Dear R-helpers,
I compared various programs for cubic spline smoothing, and it appeared
that smooth.spline ( stats version 3.0.1) seems to behave surprisingly.
For enough long series and low values of lambda (or spar), the results
of smooth.spline seem to be different from those of sreg ( package
fields version 6.8), Octave (=MATLAB) or SAS. These three last softwares
always gave the same results.
Here is a script which shows the problem:
#generate a random series of 2000 values
set.seed(1)
MyData=data.frame(Time=1:2000,Val=runif(1000))
#calculate the sreg cubic smoothing spline with a given lambda parameter
(0.006 here)
library(fields)
SplineFields=sreg(MyData$Time,MyData$Val,lambda=0.006)
#keep the minimim fitted value (or any other from a long list of
possible values)
ValMin=min(SplineFields$fitted.values)
TimeValMin=which.min(SplineFields$fitted.values)
#calculations of all possible fitted values at the TimeValMin point with
smooth.spline,
#varying the spar parameter in the range of all its possible values
SplineRValMin=sapply(seq(-0.5,2.5,0.1),
function(Ispar) {
SplineR=smooth.spline(MyData$Time,MyData$Val,spar=Ispar)
SplineR$y[TimeValMin]})
#None of the smooth.spline fitted values reach the one calculated with
sreg !
Lim=range(ValMin,SplineRValMin)
#smooth.spline values
plot(seq(-0.5,2.5,0.1),SplineRValMin,type="l",ylim=Lim)
#sreg value
abline(h=ValMin)
I hope there is no real problem here, but only some misunderstanding
from my side, because cubic splines are very often used. Best regards,
Jean-Luc Dupouey
--
INRA-Nancy University
Forest Ecology and Ecophysiology Unit
F-54280 Champenoux
mail:dupo...@nancy.inra.fr
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