On 13-07-27 2:50 PM, Jean-Luc Dupouey wrote:
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.
Did you read the ?smooth.spline help page, in particular the Details and
Note sections? They indicate that the default computation makes some
efficiency simplifications.
Duncan Murdoch
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
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