Your question is OFFTOPIC for this list. Post on a statistics list
like stats.stackexchange.com .
But both your proposals are wrong, though depending on your data and
purpose, they may be adequate. I suggest you consult wit a local
statistician on the use of mixed effects models for repeated
measu
Thanks, I think you're right. I removed the strains whose final OD was
below 0.2 since all the ones that clearly grew are above that, and
grofit produces fewer errors on the remaining 6. The error still happens
occasionally, but if I stick to 1000 bootstraps instead of 1 it's
not often. Of cour
1. Very likely, you have insufficient data in some of your growth
curves to do the fits using gcv. If you remove the curves where the
bacteria didn't grow, things should work. Alternatively, there may
well be ways of expressing the model that would allow pooling across
cultures that didn't grow. (
I'm trying to use the grofit package to compare growth rates between
bacterial cultures, but I've come across a couple glitches/things I
don't understand. I'm not sure if they're related to the package or to a
problem with my growth data, which is messy. Some strains don't follow
a proper logarithm
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 see
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 ( packa
On 15/02/2012 15:00, Nicholas Reich wrote:
Hello.
I'm getting an unexpected result when running smooth.spline(). Here
is a simple example that replicates the error I'm getting:
aa<- c(1, 2, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 12, 13, 14) bb<-
1:length(aa) plot(aa, bb) smooth.spline(aa, bb)
Error i
Hello.
I'm getting an unexpected result when running smooth.spline(). Here is a
simple example that replicates the error I'm getting:
> aa <- c(1, 2, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 12, 13, 14)
> bb <- 1:length(aa)
> plot(aa, bb)
> smooth.spline(aa, bb)
Error in smooth.spline(aa, bb) : need at l
Hi, all, I found that the smooth.spline() function produces different results
between R and S-Plus. I was trying to play different parameters of the function
without any success. The script of the function contains Fortran code, so it
seems impossible to port the code from S-Plus to R (or I may
Actually that was my next question. From the books that I have I see a "natural
spline" and a clamped spline. I am assuming that "natural" (Umerical Analysis,
Burden, et. all.) cooresponds to 'R''s "natural" method. I am not clear on what
a clamped spline cooresponds to (fmm or perodic). Or what
Duncan Murdoch wrote:
On 20/07/2008 11:11 AM, Spencer Graves wrote:
Are you aware that there are many different kinds of splines?
With "spline" and "splinefun", you can use method = "fmm" (Forsyth,
Malcolm and Moler), "natural", or "periodic". I'm not familiar with
"fmm", but it see
On 20/07/2008 11:11 AM, Spencer Graves wrote:
Are you aware that there are many different kinds of splines?
With "spline" and "splinefun", you can use method = "fmm" (Forsyth,
Malcolm and Moler), "natural", or "periodic". I'm not familiar with
"fmm", but it seems to be adequately explai
Are you aware that there are many different kinds of splines?
With "spline" and "splinefun", you can use method = "fmm" (Forsyth,
Malcolm and Moler), "natural", or "periodic". I'm not familiar with
"fmm", but it seems to be adequately explained by the "Manual spline
evaluation" you quote
On Fri, Jul 18, 2008 at 10:15 PM, Roland Rau <[EMAIL PROTECTED]> wrote:
> Spencer Graves wrote:
>>
>> I found the first chapter of Paul Dierckx (1993) Curve and Surface Fitting
>> with Splines (Oxford U. Pr.). Beyond that, I've learned a lot from the
>> 'fda' package and the two companion volumes
Fair enough. FOr a spline interpolation I can do the following:
> n <- 9
> x <- 1:n
> y <- rnorm(n)
> plot(x, y, main = paste("spline[fun](.) through", n, "points"))
> lines(spline(x, y))
Then look at the coefficients generated as:
> f <- splinefun(x, y)
> ls(envir = environment(f))
[1] "ties" "
PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html and provide commented,
minimal, self-contained, reproducible code.
I do NOT know how to do what you want, but with a self-contained
example, I suspect many people on this list -- probably including me --
co
Spencer Graves wrote:
I found the first chapter of Paul Dierckx (1993) Curve and Surface
Fitting with Splines (Oxford U. Pr.). Beyond that, I've learned a lot
from the 'fda' package and the two companion volumes by Ramsay and
Silverman (2006) Functional Data Analysis, 2nd ed. and (2002) Applie
I believe that a short answer to your question is that the
"smooth" is a linear combination of B-spline basis functions, and the
coefficients are the weights assigned to the different B-splines in that
basis.
Before offering a much longer answer, I would want to know what
problem yo
I like what smooth.spline does but I am unclear on the output. I can see from
the documentation that there are fit.coef but I am unclear what those
coeficients are applied to.With spline I understand the "noraml" coefficients
applied to a cubic polynomial. But these coefficients I am not sure ho
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