similar to: starting point for non linear fitting

Dear R-Help, I'm trying to fit a model of the following form using gnls. I've fitted it using nlsList with the following syntax: nlsList(Y~log(exp(a0-a1*X)+exp(b0-b1*X))|K,start=list (a0=6,a1=0.2,b0=4.5,b1=0.001),data=data.frame(Y=y,X=X,K=k))) which works just fine: <snip> Coefficients: a0 a1 b0 b1 1 5.459381 0.5006811 5.137458 -0.0040548687

Dear RUsers, I am new to R. I am learning how to use R. I am a PC user and run R on windows. I would appreciate if some one could guide me on a few questions I have: 1) I have 4 cel files (2 replicates for NORM and Disease resp). I have been able to run siggenes on this dataset where I have 4 labels in the class file groupsnhi.cl op-> (0,0,1,1) and my data has been read into datrmanhi after

Klibc FTBFS with '-mno-odd-spreg' on mips32(el) platforms, As it try to save/restore odd-number FPR. Indeed no other architectures save/restore FPR at all. It shouldn't be needed. --- usr/klibc/arch/mips/setjmp.S | 24 ------------------------ 1 file changed, 24 deletions(-) diff --git a/usr/klibc/arch/mips/setjmp.S b/usr/klibc/arch/mips/setjmp.S index 68eed19..21e4115 100644 ---

Hi, I am trying to fit a function of the form: y = A0 + A1 * exp( -0.5* ( (X - Mu1) / Sigma1 )^2 ) - A2 * exp ( -0.5* ( (X-Mu2)/Sigma2 )^2 ) i.e. a mean term (A0) + a difference between two gaussians. The constraints are A1,A2 >0, Sigma1,Sigma2>0, and usually Sigma2>Sigma1. The plot looks like a "Mexican Hat". I had trouble (poor fits) fitting this function to toy data

Dear R users and experts, I want to fit a shifted parabolic function with the following functional form to my data: f(x)=a0*(x+a1)^2+a2 (a0, a1 and a2 are scaling factors.) What is standard approach to do this in R? I tried the "lm" function in R but I got problems getting the above functional form. Any help is welcome :) . Greetings, Henri

Hi R experts, I have the following timeseries data: #example data structure a <- c(NA,1,NA,5,NA,NA,NA,10,NA,NA) c <- c(1:10) df <- data.frame(timestamp=a, sequence=c) print(df) where i would like to linearly interpolate between the points 1,5, and 10 in 'timestamp'. Original timestamps should not be modified. Here the code I use to run the interpolation (so far): # linear

I need to do a non-linear regression in the form of Y = a0 + a1 * arctan(a2 * x) + error. A data sample (X,Y) is available, but I can't remember how to run this sort of regression through R so that I get a value for a0, a1 and a2. Can someone please give me a hint? Thank you in advance.

Dear all; I have a dataframe with several columns. The columns are the elevation, volume and the area of the cells (which were placed inside a polygon). I have extracted them from DEM raster to calculate the volume under polygon and the elevation for a specific volume of the reservoir. > head(x6,2) Elevation Vol Area V_sum A_sum 1 2145 13990.38 85.83053 13990.38

I am trying to install Debian Linux on an an old Intel Classic R+ computer that uses an internal Yamaha SCSI CD writer model CRW8424S connected to an Adaptec ISA SCSI card (I think its a 1542CP). The CD writer is is the only device connected to the SCSI card. The computer has one hard drive connected to the on-board IDE interface, a 1.44MB 3 1/2 inch floppy and a 1.2MB 5 1/4 inch floppy. The hard

First, thanks to all who helped me with my question about rescaling axes on the fly. Using unlist() and range() to set the axis ranges in advance worked well. I've since plotted about 300 datasets with relative ease. Now I'm trying to fit a lossy oscillator resonance to (the square root of) a lorentzian (testframe$y is oscillator amplitude, testframe$x is drive frequency): lorentz

Hi, my data set is data.frame(id, yr, y, l, e, k). I would like to estimate Lee and Schmidts (1993, OUP) model in R. My colleague wrote SAS code as follows: ** procedures for creating dummy variables are omitted ** ** di# and dt# are dummy variables for industry and time ** data a2; merge a1 a2 a; by id yr; proc sysnlin maxit=100 outest=beta2; endogenous y; exogenous l e k

This message is in MIME format. The first part should be readable text, while the remaining parts are likely unreadable without MIME-aware tools. --1660387551-150661043-1249684349=:2997 Content-Type: TEXT/PLAIN; charset=iso-8859-1; format=flowed Content-Transfer-Encoding: QUOTED-PRINTABLE Hi Jeff, =09As mentioned in my message, I *did* replicate on another platform.=20 One platform was

--nextPart2699335.H7BBWTdPIb Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Content-Disposition: inline Hello :) After upgrading recently from Woody to Sarge (which went fairly well) I now= =20 have trouble with logcheck. I have been unable to track down a solution. Logcheck runs perfectly through the week until Sunday when logrotate does

Hi all, Like a lot of people I noticed that I get different results when I use nls in R compared to the exponential fit in excel. A bit annoying because often the R^2 is higher in excel but when I'm reading the different topics on this forum I kind of understand that using R is better than excel? (I don't really understand how the difference occurs, but I understand that there is a

After many months, I am now banging my head against the wall because I can't find a solution to this seemingly trivial problem.&nbsp; Any help would be appreciated: I am trying to apply a nonlinear fitting routine to a 3D MR image on a voxel-by-voxel basis.&nbsp; I've tested the routine using simulated data and things went well.&nbsp; As for the real data, the fitting routine

Dear RUsers, I am new to R. I am learning how to use R. I am a PC user and run R on windows. I would appreciate if some one could guide me on a few questions I have: 1) I have 4 cel files (2 replicates for NORM and Disease resp). I have been able to run siggenes on this dataset where I have 4 labels in the class file groupsnhi.cl op-> (0,0,1,1) and my data has been read into datrmanhi after

This smells like a bug to me. The error is triggered by the line: variables <- eval(predvars, data, env) inside model.frame.default(). At that point, na.action has not been applied, so poly() ended being called on data that still contains missing values. The qr() that issued the error is for generating the orthogonal basis when evaluating poly(), not for fitting the linear model itself.

This smells like a bug to me. The error is triggered by the line: variables <- eval(predvars, data, env) inside model.frame.default(). At that point, na.action has not been applied, so poly() ended being called on data that still contains missing values. The qr() that issued the error is for generating the orthogonal basis when evaluating poly(), not for fitting the linear model itself.

Hi everybody! I'm looking some way to do in R a polynomial fit, say like polyfit function of Octave/MATLAB. For who don't know, c = polyfit(x,y,m) finds the coefficients of a polynomial p(x) of degree m that fits the data, p(x[i]) to y[i], in a least squares sense. The result c is a vector of length m+1 containing the polynomial coefficients in descending powers: p(x) = c[1]*x^n +

From: Dejan Latinovic <Dejan.Latinovic at imgtec.com> --- usr/include/arch/mips64/klibc/archconfig.h | 3 + usr/include/arch/mips64/klibc/archsetjmp.h | 39 ++++++ usr/include/arch/mips64/machine/asm.h | 76 ++++++++++ usr/include/fcntl.h | 2 +- usr/include/sys/md.h | 1 + usr/include/sys/resource.h | 4 +-