Hi Julian,
Any chance you could send me (offline) a short version of your data,
which reproduces the problem? I can't reproduce it in a quick attempt
(but it is quite puzzling, given that bam calls predict.gam internally
in pretty much the same way that you are doing here).
btw (and nothing to do with the error) given that you are using R 3.0.1
it's a good idea to upgrade to mgcv_1.7-23 or above, for the following
reason (taken from the mgcv changeLog)
1.7-23
------
*** Fix of severe bug introduced with R 2.15.2 LAPACK change. The
shipped version of dsyevr can fail to produce orthogonal eigenvectors
when uplo='U' (upper triangle of symmetric matrix used), as opposed to
'L'. This led to a substantial number of gam smoothing parameter
estimation convergence failures, as the key stabilizing
re-parameterization was substantially degraded. The issue did not affect
gaussian additive models with GCV model selection. Other models could
fail to converge any further as soon as any smoothing parameter became
`large', as happens when a smooth is estimated as a straight line.
check.gam reported the lack of full convergence, but the issue could
also generate complete fit failures. Picked up late as full test suite
had only been run on R > 2.15.1 with an external LAPACK.
best,
Simon
On 08/07/13 10:02, julian.bo...@elitepartner.de wrote:
Hello everyone.
I am doing a logistic gam (package mgcv) on a pretty large dataframe
(130.000 cases with 100 variables).
Because of that, the gam is fitted on a random subset of 10000. Now when I
want to predict the values for the rest of the data, I get the following
error:
gam.basis_alleakti.1.pr=predict(gam.basis_alleakti.1,
+
newdata=activisale_join[gam.basis_alleakti.1.complete_cases,all.vars(gam.b
asis_alleakti.1.formula)],type="response")
Error in predict.gam(gam.basis_alleakti.1, newdata =
activisale_join[gam.basis_alleakti.1.complete_cases, :
number of items to replace is not a multiple of replacement length
The following is the code:
#formula with some factors and a lot of variables to be fitted
gam.basis_alleakti.1.formula=as.formula( paste("verlängerung ~“,
paste( names(activisale_join)[c(2:10)], collapse="+"), ##factors
paste("s(",names(activisale_join)[c(17,19:29,31:42,44)],")",
collapse="+")) # numeric variables, all count data
)
# complete cases
gam.basis_alleakti.1.complete_cases =
complete.cases(activisale_join[,all.vars(gam.basis_alleakti.1.formula) ])
# modell fitting works on random subset
gam.basis_alleakti.1=bam(gam.basis_alleakti.1.formula,
data = activisale_join[subset.10000, ], family=
"binomial")
# error, no idea why
gam.basis_alleakti.1.pr=predict(gam.basis_alleakti.1,
newdata=activisale_join[gam.basis_alleakti.1.complete_cases,
],type="response")
the prediction on the same subset (subset.10000) works.
It could be that this error is somewhat similar to that described as
sidequestion in
http://r.789695.n4.nabble.com/gamm-tensor-product-and-interaction-td452618
8.html, where simon answered the following:
“> Here is the error message I obtain:
vis.gam(gm1$gam,plot.type="contour",n.grid=200,color="heat",zlim=c(0,4))
Error in predict.gam(x, newdata = newd, se.fit = TRUE, type = type) :
number of items to replace is not a multiple of replacement length
- hmm, possibly a bug. I'll look into it.
best,
Simon“
All the best
Julian
Ps.: > version
_
platform x86_64-w64-mingw32
arch x86_64
os mingw32
system x86_64, mingw32
status
major 3
minor 0.1
year 2013
month 05
day 16
svn rev 62743
language R
version.string R version 3.0.1 (2013-05-16)
nickname Good Sport
package mgcv version 1.7-22
[[alternative HTML version deleted]]
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--
Simon Wood, Mathematical Science, University of Bath BA2 7AY UK
+44 (0)1225 386603 http://people.bath.ac.uk/sw283
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