Thank you very much, it works now!
Best regards,
J
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The 2006 CSDA paper is really very informative, perhaps, I'm trying to
understand the things lying beyond. If we have e.g. k=3, then taking nproc=3
for the functional maxBB we get a critical value (boundary)
maxBB$computeCritval(0.05,nproc=3)
[1] 1.544421,
and this for nproc=NULL (Bonferroni
On Fri, 4 Nov 2011, bonda wrote:
The 2006 CSDA paper is really very informative, perhaps, I'm trying to
understand the things lying beyond. If we have e.g. k=3, then taking
nproc=3 for the functional maxBB we get a critical value (boundary)
maxBB$computeCritval(0.05,nproc=3)
[1] 1.544421,
Thank you. I've understood, that it should be k (number of parameters)
separate Brownian bridges.
Is it possible, to get such separated/disaggregated processes also in
function efp()? (one can take gefp(..., family=gaussian), or construct by
myself residuals(lm.model)*X, but still interesting).
On Thu, 3 Nov 2011, bonda wrote:
Thank you. I've understood, that it should be k (number of parameters)
separate Brownian bridges.
Well, if you use a process based on OLS residuals, you have always a
one-dimensional process even though your model has k parameters. Hence,
the two parameters
Hello all,
could anyone explain the exact meaning of parameter nproc? Why different
values of nproc give so different critical values, i.e.
meanL2BB$computeCritval(0.05,nproc=3)
[1] 0.9984853
meanL2BB$computeCritval(0.05,nproc=1)
[1] 0.4594827
The strucchange-package description gives integer
On Wed, 2 Nov 2011, bonda wrote:
Hello all,
could anyone explain the exact meaning of parameter nproc? Why different
values of nproc give so different critical values, i.e.
meanL2BB$computeCritval(0.05,nproc=3)
[1] 0.9984853
meanL2BB$computeCritval(0.05,nproc=1)
[1] 0.4594827
The
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