similar to: 3D constrained nonlinear least squares fit

After many months, I am now banging my head against the wall because I can't find a solution to this seemingly trivial problem.  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.  I've tested the routine using simulated data and things went well.  As for the real data, the fitting routine

Hi Experts, I am new to R, and was wondering how to do 3D linear regression in R. In other words, I need to Fit a 3-Dimensional Line to Data Points (input). I googled before posting this, and found that it is possible in Matlab and other commercial packages. For example, see the Matlab link:

Dear "R" users, I have a question about the Package DierckxSpline. I have tried to find the answer by myself but it didn't worked out. I wondered if Dierckxspline can use different sets of y values in one time to fit a line with knot. I have different sets of Y values representing the same thing for different voxels (in an fmri image). I have already fitted the data in different

Based on the examples I've seen in using statistical analysis packages such as lmer, it seems that people usually tabulate all the input data into one file with the first line indicating the variable names (or labels), and then read the file inside R. However, in my case I can't do that because of the huge amount of imaging data. Suppose I have a one-way within-subject ANCOVA with

Dear colleagues, I have an image which I can display in the greyscale using image. On this image, for some pixels, which I know, I want to display their activity based on a third measure. One way to do that would be to color these differently, and use an opacity measure to display the third measure. An example of what I am trying to do is at:

Hi everyone, I'm not exactly sure how to ask this question most clearly, but I hope that giving the context in which it occurs for me will help: I'm trying to compare the brain images of two patient populations; each image is composed of voxels (the 3D analogue of pixels), and I have two images per patient, one reflecting grey matter concentration at each voxel, and the other reflecting

Is there a function in R for constrained linear least squares? I used the matlab function LSQLIN: my aim is to obtain non-negative regression coefficients which sum 1. Thanks in advance, domenico vistocco ___________________________________ Yahoo! Mail: gratis 1GB per i messaggi e allegati da 10MB http://mail.yahoo.it

Hello. I have searched the archives but have not found anything. I need to solve a constrained optimisation problem for a nonlinear function (“maximum entropy formalism”). Specifically, Optimise: -1*SUM(p_ilog(p_i)) for a vector p_i of probabilities, conditional on a series of constraints of the form: SUM(T_i*p_i)=k_i for given values of T_i and k_i (these are constraints on

Dear R-ians, I want to perform an linear unmixing of image pixels in fractions of pure endmembers. Therefore I need to perform a constrained linear least-squares problem that looks like : min || Cx - d || ? where sum(x) = 1. I have a 3x3 matrix C, containing the values for endmembers and I have a 3x1 column vector d (for every pixel in the image). In theory my x values should all be in the

Hi guys I have data that contains the variances vt of the yields of 1, 2, 3, 4, 5,10, 20 year bonds. Assuming the Hull-White model for the yield of a t-year zero-coupon bond, I have to estimate the ? of the Hull-White model using nonlinear least squares and give a 95% con?dence interval for each parameter. Please can you guys tell how to find out ? using R. Any suggestion regarding what functions

Dear all, When doing nonlinear regression, we normally use nls if e are iid normal. i learned that if the form of the variance of e is not completely known, we can use the IRWLS (Iteratively Reweighted Least Squares ) algorithm: for example, var e*i =*g0+g1*x*1 1. Start with *w**i = *1 2. Use least squares to estimate b. 3. Use the residuals to estimate g, perhaps by regressing e^2 on

Hello, I am trying to optimise a nonlinear model to derive 'best-fit' parameter esimates using the genoud function. I have been using the genetic algorithm - gafit - in order to do this, but I am getting parameter estimates that do not always reach the global minimum. I am very keen to apply genoud to optimising this model to see if my results will improve, and also out of personal

Hello, I'd like to fit a user defined function to a data set, but I have problems to find my problem. The user defined function is a combination of two rectangular functions, and the listing below gives an example for what I want to do. The problem is, that I get the error message for fit1 and fit2 "Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial

Dear all, I encounter some discrepancies when comparing the deviance of a weighted and unweigthed model with the AIC values. A general example (from 'nls'): DNase1 <- subset(DNase, Run == 1) fm1DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1) This is the unweighted fit, in the code of 'nls' one can see that 'nls' generates a vector

Following on my posting of this morning, concerning a problem that I am having constructing a self-starting function for use with nls (and eventually with nlsList and nlme), the following is the self-starting function called NRhyperbola: > NRhyperbola function (Irr,theta,Am,alpha,Rd) { # Am is the maximum gross photosynthetic rate # Rd is the dark resiration rate (positive value) #

I have used Mike Powell's optimization routine (VA05AD) from the Harwell Subroutine Library (HSL) for more than 20 years. It is no exaggeration to say that it has helped make my career (thanks Mike). I recently learned that I am not alone in this respect - apparently it still has a loyal following in all sorts of fields! It is an exceedingly fine piece of software - fast, reliable and easy to

Hi there, I'm trying to fit a growth curve to some data and need to use a weighted least squares estimator to account for heteroscedasticity in the data. A weights argument is available in nls that would appear to be appropriate for this purpose, but it is listed as 'not yet implemented'. Is there another package which could implement this procedure? Regards, Robert Brown

Hello. I am having a problem setting up a self-starting function for use in nonlinear regression (and eventually in the mixed model version). The function is a non-rectangular hyperbola - called "NRhyperbola" - which is used for fitting leaf photosynthetic rate to light intensity. It has one independent variable (Irr) and four parameters (theta, Am, alpha and Rd). I have created this

Hi. I need some advises on how to use R to find pi (i is the index) with the following objective function and constraint: max (sum i)[ f(ai, bi, pi) * g(ci, di, pi) * Di ] s.t. (sum i)[ f(ai, bi, pi) * Di * pi] / (sum i)[ f(ai, bi, pi) * Di ] <= constant f and g are diffentiable. So, I am thinking of optim with method = "BFGS"? But wonder how to include the

Dear Mr Graves, Thank you very much for your response. Nobody else from this mailing list ventured to reply to me for the two weeks since I posted my question. "nlminb" and "optim" are just optimization procedures. What I need is not just optimization, but a nonlinear CURVE FITTING procedure. If there is some way to perform nonlinear curve fitting with the