>>>>> "H" == Hubertus <stonemonkey at web.de>
>>>>> on Wed, 4 Jul 2007 14:58:44 +0200 writes:
H> Dear list,
H> if I do
H> smooth.spline(tmpSec, tmpT, all.knots=T)
H> with the attached data,
Thanks for providing the data via URL (see below)
H> with the attached data, I get this error-message:
H> Error in smooth.spline(tmpSec, tmpT, all.knots = T) :
H> smoothing parameter value too small
H> If I do
H> smooth.spline(tmpSec[-single arbitrary number], tmpT[-single arbitrary
number], all.knots=T)
H> it works!
not quite (see below)
I have transformed your reports into something reproducible
(as *all* such R-help messages should be!):
dFile <- "..../sspl_Hub_data.rda"
## use a file name you can write to
if(file.exists(dFile)) {
load(dFile)
} else {
load(url("http://phi.stonemonkey.org/smoothspline.rda"))
d.tmp <- data.frame(Sec = tmpSec, T = tmpT)
save(d.tmp, file = dFile)
}
str(d.tmp)
## 'data.frame': 2055 obs. of 2 variables:
## $ Sec: num 25743 25744 25745 25746 25747 ...
## $ T : num 288 288 288 288 288 ...
## Dear list,
## if I do
ssT <- with(d.tmp, smooth.spline(Sec, T, all.knots=TRUE))
## with the attached data, I get this error-message:
## Error in smooth.spline(tmpSec, tmpT, all.knots = T) :
## smoothing parameter value too small
## MM: it works "fine" for me on 64-bit, RH5 ("lynne"),
## I but can confirm the problem on my 32-bit Pentium M notebook:
ssT <- with(d.tmp, smooth.spline(Sec, T, all.knots=TRUE,
control.spar = list(trace = TRUE)))
## gives (on nb-mm):
## sbart (ratio = 1.1399039e-11) iterations; initial tol1 = 3.334042e-05 :
## spar GCV b - a e Kind NEW lspar
crit
##
-------------------------------------------------------------------------------
## -0.35410197 0.000220934951 3.0000e+00 0 GS -- 1.61033e-11
0.00027127
## -0.35410197 0.000220934951 1.8541e+00 1.8541 FP GS 8.47468e-20
1.57859e-05
## -0.79179607 1.57858803e-05 1.1459e+00 -1.1459 FP GS 9.41382e-22
3.555e-12
## -1.06230590 3.55500244e-12 7.0820e-01 -0.7082 FP PI 3.86481e-21
2.08386e-10
## -1.06230590 3.55500244e-12 5.2259e-01 -0.27051 FP PI 1.9073e-21
1.13107e-09
## -1.06230590 3.55500244e-12 4.8014e-01 0.084898 FP GS 5.83319e-23
1.04372e-18
## -1.22949017 1.04371872e-18 4.3769e-01 -0.43769 FP PI 2.30006e-22
4.72251e-15
## -1.22949017 1.04371872e-18 3.5298e-01 -0.16718 FP PI 1.1561e-22
2.37808e-16
## -1.22949017 1.04371872e-18 3.1163e-01 0.082471 FP PI 7.90222e-23
2.03459e-15
## -1.22949017 1.04371872e-18 2.8876e-01 0.041121 FP GS 1.0457e-23
2.03459e-15
## >>> 1.0457e-23 1.04372e-18
## Error in smooth.spline(Sec, T, all.knots = TRUE, control.spar = list(trace =
2)) :
## smoothing parameter value too small
## Where it works, I get
ssT
## Call:
## smooth.spline(x = Sec, y = T, all.knots = TRUE)
## Smoothing Parameter spar= -1.044387 lambda= 1.266990e-21 (22 iterations)
## Equivalent Degrees of Freedom (Df): 2054.954
## Penalized Criterion: 4.420969e-20
## OTOH, need a relaxed convergence criterion:
ssT. <- with(d.tmp,
smooth.spline(Sec, T, all.knots=TRUE,
control.spar = list(trace = TRUE,
tol=1e-4, eps= 0.01)))
ssT.
##-> Df: 2055.163 --- interpolation !!!!
##------> really almost fails to do spline *interpolation* as a
## limiting case.
In principle that's well know:
the smoothing spline solution tends to an interpolation spline
when lambda -> 0, however the formula (algorithm) that is used
for smoothing spline fitting( given lambda) is not applicable to
the limiting case, lambda=0.
I agree that this is a small remaining flaw of an already
somewhat sophisticated algorithm for determining the smoothing
parameter.
I'm sure we could fix the algorithm to "work" in this case,
however to do the fix in such a way that all other cases remain
returning the exact same solution as now, seems a harder task.
And changing the default behavior of smooth.spline() even if only
slightly, is quite a bit problematic.
I know it, because I did it many many R versions back...
For you data, see many possibilities;
- don't use all.knots = TRUE
- really use spline interpolation,
library(splines)
?interpSpline
- use 'df = <something reasonable>' if you want to apply it to
many datasets
- don't use 'all.knots = TRUE' or use 'cv=TRUE' or ... :
## What about "proper" crossvalidation instead of GCV (default)
## which I'd recommend anyway on todays fast computers :
ssTg <- with(d.tmp,
smooth.spline(Sec, T, all.knots=TRUE, cv= TRUE,
control.spar = list(trace = TRUE)))
## *does* work (interestingly)
ssTg # hmm, also DF = 2055
plot(T ~ Sec, data = d.tmp, pch = ".")
lines(predict(ssTg), col=2)
## don't use all.knots:
ssTg. <- with(d.tmp,
smooth.spline(Sec, T, cv= TRUE,
control.spar = list(trace = TRUE)))
## *does* work too
ssTg. # DF = 103.8
plot(T ~ Sec, data = d.tmp, pch = ".")
lines(predict(ssTg.), col=2)
sfsmisc::TA.plot(ssTg., labels="o")
sfsmisc::p.ts(residuals(ssTg.))
## hmm, the residuals *do* look like a time-series
acf(residuals(ssTg.))
plot(spectrum(residuals(ssTg.), method = "ar"))
--------------------
Regards,
Maritn Maechler, ETH Zurich
## If I do
## If I do
## smooth.spline(tmpSec[-single arbitrary number], tmpT[-single arbitrary
number], all.knots=T)
## it works!
## MM: *reproducible* code, please!
N <- nrow(d.tmp)
set.seed(1)# or any(?) other
for(n in 1:100) {
i <- sample(N, 1)
cat(sprintf("i =%5d : ", i))
sst.i <- with(d.tmp, smooth.spline(Sec[-i], T[-i], all.knots=TRUE))
cat(" -> df = ", format(sst.i$df),"\n")
}
## what Hubertus says is not true, already the 3rd example
## also gives error:
## i = 546 : -> df = 2054.019
## i = 765 : -> df = 2053.997
## i = 1178 : Error in smooth.spline(Sec[-i], T[-i], all.knots = TRUE) :
## smoothing parameter value too small