similar to: R + Hull-White model using nonlinear least squares

Hello. I am analysing data from a mixed model perspective using the lme() function. The fixed effects model is a quadratic (Y~X+X2) where X2 is the square of X and the data have a 3-level structure. I fitted a series of three models with the same fixed effects but differing in the random effects (only intercept, intercept + X, intercept +X +X2). The anova shows that all three parameters vary

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

Hi, I am new to R, and am trying to solve the following optimization problem: This is a nonlinear least squares problem. I have a set of 3D voxels. All I need is to find a least squares fit to this data. The data model actually represent a cube-like structure, consisting of seven straight lines. The lines have some intersections (and at this intersection both of the participating lines end).

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

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

Hello! I am running R-2.3.1-i386-1 on Slackware Linux 10.2. I am a former matlab user, moving to R. In matlab, via the cftool, I performed nonlinear curve fitting using the method "nonlinear least squares" with the "Trust-Region" algorithm and not using robust fitting. Is it possible to perform the same analysis in R? I read quite a lot of R documentation, but I could not find

I'm trying to fit a model to solve a biological problem. There are multiple independent variables, and also there are multiple responses. Each response is a function of all the independent variables, plus a set of parameters. All the responses depend on the same variables and parameters - just the form of the function changes to define each seperate response. Any ideas how I can fit

Hi, I am trying to use the nls() function to closely approximate a vector of values, colC and I'm running into trouble. I am not sure how if I am asking the program to do what I think its doing, because the same minimization in Excel's Solver does not run into problems. If anyone can tell me what is going wrong, and why I'm getting a singular convergence(7) error, please tell me. I

Dear UseRs, I would like to know which function is the most efficient in finding convex hull of points in 3(or 2)-dimensional case? Functions for finding convex hull is the following: convex.hull (tripack), chull (grDevices), in.chull (sgeostat), convhulln (geometry), convexhull.xy (spatstat), calcConvexHull (PBSmapping). I also would like to know if there is a function that can be used

Dear R gurus and users, I seem to have problem finding the right tool for plotting convex hulls over 2D plots, after a cluster analysis. In fact I would like to draw a convex hull in 2D for a generic group of points. I found a "convhulln", but this doesn't seem to give me a convex hull. Here is what I do: > library(mvtnorm) > Mean <- c(2,1) > Sigma <-

Hi everyone, I wonder if there is a code in r that can generate a 3d convex hull from a data-frame containing 3 columns and then use another database with the same three columns and for each row determine if the xyz point is inside or not the convex hull generated with the first database? The package geometry allows to calculate a hull and it's volume. I was planning to calculate the volume

Hi there, I have been using the Tps function (within the Fields package) for a while now to interpolate different sedimentary units. Due to the method of formation of the units I know that at some edges the thickness of the unit decreases to zero. I was wondering if there was someway to specify that the interpolation only occurs within the convex hull of the data, outside of which the the values

Does anyone know of a R package that will determine the convex hull of a high-dimensional dataset (say 4-10 dimensions). I know chull works for 2D data. I'm neophyte to R and convex hulls so please keep it simple. Many thanks Ben -- Ben Stapley. Biomolecular Sciences, UMIST, PO Box 88, Manchester M60 1QD. Tel 0161 200 5818 Fax 0161 236 0409

Dear Friends, Here is an algorithm for finding concave hulls: http://get.dsi.uminho.pt/local/ Has anyone implemented such an algorithm in R? RSiteSearch('concave hull') didn't reveal one (I think). _____________________________ Professor Michael Kubovy University of Virginia Department of Psychology Postal Address: P.O.Box 400400, Charlottesville, VA 22904-4400 Express Parcels