mcamodelobis {visualpred} | R Documentation |
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
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)