Displaying 20 results from an estimated 4000 matches similar to: "A model for possibly periodic data with varying amplitude [repost, much edited]"
2006 Jul 31
0
Three questions about a model for possibly periodic data with varying amplitude
Hi dear R community,
I have up to 12 measures of a protein for each of 6 patients, taken
every two or three days. The pattern of the protein looks periodic,
but the height of the peaks is highly variable. It's something like
this:
patient <- data.frame(
day = c(1, 3, 5, 8, 10, 12, 15, 17, 19, 22, 24, 26),
protein = c(5, 3, 10, 7, 2, 8, 25, 12, 7, 20, 10, 5)
)
plot(patient$day,
2005 Mar 02
1
Using varPower in gnls, an answer of sorts.
Back on January 16, a message on R-help from Ravi Varadhan described a
problem with gnls using weights=varPower(). The problem was that the
fit failed with error
Error in eval(expr, envir, enclos) : Object "." not found
I can reliably get this error in version 2.0.1-patched 2004-12-09 on
Windows XP and 2.0.1-Patched 2005-01-26 on Linux.
The key feature of that example is that the
2010 Jun 09
0
Testing for differences in amplitude and phase
Dear
R-helpers,
I have time
series data from 16 subjects and 2 treatment groups. The seasonal variation can
be best described by two harmonics, I called the frequencies
omega and omega2.
I now want
to test whether (1) the seasonal pattern differs between the treatments (some
kind of overall test). If this is the case, (2) I want to conduct tests to find
out whether the amplitude of the
2011 Feb 08
1
Recuperate Spectrum() amplitude
Dear list,
I apologies first for my English, hope you will understand well my question.
I am working on 1/2 hour piezometric data, time unit is second. They
present daily oscillation when using the spectrum() function. What I am
really interested in, is to find the amplitude corresponding to this
oscillation.
I work with a college using Matlab, and although we apply the same
methodology, our
2005 Jul 26
1
evaluating variance functions in nlme
Hi,
I guess this is a final plea, and maybe this should go to R-help but
here goes.
I am writing a set of functions for calibration and prediction, and to
calculate standard
errors and intervals I need the variance function to be evaluated at new
prediction points.
So for instance
fit<-gnls(Y~SSlogis(foo,Asym,xmid,scal),weights=varPower())
2011 Feb 13
1
calculate phase/amplitude of fourier transform function in R
I did a fourier transform on a function in time domain to get the following
functions in frequency domain (in latex):
$Y_1[\omega] = \frac{1}{1-\phi_1 e^{-jw}}$
$Y_2[\omega] = \frac{1}{1-(\phi_1 + \phi_2)e^{-jw} +\phi_1\phi_2e^{-2jw}}$
How do I find the spectrum of this function for given $\phi_1$ and $\phi_2$
coefficients and in the discretization interval $w = [-\pi:.1*\pi: \pi]$?
Then, how
2008 Jun 10
3
fitting periodic 'sine wave' model
I have been attempting to estimate the periodic contribution of an
effect to some data but have not been able to fit a sine wave within R.
It would be nice to start by being able to fit a sine wave with an
amplitude and frequency.
x<-seq(0,20,by=0.5)
y<-2*sin(2*pi*.5*x) #amplitude =2, frequency=0.5
# This failed to converge
r<-nls(y ~ A*sin(2*pi*F*x), start=list(A = 1, F = 1),
2008 Feb 25
0
logLik calculation in gls (nlme)
I'm getting some odd results computing log-likelihoods
with gls using splines with increasing degrees of freedom --
the deviance *increases* substantially with increasing df.
(Since spline models with increasing df aren't nested, it
need not decline monotonically but I would expect it to
have a decreasing trend!)
I may just be confused, but I *think* the issue is somewhere
within the
2004 Jan 14
2
Generalized least squares using "gnls" function
Hi:
I have data from an assay in the form of two vectors, one is response
and the other is a predictor. When I attempt to fit a 5 parameter
logistic model with "nls", I get converged parameter estimates. I also
get the same answers with "gnls" without specifying the "weights"
argument.
However, when I attempt to use the "gnls" function and try to
2006 Jan 24
1
spec.pgram() normalized too what?
Dear list,
What on earth is spec.pgram() normalized too? If you would like to skip my
proof as to why it's not normed too the mean squared or sum squared
amplitude of the discrete function a[], feel free too skip the rest of the
message. If it is, but you know why it's not exact in spec.pgram() when it
should be, skip the rest of this message. The issue I refer herein refers
only too a
2012 Sep 19
2
Warning Message: In if (deparse(params[[nm]][[3]]) != "1")
I am using the gnls procedure in nlme package to fit a nonlinear model as:
nl.fit<-gnls(Y ~ b0*exp(b1/X),
data = data1,
params=list(
b0~p1+I(p1^2)+p2+I(p2^2)+p3+I(p3^2)+p5+p6
b1~p8+p2+I(p2^2)+p3+p9+p10+p11),
start = c(25,0,0,0,0,0,0,0,0,-8.6,0,0,0,0,0,0,0),
weights=varPower(form =~ X)
2009 Oct 30
0
Interpreting gnls() output in comparison to nls()
Hi,
I've been trying to work with the gnls() function in the "nlme" package. My
decision to use gnls() was so that I could fit varPower and such to some of
the data. However, in working with a small dataset, I've found that the
results given by gnls() don't seem to make any sense and they differ
substantially from those produced by nls(). I suspect that I am just
2008 Dec 12
0
Is there anyone in charge of package wmtsa ?
Here is another occurrence of wmTSA internal error.
My time series is a short breathing cycle (2425-Cyle_9.txt).
Since wmtsa functions that extract extrema seem to expect longer series than I have, I tried the folowing two tricks:
1) I prolong the 1-cycle series on both ends through duplicating the first value (on the left end( and the last value on the right end as any ties as the
1-cycle
2006 Feb 17
0
trouble with extraction/interpretation of variance struct ure para meters from a model built using gnls and varConstPower
Works perfectly. Thank you.
-Hugh Rand
-----Original Message-----
From: Spencer Graves [mailto:spencer.graves at pdf.com]
Sent: Sunday, January 15, 2006 6:41 PM
To: Rand, Hugh
Cc: 'r-help at lists.R-project.org'
Subject: Re: [R] trouble with extraction/interpretation of variance
structure para meters from a model built using gnls and varConstPower
How about this:
>
2017 Jun 21
1
fitting cosine curve
Using a more stable nonlinear modeling tool will also help, but key is to get
the periodicity right.
y=c(16.82, 16.72, 16.63, 16.47, 16.84, 16.25, 16.15, 16.83, 17.41, 17.67,
17.62, 17.81, 17.91, 17.85, 17.70, 17.67, 17.45, 17.58, 16.99, 17.10)
t=c(7, 37, 58, 79, 96, 110, 114, 127, 146, 156, 161, 169, 176, 182,
190, 197, 209, 218, 232, 240)
lidata <- data.frame(y=y, t=t)
#I use the
2007 Oct 17
2
nmle: gnls freezes on difficult case
Hi,
I am not sure this is a bug but I can repeat it, The functions and data
are below.
I know this is nasty data, and it is very questionable whether a 4pl
model
is appropriate, but it is data fed to an automated tool and I would
have hoped for an error. Does this repeat for anyone else?
My details:
> version
_
platform i686-pc-linux-gnu
1999 Nov 25
1
gnls
Doug,
I have been attempting to learn a little bit about nlme without too
much documentation except the online help. The Latex file in the nlme
directory looks interesting but uses packages that I do not have so
that I have not been able to read it.
I have run the example from gnls to compare it with the results I
get from my libraries (code below - I have not included output as it
is rather
2010 Dec 02
0
24 bit question
Thanks for the replies!
My first thought was that the file had low levels (before he sent me the
file), but that's definitely not the case with this file. There are many
peaks that reach 0dBFS.
He sent me the original wav this morning and I loaded it into Wave Editor on
OS X. I dithered to 16 bit using MBIT+ (high/ultra setting) and saved the
16 bit file. I did nothing else (no
2002 Sep 11
1
lme with/without varPower - can I use AIC?
I want to compare the following two models in AIC
(Treat, Spotter are categorial, p is pressure, Pain is
continuous)
PainW.lme<-lme(Pain~p+Treat*Spotter,data=saw,random=~p|Pat,
weights=varPower(form=~Pain))
# AIC= -448
Pain.lme<-lme(Pain~p+Treat*Spotter,data=saw,random=~p|Pat)
#AIC = -19.7
Note the huge differences in AIC, and the estimated power of 6.
A plot of the residual
2004 Oct 18
3
manual recreation of varConstPower using new fixed effects variables in nlme
Hello, I am trying to design new variance structures
by using fixed effects variables in combination with
the VarPower function. That is, I would like to
create and evaluate my own variance function in the
data frame and then incorporate it into the model
using varPower, with value=.5.
As a start, I am trying to recreate the function of
VarConstPower by introducing two new variables in the