RSDA (version 1.1)

An introduction to RSDA: R to Symbolic Data Analysis

Description

This work is framed inside the Symbolic Data Analysis (SDA). The objective of this work is to implement in R to the symbolic case certain techniques of the automatic classification, as well as some lineal models. These implementations will always be made following two fundamental principles in Symbolic Data Analysis like they are: Classic Data Analysis should always be a case particular case of the Symbolic Data Analysis and both, the exit as the input in an Symbolic Data Analysis should be symbolic. We implement for variables of type interval the mean, the median, the mean of the extreme values, the standard deviation, the deviation quartil, the dispersion boxes and the correlation also three new methods are also presented to carry out the lineal regression for variables of type interval. We also implement in this R package the method of Principal Components Analysis in two senses: First, we propose three ways to project the interval variables in the circle of correlations in such way that is reflected the variation or the inexactness of the variables. Second, we propose an algorithm to make the Principal Components Analysis for variables of type histogram. We implement a method for multidimensional scaling of interval data, denominated INTERSCAL.

Details

ll{ Package: RSDA Type: Package Version: 1.1 Date: 2013-06-29 License: GPL (>=2) } Most of the function of the package stars from a symbolic data table that can be store in a CSV file withe follwing forma: In the first row the labels $C means that follows a continuous variable, $I means an interval variable, $H means a histogram variables and $S means set variable. In the first row each labels should be follow of a name to variable and to the case of histogram a set variables types the names of the modalities (categories) . In data rows for continuous variables we have just one value, for interval variables we have the minimum and the maximum of the interval, for histogram variables we have the number of modalities and then the probability of each modality and for set variables we have the cardinality of the set and next the elements of the set.

References

Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Billard L., Douzal-Chouakria A. and Diday E. (2011) Symbolic Principal Components For Interval-Valued Observations, Statistical Analysis and Data Mining. 4 (2), 229-246. Wiley.

Bock H-H. and Diday E. (eds.) (2000). Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data. Springer, Germany.

Carvalho F., Souza R.,Chavent M., and Lechevallier Y. (2006) Adaptive Hausdorff distances and dynamic clustering of symbolic interval data. Pattern Recognition Letters Volume 27, Issue 3, February 2006, Pages 167-179

Cazes P., Chouakria A., Diday E. et Schektman Y. (1997). Extension de l'analyse en composantes principales a des donnees de type intervalle, Rev. Statistique Appliquee, Vol. XLV Num. 3 pag. 5-24, France.

Diday, E., Rodriguez O. and Winberg S. (2000). Generalization of the Principal Components Analysis to Histogram Data, 4th European Conference on Principles and Practice of Knowledge Discovery in Data Bases, September 12-16, 2000, Lyon, France.

Chouakria A. (1998) Extension des methodes d'analysis factorialle a des donnees de type intervalle, Ph.D. Thesis, Paris IX Dauphine University.

Makosso-Kallyth S. and Diday E. (2012). Adaptation of interval PCA to symbolic histogram variables, Advances in Data Analysis and Classification July, Volume 6, Issue 2, pp 147-159.

Rodriguez, O. (2000). Classification et Modeles Lineaires en Analyse des Donnees Symboliques. Ph.D. Thesis, Paris IX-Dauphine University.

See Also

display.sym.table

Examples

data(example3)
sym.data<-example3
display.sym.table(sym.data)
sym.scatterplot(sym.var(sym.data,1),sym.var(sym.data,4),col='blue',main='Main Title')

data(oils)  
res<-sym.interval.pca(oils,'centers')
sym.scatterplot(sym.var(res$Sym.Components,1),sym.var(res$Sym.Components,2),
  labels=TRUE,col='red',main='PCA Oils Data')
sym.scatterplot3d(sym.var(res$Sym.Components,1),sym.var(res$Sym.Components,2),
   sym.var(res$Sym.Components,3),color='blue',main='PCA Oils Data')
sym.scatterplot.ggplot(sym.var(res$Sym.Components,1),
   sym.var(res$Sym.Components,2), labels=TRUE)
sym.circle.plot(res$Sym.Prin.Correlations)

res<-sym.interval.pca(oils,'classic')
plot.PCA(res,choix="ind")
plot.PCA(res,choix="var")

data(lynne2)
res<-sym.interval.pca(lynne2,'centers')
sym.scatterplot(sym.var(res$Sym.Components,1),sym.var(res$Sym.Components,2),
  labels=TRUE, col='red',main='PCA Lynne Data')				  
sym.scatterplot3d(sym.var(res$Sym.Components,1),sym.var(res$Sym.Components,2),
  sym.var(res$Sym.Components,3),color='blue', main='PCA Lynne Data')
sym.scatterplot.ggplot(sym.var(res$Sym.Components,1),sym.var(res$Sym.Components,2),
  labels=TRUE)
sym.circle.plot(res$Sym.Prin.Correlations)