Dear Reader, 
You will find in the associated text files ("mpdp-commandlinesR.txt", "mpdp-routinesR.txt" and "flr-routinesR.txt")
 the materials needed for implementing the statistical methodology detailed in the paper entitled 

"Most predictive design points for functional data predictors" and co-authored by Frederic Ferraty, Peter Hall,
 Philippe Vieu (to appear in Biometrika). 

The file "mpdp-routinesR.txt" contains R routines:
1) "mpdp" ==> stepwise algorithm (forward selection + backward deletion) allowing the selection of the 
most predicitve design points
2) "predict.mpdp" ==> allows to predict responses from an object of class "mpdp"
3) "quadratic2" ==> asymmetrical quadratic kernel

The file "flr-routinesR.txt" contains R routines ("Splinemlf", "Bspline.ini", "approx.spline.deriv", 
"interp.spline.deriv") allowing the implementation of the alternative functional linear regression 
method (in order to compare with)

The file "mpdp-commandlinesR.txt" contains all R commandlines allowing to implement the stepwise algorithm 
(i.e. selection of most predictive design points) on simulations and real datasets. 

How to use this material? 
1) open an R session 
2) follow the instructions given in the file "mpdp-commandlinesR.txt" (copy-paste the R commandlines into your R session) 

IMPORTANT REMARK: this is an intensive computational method; the computational labour may become very huge 
with large dataset (one run with chopped pork dataset - 159 curves with 100 design points - lasts about 
5 minutes on a standard personal computer; one run with orange juice dataset - 149 curves with 700 design points - 
lasts about 30 minutes). 


All this material is freely downloadable from the NPFDA (NonParametric Functional Data Analysis) website 
"http://www.math.univ-toulouse.fr/staph/npfda" which contains materials for implementing various nonparametric 
statistical methods for functional data analysis. Use for any commercial purpose is forbidden.