Generally nlsr package has better reliability in getting parameter estimates
because it tries to use automatic derivatives rather than a rather poor
numerical
estimate, and also uses a Levenberg-Marquardt stabilization of the linearized
model. However, nls() can sometimes be a bit more flexible.
Thanks Jeff
Bernard
Sent from my iPhone so please excuse the spelling!"
> On Apr 5, 2020, at 3:14 PM, Jeff Newmiller wrote:
>
> stats::nlm?
>
>> On April 5, 2020 11:53:10 AM PDT, Bernard Comcast
>> wrote:
>> Any recommendations on an R package to fit data to a nonlinear model
>> Y=f(x)
err... stats::nls...
On April 5, 2020 12:14:15 PM PDT, Jeff Newmiller
wrote:
>stats::nlm?
>
>On April 5, 2020 11:53:10 AM PDT, Bernard Comcast
> wrote:
>>Any recommendations on an R package to fit data to a nonlinear model
>>Y=f(x) with a single x and y variable?
>>
>>I want to be able to
stats::nlm?
On April 5, 2020 11:53:10 AM PDT, Bernard Comcast
wrote:
>Any recommendations on an R package to fit data to a nonlinear model
>Y=f(x) with a single x and y variable?
>
>I want to be able to generate parameter uncertainty estimates and
>prediction uncertainties if possible.
>
Any recommendations on an R package to fit data to a nonlinear model Y=f(x)
with a single x and y variable?
I want to be able to generate parameter uncertainty estimates and prediction
uncertainties if possible.
Bernard
Sent from my iPhone so please excuse the spelling!"
Hi Waltenegus,
you should consider to show us your code and the data you used to fit the
curve. If you can't or if you prefer you can simply show the code and some
sample data on which the code can be run.
You'll find nice and useful tool in nlstools package.
Hope this help
Giuseppe
-
You could use the summation convention built into mgcv:gam for this. See
?linear.functional.terms
for details, but here is some example code, both for the exact match,
you describe, and a noisy version. best, Simon
library(mgcv)
f2 - function(x) 0.2*x^11*(10*(1-x))^6+10*(10*x)^3*(1-x)^10 ##
This sounds like possibly using logsplines may be what you want. See
the 'oldlogspline' function in the 'logspline' package.
On Thu, Apr 12, 2012 at 7:45 AM, Michael Haenlein
haenl...@escpeurope.eu wrote:
Dear all,
This is probably more related to statistics than to [R] but I hope someone
Dear all,
This is probably more related to statistics than to [R] but I hope someone
can give me an idea how to solve it nevertheless:
Assume I have a variable y that is a function of x: y=f(x). I know the
average value of y for different intervals of x. For example, I know that
in the
-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-
project.org] On Behalf Of Kyeong Soo (Joseph) Kim
Sent: Friday, April 30, 2010 4:10 AM
To: kMan
Cc: r-help@r-project.org
Subject: Re: [R] Curve Fitting/Regression with Multiple Observations
[snip]
By the way, I
]
Sent: Tuesday, April 27, 2010 2:33 PM
To: Gabor Grothendieck
Cc: r-help@r-project.org
Subject: Re: [R] Curve Fitting/Regression with Multiple Observations
Frankly speaking, I am not looking for such a framework.
The system I'm studying is a communication network (like M/M/1 queue, but way
too
-project.org
Subject: Re: [R] Curve Fitting/Regression with Multiple Observations
Frankly speaking, I am not looking for such a framework.
The system I'm studying is a communication network (like
M/M/1 queue, but way too complicated to mathematically
analyze it using classical queueing
.
-Original Message-
From: Kyeong Soo (Joseph) Kim [mailto:kyeongsoo@gmail.com]
Sent: Friday, April 30, 2010 4:10 AM
To: kMan
Cc: r-help@r-project.org
Subject: Re: [R] Curve Fitting/Regression with Multiple Observations
Dear Keith,
Thanks for the suggestion and taking your time to respond
-help@r-project.org
Subject: Re: [R] Curve Fitting/Regression with Multiple Observations
Frankly speaking, I am not looking for such a framework.
The system I'm studying is a communication network (like
M/M/1 queue, but way too complicated to mathematically
analyze it using classical
30, 2010 4:10 AM
To: kMan
Cc: r-help@r-project.org
Subject: Re: [R] Curve Fitting/Regression with Multiple Observations
Dear Keith,
Thanks for the suggestion and taking your time to respond to it.
But, you misunderstand something and seems that you do not read all my
previous e-mails
I am having troubles in fitting functions of the form
y~a*x^b+c
to data, for example
x-c(0.1,0.36,0.63,0.90,1.166,1.43, 1.70, 1.96, 2.23)
y-c(8.09,9.0,9.62,10.11,10.53,10.9, 11.25, 11.56, 11.86)
I tried for example with nls, which did only work with really good initial
guessed values.
Any
-help@r-project.org
Date:
04/30/2010 03:33 PM
Subject:
[R] Curve Fitting
Sent by:
r-help-boun...@r-project.org
I am having troubles in fitting functions of the form
y~a*x^b+c
to data, for example
x-c(0.1,0.36,0.63,0.90,1.166,1.43, 1.70, 1.96, 2.23)
y-c(8.09,9.0,9.62,10.11,10.53,10.9, 11.25
You can use nls2 to try many starting values. It works just like nls but:
- if you give it a two row data frame as the start value it will
create a grid between the upper and lower values of each parameter and
then run an optimization starting at each such point on the grid
returning the best
-
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-
project.org] On Behalf Of Kyeong Soo (Joseph) Kim
Sent: Friday, April 30, 2010 4:10 AM
To: kMan
Cc: r-help@r-project.org
Subject: Re: [R] Curve Fitting/Regression with Multiple Observations
[snip
-boun...@r-
project.org] On Behalf Of Kyeong Soo (Joseph) Kim
Sent: Friday, April 30, 2010 4:10 AM
To: kMan
Cc: r-help@r-project.org
Subject: Re: [R] Curve Fitting/Regression with Multiple Observations
[snip]
By the way, I wonder why most of the responses I've received from this
list are so
Message-
From: Kyeong Soo (Joseph) Kim [mailto:kyeongsoo@gmail.com]
Sent: Friday, April 30, 2010 5:24 PM
To: Greg Snow
Cc: r-help@r-project.org
Subject: Re: [R] Curve Fitting/Regression with Multiple Observations
I have already learned a lot from the list, both technical
-
From: Kyeong Soo (Joseph) Kim [mailto:kyeongsoo@gmail.com]
Sent: Tuesday, April 27, 2010 2:33 PM
To: Gabor Grothendieck
Cc: r-help@r-project.org
Subject: Re: [R] Curve Fitting/Regression with Multiple Observations
Frankly speaking, I am not looking for such a framework.
The system I'm
I recently came to realize the true power of R for statistical
analysis -- mainly for post-processing of data from large-scale
simulations -- and have been converting many of existing Python(SciPy)
scripts to those based on R and/or Perl.
In the middle of this conversion, I revisited the problem
-project.org] On
Behalf Of Kyeong Soo (Joseph) Kim
Sent: Tuesday, April 27, 2010 10:31 AM
To: r-help@r-project.org
Subject: [R] Curve Fitting/Regression with Multiple Observations
I recently came to realize the true power of R for statistical
analysis -- mainly for post-processing of data from large
This will compute a loess curve and plot it:
example(loess)
plot(dist ~ speed, cars, pch = 20)
lines(cars$speed, fitted(cars.lo))
Also this directly plots it but does not give you the values of the
curve separately:
library(lattice)
xyplot(dist ~ speed, cars, type = c(p, smooth))
On Tue, Apr
Hello Gabor,
Many thanks for providing actual examples for the problem!
In fact I know how to apply and generate plots using various R
functions including loess, lowess, and smooth.spline procedures.
My question, however, is whether applying those procedures directly on
the data with multiple
If you are looking for a framework for statistical inference you could
look at additive models as in the mgcv package which has a book
associated with it if you need more info. e.g.
library(mgcv)
fm - gam(dist ~ s(speed), data = cars)
summary(fm)
plot(dist ~ speed, cars, pch = 20)
fm.ci -
Frankly speaking, I am not looking for such a framework.
The system I'm studying is a communication network (like M/M/1 queue,
but way too complicated to mathematically analyze it using classical
queueing theory) and the conclusion I want to make is qualitative
rather than quantatitive -- a
Hi to all
This is the first time I am quoting a question and I hope, my
question is not too basic...
For the following data, I wish to draw a fitted curve.
x - c(123,129,141,144,144,145,149,150,158,159,163,174,183,187,242,248)
y -
A simple y vs log(x) fit seems to work pretty well here:
fit - lm(y ~ log(x))
summary(fit)
plot(y ~ log(x))
abline(fit)
On Fri, Dec 4, 2009 at 9:06 AM, Pascale Weber pascale.we...@wsl.ch wrote:
Hi to all
This is the first time I am quoting a question and I hope, my question is
not too
Pascale,
If you do want an nls fit with the associated error structure
assumptions, check ?SSlogis.
fm - nls(y ~ SSlogis(x, Asy, xmid, scal))
summary(fm)
xx - seq(123, 248, length = 101)
yy - predict(fm, list(x = xx))
plot(x, y)
lines(xx, yy)
-Peter Ehlers
Gabor Grothendieck wrote:
A
I have the data:
for x: 2, 8, 14, 20, 26, 32, 38, 44, 50, 56, 62, 68, 74,
for y: 100, 99, 99, 98, 97, 94, 82, 66, 48, 38, 22, 10, 1.
y depends on x by equation: y = 100*exp(b*(1-exp(c*x))/c),
where b and c are coefficients. I need to find coefficients in this
equation for given data. How can I do
Dear Dmitry,
Take a look at ?nls and its examples.
HTH,
Jorge
On Tue, May 12, 2009 at 5:44 PM, Dmitry Gospodaryov
gospodar...@rambler.ruwrote:
I have the data:
for x: 2, 8, 14, 20, 26, 32, 38, 44, 50, 56, 62, 68, 74,
for y: 100, 99, 99, 98, 97, 94, 82, 66, 48, 38, 22, 10, 1.
y depends on
or use nls.lm as in
install.packages(minpack.lm)
library(minpack.lm)
x - c(2, 8, 14, 20, 26, 32, 38, 44, 50, 56, 62, 68, 74)
y - c(100, 99, 99, 98, 97, 94, 82, 66, 48, 38, 22, 10, 1)
res - function(p, x, y) y - ff(p,x)
ff - function(p, x) 100*exp(p[1]*(1-exp(p[2]*x))/p[2])
aa -
Hi Thanks a lot,
I think you have covered the things I want to do for now so I will try to
implement them as soon I can.
A finite Fourier series could be the best tool IF the the multiple
periodicities are all integer fractions of a common scale.
This is certainly true for my repetitive
Dear Dr Gkikopoulos:
1. Have you looked at bioconductor.org? They have substantive
extensions to R specifically for genomic data, which I assume would
include chromosome.
2. To identify periodicities at different timescales, I agree
with Stephen that spectrum would likely
There are a couple of different goals for this projects
*identify periodicities at different timescales (ie different dT)
*fit data into discrete number of curves, ie 6 different basic functions
should be enough to describe the basic repeating elements in this data (ie 6
different categories
Dear all,
Another newbie just got attracted to this mailing list.
I am a biologist currently working my way through R, had sort play around with
python earlier this year.
I have some data exhibiting periodicity ** my data consists of peaks and
valleys, with peaks arising due to the presence
Here is the gif that didn't come through earlier
http://www.nabble.com/file/p22870832/signal.gif signal.gif
--
View this message in context:
http://www.nabble.com/Curve-fitting%2CFDA-for-biological-data-tp22868069p22870832.html
Sent from the R help mailing list archive at Nabble.com.
What is your end goal? If it is to try and account for the
variability of the timeseries you may want to look at ?spectrum
If it is to model the periodicity...
Stephen Sefick
On Fri, Apr 3, 2009 at 11:30 AM, trias t.gkikopou...@dundee.ac.uk wrote:
Here is the gif that didn't come through
hello,
I want to fit a curve to a simple x,y dataset - my problem is, that I
want to fit it for the following term:
n(1-e^x/y) - so I get the n constant for my data...
can anyone help/comment on that?
cheers,
gregor
__
R-help@r-project.org mailing
gregor rolshausen wrote:
hello,
I want to fit a curve to a simple x,y dataset - my problem is, that I
want to fit it for the following term:
n(1-e^x/y) - so I get the n constant for my data...
Not an R problem in the first place, but the question arises what
n(1-e^x/y) means, its is just
ok. sorry for being blurry.
I have x,y data, that probably fits a asymptotic curve (asymptote at N).
now I want to fit a curve onto the data, that gives me the N. therefore
I thought to fit an e-function, namely N(1-e^(y/x)) onto the data and
get the N from the fitted curves' equation.
in the
gregor rolshausen gregor.rolshausen at biologie.uni-freiburg.de writes:
ok. sorry for being blurry.
I have x,y data, that probably fits a asymptotic curve (asymptote at N).
now I want to fit a curve onto the data, that gives me the N. therefore
I thought to fit an e-function, namely
I'm trying to fit a function y=k*l^(m*x) to some data points, with reasonable
starting value estimates (I think). I keep getting singular matrix 'a' in
solve.
This is the code:
ox - c(-600,-300,-200,1,100,200)
ir - c(1,2.5,4,9,14,20)
model - nls(ir ~
Your model is singular. Varying m and log(l) have the same
effect: log(ir) = log(k) + m * log(l) * ox
Also with plinear you don't specify the linear coefficients but
rather an X matrix whose coefficients represent them:
If we use this model instead:
ir = k * exp(m * ox)
Then:
mod0 -
46 matches
Mail list logo
