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
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")data frame.
Interval variables to use, in format c("var1","var2",...).
Class variables to use, in format c("var1","var2",...).
Dependent binary classification variable.
vector of probability predictions obtained externally (optional)
plot main title
plot subtitle
vector of two colors for points
vector of colors for labels
alpha transparency for majoritary class
alpha transparency for minoritary class
alpha transparency for fit probability plots
1 if dependent variable categories are plotted as supplementary
scale of grid for contour:0 if automatic
1 if stepwise logistic variable selection is required, 0 if not.
name of model: "glm","gbm","rf,","nnet","svm".
nnet: nodes
nnet: iterations
nnet: decay
rf: sampsize
rf: mtry
rf: nodesize
rf: ntree
gbm: ntree
gbm: shrink
gbm: bag.fraction
gbm:n.minobsinnode
svm Radial: C
svm Radial: gamma
FAMD Dimensions to consider. Dim.1 and Dim.2 by default.
A list with the following objects:
plot of points on FAMD first two dimensions
plot of points and contour curves
plot of points and variables
plot of points variable and contour curves
plot of points colored by fitted probability
plot of points colored by abs difference
data frame used for graph1
data frame used for contour curves
data frame used for variable names
interval variables used-selected
class variables used-selected
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.
For predictive modeling, selec=1 selects variables with a simple stepwise logistic regression. By default select=0. Logistic regression is used by default. Basic parameter setting is supported for algorithms nnet, rf,gbm and svm-RBF. A vector of fitted probabilities obtained externally from other algorithms can be imported in parameter proba=nameofvector. Contour curves are then computed based on this vector.
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.
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.
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 n 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.
Pages J. (2004). Analyse factorielle de donnees mixtes. Revue Statistique Appliquee. LII (4). pp. 93-111.
# NOT RUN {
data(breastwisconsin1)
dataf<-breastwisconsin1
listconti=c( "clump_thickness","uniformity_of_cell_shape","mitosis")
listclass=c("")
vardep="classes"
result<-famdcontour(dataf=dataf,listconti,listclass,vardep)
# }
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