1.0)

mcamodelobis: 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=1,
Dime1="Dim.1",Dime2="Dim.2")

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.

bins

Number of bins for categorize interval variables .

selec

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

Dime1, Dime2

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

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

# NOT RUN {
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)
# }