similar to: NLS "Singular Gradient" Error

Greetings, I am struggling a bit with a non-linear regression. The problem is described below with the known values r and D inidcated. I tried to alter the start values but get always following error message: Error in nlsModel(formula, mf, start, wts): singular gradient matrix at initial parameter estimates Calls: nls -> switch -> nlsModel I might be missing something with regard to the

Hi, I am a non-expert user of R. I am essaying the fit of two different functions to my data, but I receive two different error messages. I suppose I have two different problems here... But, of which nature? In the first instance I did try with some different starting values for the parameters, but without success. If anyone could suggest a sensible way to proceed to solve these I would be

Hello there I have the following model based on the hollings disc equation for the type II functional response for 2 data sets: nls(eaten~(a*suppl)/(1+a*h*suppl) where eaten is the number of prey eaten by a predator and suppl is the number of prey initially supplied to the same predator. I have parameter estimates of 'a' and 'h' for the two populations studied and would like

I am using nls to fit a non linear function to some data. The non linear function is: y= 1- exp(-(k0+k1*p1+ .... + kn*pn)) I have chosen algorithm "port", with lower boundary is 0 for all of the ki parameters, and I have tried many start values for the parameters ki (including generating them at random). If I fit the non linear function to the same data using an external

Hi, I have data on stomach contents. Possible prey species are in the hundreds, so a list of prey codes has been in used in many labs doing this kind of work. When comes time to do analyses on these data one often wants to regroup prey in broader categories, especially for rare prey. In SAS you can nest a large number of "if-else", or do this more cleanly with "select"

Hi, I'm trying to move to R the last few data handling routines I was performing in SAS. I'm working on stomach content data. In the simplified example I provide below, there are variables describing the origin of each prey item (nbpc is a ship number, each ship may have been used on different trips, each trip has stations, and individual fish (tagno) can be caught at each

//Referring to the response posted many years ago, copied below, what is the specific criterium used for singularity of the gradient matrix? Is a Singular Value Decomposition used to determine the singular values? Is it the gradient matrix condition number or some other criterion for determining singularity? // //Glenn // / / /> What does the error 'singular gradient' mean

Hi R experts :) I'm trying to carry out a survival model on my data, but I am unsure of whether it's appropriate or if I should do something specific in regards to hazard. My data is time to death by predator where I have 8 prey and one predator in the setting. This means that two prey can't possibly die at the same time and I can't quite get my head around how to include this in

Hi, I wonder if anyone can help me with specifying a right model for my analysis. I am a beginner to lme methods and though have spent already many hours studying from various books an on-line helps, I was unfortunately not able to find a solution to my problem on my own. Data structure: I studied escape behavior of three species of a prey to a predator. The prey specimens (many) were in a

Dear R-users, I am currently modifying a previously developed predator prey model and was curious if there was a way to add in a disturbance to the model (let's say at time t=100). The disturbance can be the introduction of 40 prey (N=40) and 10 predators (Pred = 10). I would like to see my model go from a state of equilibrium (up to t = 99), show this disturbance (at t = 100) and then

Dear R friends- I'm attempting to generate a regression tree with one gradient predictor and multiple responses, trying to test if change in size (turtle.data$Clength) acts as a single predictor of ten multiple diet taxa abundances (prey.data) Neither rpart or mvpart seem to allow me to do multiple responses. (Or if they can, I'm not using the functions properly.) > library(rpart)

I have installed Prey onto my pc running PCLinuxOS following the instructions in http://frankscorner.org/index.php?p=prey or http://appdb.winehq.org/objectManager.php?sClass=application&iId=3465 and tried running it. I then found I needed the nocd patch, which I downloaded and applied. I then tried cd to the app folder, then wine prey.exe with the following result: Prey 1.0.103 win-x86 Jun 10

Hi I am struggling with the correct code syntax to generate xyplots that are different from the default graphics option. This is simplified version of my data: site size dnaconc prey A 44 22.6 1 A 47 18.2 1 B 38 25.6 0 B 47 20.5 1 C 45 30.5 0 C 32 18.5 0 D 45 15.0 1 D 22 20.1 0 So I am using xyplot(dnaconc~size|site,data=bug,groups=prey) to generate 4 panels (one for each site). My

Dear list, I´m using the mgcv package to model the proportion by weight of certain prey on the stomach content of a predator. This proportion is the ratio of two weights (prey weight over stomach weight), and ranges between 0 and 1. The variance is low when proportion is close to 0 and 1, and higher at intermediate values. It seems that the best way to go is to model this using the

I could use some help understanding how nls parses the formula argument to a model.frame and estimates the model. I am trying to utilize the functionality of the nls formula argument to modify garchFit() to handle other variables in the mean equation besides just an arma(u,v) specification. My nonlinear model is y<-nls(t~a*sin(w*2*pi/365*id+p)+b*id+int,data=t1,

I've been trying to run some analysis using the nls function in R but keep coming up with an error message which I don't understand how to fix. The message follows here: Error in nls(formula = f.p, data = n.data, start = list(S = 1, a = -0.1, : singular gradient In addition: Warning messages: 1: In min(x) : no non-missing arguments to min; returning Inf 2: In max(x) : no non-missing

Hi all. Sorry for posting again such a topic but I went through previous posts but couldn't find a solution. I use the following code to fit an exponential model to my data. I have 4 different datasets. For 3 datasets nls seems to work fine and I have no error messages. But for 1 dataset I am getting the "world known" singular gradient error. xfit.dNEE <-

I could use some help understanding how nls parses the formula argument to a model.frame and estimates the model. I am trying to utilize the functionality of the nls formula argument to modify garchFit() to handle other variables in the mean equation besides just an arma(u,v) specification. My nonlinear model is y<-nls(t~a*sin(w*2*pi/365*id+p)+b*id+int,data=t1,

I could use some help understanding how nls parses the formula argument to a model.frame and estimates the model. I am trying to utilize the functionality of the nls formula argument to modify garchFit() to handle other variables in the mean equation besides just an arma(u,v) specification. My nonlinear model is y<-nls(t~a*sin(w*2*pi/365*id+p)+b*id+int,data=t1,

Hello I was trying to estimate the weibull model using nls after putting OLS values as the initial inputs to NLS. I tried multiple times but still i m getting the same error of Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial parameter estimates. The Program is as below > vel <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14) > df <- data.frame(conc, vel) >