The three-way data consists of a set of variables measured on several groups of individuals. To each group is associated an estimated probability density function. The package provides functional methods (principal component analysis, multidimensional scaling, cluster analysis, discriminant analysis...) for such probability densities.
The main functions applying to the probability densities are:
fpcad: functional principal component analysis,
fmdsd: multidimensional scaling,
fhclustd: hierarchical clustering,
fdiscd.misclass: functional discriminant analysis in order to compute the misclassification ratio with the one-leave-out method,
fdiscd.predict: discriminant analysis in order to predict the class (synonymous with cluster, not to be confused with the class attribute of an R object) of each probability density whose class is unknown.
The above functions are completed by:
print() method for objects of class
fdiscd.predict, in order to display the results of the corresponding function,
plot() method for objects of class
fhclustd, in order to display some useful graphics attached to the corresponding function,
A generic function
interpret that applies to objects of class
fmdsd, helps the user to interpret the scores returned by the corresponding function, in terms of moments.
We also introduce classes of objects and tools in order to handle collections of data frames:
folder creates an object of class
folder, that is a list of data frames which have in common the same columns.
The following functions apply to a folder and compute some statistics on the columns of its elements:
folderh creates an object of class
folderh, that is a list of data frames with a hierarchic relation between each pair of consecutive data frames.
read.mtg creates an object of class
foldermtg from an MTG (Multiscale Tree Graph) file containing plant architecture data.
Rachid Boumaza, Pierre Santagostini, Smail Yousfi, Sabine Demotes-Mainard with the contributions from Gilles Hunault, Julie Bourbeillon and Besnik Pumo
Boumaza, R. (1998). Analyse en composantes principales de distributions gaussiennes multidimensionnelles. Revue de Statistique Appliqu<e9>e, XLVI (2), 5-20.
Boumaza, R., Yousfi, S., Demotes-Mainard, S. (2015). Interpreting the principal component analysis of multivariate density functions. Communications in Statistics - Theory and Methods, 44 (16), 3321-3339.
Boumaza, R. (2004). Discriminant analysis with independently repeated multivariate measurements: an L^2 approach. Computational Statistics & Data Analysis, 47, 823-843.
Cokelaer, T. and Pradal, C. (2010). MTG user guide
Delicado, P. (2011). Dimensionality reduction when data are density functions. Computational Statistics & Data Analysis, 55, 401-420.
Rudrauf, J.M., Boumaza, R. (2001). Contribution <e0> l'<e9>tude de l'architecture m<e9>di<e9>vale: les caract<e9>ristiques des pierres <e0> bossage des ch<e2>teaux forts alsaciens. Centre de Recherches Arch<e9>ologiques M<e9>di<e9>vales de Saverne, 5, 5-38.
Yousfi, S., Boumaza, R., Aissani, D., Adjabi, S. (2014). Optimal bandwith matrices in functional principal component analysis of density function. Journal of Statistical Computation and Simulation, 85 (11), 2315-2330.
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