Re: [R] Curve fitting

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.

Re: [R] Curve fitting

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)

Re: [R] Curve fitting

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

Re: [R] Curve fitting

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. >

[R] Curve fitting

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!"

Re: [R] curve() doesn't seem to use the whole range of x? And Error: longer object length is not a multiple of shorter object length

I think you are going to have to be more specific than "having some trouble". Your plot used lka as the x-axis. FWIW note that lm(ruotsi.pist ~ mies + koulu + clka + koulu*clka, data=dta) is the same as lm(ruotsi.pist ~ mies + koulu*clka, data=dta) -- Sent from my phone. Please excuse my

Re: [R] curve() doesn't seem to use the whole range of x? And Error: longer object length is not a multiple of shorter object length

If your goal is to visualize the predicted curve from an lm fit (or other model fit) then you may want to look at the Predict.Plot and TkPredict functions from the TeachingDemos package. On Sun, Sep 25, 2016 at 7:01 AM, Matti Viljamaa wrote: > I’m trying to plot regression

Re: [R] curve() doesn't seem to use the whole range of x? And Error: longer object length is not a multiple of shorter object length

> On 26 Sep 2016, at 19:41, Matti Viljamaa wrote: > > Thank you. > > However, I’m having some trouble converting your code to use clka, because > the model I was using was: > > fit2 <- lm(ruotsi.pist ~ mies + koulu + clka + koulu*clka, data=dta) I mean, not to use clka

Re: [R] curve() doesn't seem to use the whole range of x? And Error: longer object length is not a multiple of shorter object length

Thank you. However, I’m having some trouble converting your code to use clka, because the model I was using was: fit2 <- lm(ruotsi.pist ~ mies + koulu + clka + koulu*clka, data=dta) > On 25 Sep 2016, at 21:23, Jeff Newmiller wrote: > > This illustrates why you need

Re: [R] curve() doesn't seem to use the whole range of x? And Error: longer object length is not a multiple of shorter object length

This illustrates why you need to post a reproducible example. You have a number of confounding factors in your code. First, "data" is a commonly-used function... avoid using it for variable names. Second, using the attach function this way leads to confusion... best to forget this function

Re: [R] curve() doesn't seem to use the whole range of x? And Error: longer object length is not a multiple of shorter object length

> On 25 Sep 2016, at 19:37, Matti Viljamaa wrote: > > Okay here’s a pretty short code to reproduce it: > > data <- > read.table("http://users.jyu.fi/~slahola/files/glm1_datoja/yoruotsi.txt;, > header=TRUE) data$clka <- I(data$lka - mean(data$lka)) > attach(data) > >

Re: [R] curve() doesn't seem to use the whole range of x? And Error: longer object length is not a multiple of shorter object length

Object clka not found. Did you test run it in a fresh R environment? -- Sent from my phone. Please excuse my brevity. On September 25, 2016 9:37:56 AM PDT, Matti Viljamaa wrote: >Okay here’s a pretty short code to reproduce it: > >data <-

Re: [R] curve() doesn't seem to use the whole range of x? And Error: longer object length is not a multiple of shorter object length

Okay here’s a pretty short code to reproduce it: data <- read.table("http://users.jyu.fi/~slahola/files/glm1_datoja/yoruotsi.txt;, header=TRUE) attach(data) fit2 <- lm(ruotsi.pist ~ mies + koulu + lka + koulu*clka) bs <- coef(fit2) varitB <- c(data[koulu == 'B',]$mies) varitB[varitB == 0] =

Re: [R] curve() doesn't seem to use the whole range of x? And Error: longer object length is not a multiple of shorter object length

Go directly to C. Do not pass go, do not collect $200. You think curve does something, but you are missing what it actually does. Since you don't seem to be learning from reading ?curve or from our responses, you need to give us an example you can learn from. -- Sent from my phone. Please

Re: [R] curve() doesn't seem to use the whole range of x? And Error: longer object length is not a multiple of shorter object length

On 2016-09-25 18:52, Jeff Newmiller wrote: You seem to be confused about what curve is doing vs. what you are doing. But my x-range in curve()'s parameters from and to should be the entire lka vector, since they are from=min(lka) and to=max(lka). Then why does this not span the entire of

Re: [R] curve() doesn't seem to use the whole range of x? And Error: longer object length is not a multiple of shorter object length

You seem to be confused about what curve is doing vs. what you are doing. A) Compute the points you want to plot and put them into 2 vectors. Then figure out how to plot those vectors. Then (perhaps) consider putting that all into one line of code again. B) The predict function is the

Re: [R] curve() doesn't seem to use the whole range of x? And Error: longer object length is not a multiple of shorter object length

On 2016-09-25 18:30, Duncan Murdoch wrote: On 25/09/2016 9:10 AM, Matti Viljamaa wrote: Writing: bs["(Intercept)"]+bs["mies"]*0+bs["kouluB"]+bs["lka"]*lka+bs["kouluB:clka"]*clka i.e. without that being inside curve produces a vector of length 375. So now it seems that curve() is really

Re: [R] curve() doesn't seem to use the whole range of x? And Error: longer object length is not a multiple of shorter object length

On 25/09/2016 9:10 AM, Matti Viljamaa wrote: Writing: bs["(Intercept)"]+bs["mies"]*0+bs["kouluB"]+bs["lka"]*lka+bs["kouluB:clka"]*clka i.e. without that being inside curve produces a vector of length 375. So now it seems that curve() is really skipping some lka-/x-values. How could curve()

Re: [R] curve() doesn't seem to use the whole range of x? And Error: longer object length is not a multiple of shorter object length

Writing: bs["(Intercept)"]+bs["mies"]*0+bs["kouluB"]+bs["lka"]*lka+bs["kouluB:clka"]*clka i.e. without that being inside curve produces a vector of length 375. So now it seems that curve() is really skipping some lka-/x-values. > On 25 Sep 2016, at 16:01, Matti Viljamaa

[R] curve() doesn't seem to use the whole range of x? And Error: longer object length is not a multiple of shorter object length

I’m trying to plot regression lines using curve() The way I do it is: bs <- coef(fit2) and then for example: curve(bs["(Intercept)"]+bs["mies"]*0+bs["kouluB"]+bs["lka"]*x+bs["kouluB:clka"]*clka, from=min(lka), to=max(lka), add=TRUE, col='red') This above code runs into error: Error in

Re: [R] curve fitting an exponential function

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 -

Re: [R] curve comparison

/geyer/old/5601/examp/kolmogorov.html). I am not very familiar with non-parametric tests. A.K. From: Luis Fernando García Hernández luysgar...@gmail.com To: arun smartpink...@yahoo.com Sent: Monday, July 30, 2012 9:00 PM Subject: Re: [R] curve comparison

Re: [R] curve comparison

From: Luis Fernando García Hernández luysgar...@gmail.com To: arun smartpink...@yahoo.com Sent: Monday, July 30, 2012 9:00 PM Subject: Re: [R] curve comparison Thanks both for your answer, Will, do you have any reference which shows me how how to perform this analysis? I´d like to know to find

[R] curve comparison

Dear R users, I have seven regression lines I´d like to compare, in order to find out if these are significatively different. The main problem is that these are curves, non normal, non homogeneous data, I´ve tried to linearize them but it has not worked. So I´d like to know if you know any

Re: [R] curve comparison

I'm very much a novice with R, and not a statistician. But as my group has been working with generalized additive models (semi-parametric regression), we have followed Wood's advice about using the R anova function to do model comparison for different regressions. I would imagine at least some

Re: [R] curve comparison

-project.org Cc: Sent: Monday, July 30, 2012 1:38 AM Subject: [R] curve comparison Dear R users, I have seven regression lines I´d like to compare, in order to find out if these are significatively different. The main problem is that these are curves,  non normal, non homogeneous data, I´ve tried

Re: [R] Curve fitting, probably splines

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 ##

Re: [R] Curve fitting, probably splines

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

[R] Curve fitting, probably splines

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

Re: [R] curve fitted ... how to retreive data

THanks so much Gene, it worked mintewab Från: gley...@gmail.com [gley...@gmail.com] f#246;r Gene Leynes [gleyne...@gmail.com] Skickat: den 7 december 2011 18:43 Till: Mintewab Bezabih Kopia: r-help@r-project.org Ämne: Re: [R] curve fitted ... how

[R] curve fitted ... how to retreive data

Dear R users, I have now managed to fit the curve using the thin plate spline as follows: library(mgcv) b - gam(y~s(x1,x2,k=100),data =dat) vis.gam(b) What I want now is to get the fitted data for y and copy it so that I use it for further analysis. Many thanks in advance mintewab

Re: [R] curve fitted ... how to retreive data

For the components: result = predict(b, type=terms) For the total fit: result = predict(b) result = b$fitted.values On Wed, Dec 7, 2011 at 3:24 AM, Mintewab Bezabih mintewab.beza...@economics.gu.se wrote: Dear R users, I have now managed to fit the curve using the thin plate spline as

Re: [R] curve

Val, Here's the complete console output. The graph produced is at: http://www.functionaldiversity.org/temp/curve.png R version 2.12.0 (2010-10-15) Copyright (C) 2010 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: x86_64-redhat-linux-gnu (64-bit) R is free software and

Re: [R] curve

You are most likely to get reasonable help if you provide at a minimum your OS, the version of R you're using, and the error message you get. And also if you send your replies to the R-help list, not just me. Sarah On Tue, Dec 14, 2010 at 10:58 AM, Val valkr...@gmail.com wrote: Thank you

Re: [R] curve

On 2010-12-14 08:43, Sarah Goslee wrote: You are most likely to get reasonable help if you provide at a minimum your OS, the version of R you're using, and the error message you get. And also if you send your replies to the R-help list, not just me. Sarah Sarah's code works perfectly well on

[R] curve

Hi All, I generated 5000 samples using the following script test- rnorm(5000,1000,100) test1 - subset(test, subset=(test 1100)) d - density(test) plot(d, main=Density of production) abline(v=mean(test1) I wanted to do the following but faced

Re: [R] curve

Here's one way to do what I think you want: test- rnorm(5000,1000,100) test1 - subset(test, subset=(test 1100)) d - density(test) plot(d, main=Density of production, xlab=) lines(d$x[d$x 1100], d$y[d$x 1100], col=blue, lwd=2) curveheight - d$y[abs((d$x -

Re: [R] curve

Thanks Sarah, 1. to shade or color (blue) the curve using the criterion that any values greater than 11,000 I think I was not clear in the above point. I want shade not the line but the area under the curve, and Your last line of code, segments(x0=mean(test1), y0=0, y1=curveheight) gave me

Re: [R] curve

On Mon, Dec 13, 2010 at 2:12 PM, Ashta sewa...@gmail.com wrote: Thanks Sarah, 1. to shade or color (blue) the curve using the criterion that any values greater than 11,000 I think I was not clear in the above point. I want shade not the line but the area under the curve, Here's an example

Re: [R] Curve Fitting/Regression with Multiple Observations

. Snow Ph.D. Statistical Data Center Intermountain Healthcare greg.s...@imail.org 801.408.8111 -Original 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

Re: [R] Curve Fitting/Regression with Multiple Observations

] 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

Re: [R] Curve Fitting/Regression with Multiple Observations

-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

Re: [R] Curve Fitting/Regression with Multiple Observations

. -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

Re: [R] Curve Fitting/Regression with Multiple Observations

-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

Re: [R] Curve Fitting/Regression with Multiple Observations

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

[R] Curve Fitting

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

Re: [R] Curve Fitting

-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

Re: [R] Curve Fitting

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 -

Re: [R] Curve Fitting/Regression with Multiple Observations

-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

Re: [R] Curve Fitting/Regression with Multiple Observations

-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

Re: [R] Curve Fitting/Regression with Multiple Observations

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

Re: [R] Curve Fitting/Regression with Multiple Observations

- 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

[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-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

Re: [R] Curve Fitting/Regression with Multiple Observations

-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

Re: [R] Curve Fitting/Regression with Multiple Observations

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

Re: [R] Curve Fitting/Regression with Multiple Observations

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

Re: [R] Curve Fitting/Regression with Multiple Observations

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 -

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 theory) and the conclusion I want to make is qualitative rather than quantatitive -- a

Re: [R] curve

-project.org [mailto:r-help-boun...@r- project.org] On Behalf Of Duncan Murdoch Sent: Thursday, April 15, 2010 7:29 AM To: Dwayne Blind Cc: r-help@r-project.org Subject: Re: [R] curve On 14/04/2010 4:59 PM, Dwayne Blind wrote: Dear R users, How can I use curve with a function of two

Re: [R] curve

On 14/04/2010 4:59 PM, Dwayne Blind wrote: Dear R users, How can I use curve with a function of two variables ? See Ben Bolker's reply if you want to plot a surface. If you want to plot a curve by holding one of the two variables fixed, just set it to a constant value, and use x as the

Re: [R] curve

: Thursday, April 15, 2010 7:29 AM To: Dwayne Blind Cc: r-help@r-project.org Subject: Re: [R] curve On 14/04/2010 4:59 PM, Dwayne Blind wrote: Dear R users, How can I use curve with a function of two variables ? See Ben Bolker's reply if you want to plot a surface. If you want to plot

[R] curve

Dear R users, How can I use curve with a function of two variables ? Thank you very much, Dwayne [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting

Re: [R] curve

Dwayne Blind dwayneblind at gmail.com writes: How can I use curve with a function of two variables ? see curve3d in the emdbook package. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting

[R] curve fitting to data

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 -

Re: [R] curve fitting to data

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

Re: [R] curve fitting to data

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

Re: [R] curve from a formula with ggplot2

-project.org Objet : Re: [R] curve from a formula with ggplot2 Hi Benoit, Have a look at http://had.co.nz/ggplot2/stat_function.html. Does that help? Hadley On Tue, Jul 7, 2009 at 11:15 AM, Benoit Boulinguiezbenoit.boulingu...@ensc-rennes.fr wrote: Hi all, I'm smoothly transferring my

Re: [R] curve from a formula with ggplot2

Hi Benoit, Have a look at http://had.co.nz/ggplot2/stat_function.html. Does that help? Hadley On Tue, Jul 7, 2009 at 11:15 AM, Benoit Boulinguiezbenoit.boulingu...@ensc-rennes.fr wrote: Hi all, I'm smoothly transferring my lattice graphs to ggplot2 graphs, but I'm stuck on representing a

[R] curve from a formula with ggplot2

Hi all, I'm smoothly transferring my lattice graphs to ggplot2 graphs, but I'm stuck on representing a curve from a formula. I'm looking for the equivalent of curve() in ggplot2, Hadley Wickham mentions geom_curve, but as far as I've seen in the help it doesn't exist. My need is to plot a

[R] Curve fit a nonlinear equation with box constraints: success, many thanks!

Dear R Helper Community! Thank you for all your help and suggestions. For your reference and any future person searching the archives, here is the solution that did what I wanted it to do. As background, my goal was to find the coefficients for the following equation form: y ~ c1 * x1

[R] curve fitting

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

Re: [R] curve fitting

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

Re: [R] curve fitting

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 -

Re: [R] Curve fitting,FDA for biological data

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

Re: [R] Curve fitting,FDA for biological data

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

Re: [R] Curve fitting,FDA for biological data

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

[R] Curve fitting,FDA for biological data

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

Re: [R] Curve fitting,FDA for biological data

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.

Re: [R] Curve fitting,FDA for biological data

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

[R] curve fitting with given term

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

Re: [R] curve fitting with given term

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

Re: [R] curve fitting with given term

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

Re: [R] curve fitting with given term

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

[R] curve fitting problem

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 ~

Re: [R] curve fitting problem

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 -