similar to: NLS problem

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,

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) >

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

I have data vectors x and y both with 179 observations. I'm trying to fit a nonlinear model with five parameters using nls. The formula is only defined within a range of x-values, it should be zero otherwise, thus my attempted use of ifelse: > df<-data.frame(x,y) >

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

Dear R-users, I am trying to create a model using the NLS function, such that: Y = f(X) + q + e Where f is a nonlinear (Weibull: a*(1-exp(-b*X^c)) function of X and q is a covariate (continous variable) and e is an error term. I know that you can create multiple nonlinear regressions where x is polynomial for example, but is it possible to do this kind of thing when x is a function with unknown

Hello, I am trying to model a type II functional response of number of prey eaten (Ne) against number supplied (No) with a non-linear least squares regression (nls). I am using a modification of Holling's (1959) disc equation to account for non-replacement of prey; Ne=No{1-exp[a(bNe-T)]} where a is the attack rate, b is the handling time, and T is the experimental period. My script is as

Hello, I have data like the following: datum <- structure(list(Y = c(415.5, 3847.83333325, 1942.833333325, 1215.22222233333, 950.142857325, 2399.5833335, 804.75, 579.5, 841.708333325, 494.053571425 ), X = c(1.081818182, 0.492727273, 0.756363636, 0.896363636, 1.518181818, 0.499166667, 1.354545455, 1.61, 1.706363636, 1.063636364 )), .Names = c("Y", "X"), row.names = c(NA,

Hi,all: I met a problem of nls. My data: x y 60 0.8 80 6.5 100 20.5 120 45.9 I want to fit exp curve of data. My code: > nls(y ~ exp(a + b*x)+d,start=list(a=0,b=0,d=1)) Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial parameter estimates I can't find out the reason for the error. Any suggesions are welcome. Many thanks. [[alternative HTML

Hi All, would appreciate an answer on this if you have a moment; Is there a function (before I try and write it !) that allows the input of a covariance or correlation matrix to calculate PCA, rather than the actual data as in princomp() Regards Glenn [[alternative HTML version deleted]]

Hi, I'm trying to make a regression of the form : formula <- y ~ Asym_inf + Asym_sup * ( (1 / (1 + (n1 * (exp( (tmid1-x) / scal1) )^(1/n1) ) ) ) - (1 / (1 + (n2 * (exp( (tmid2-x) / scal2) )^(1/n2) ) ) ) ) which is a sum of the generalized logistic model proposed by richards. with data such as these: x <- c(88,113,128,143,157,172,184,198,210,226,240,249,263,284,302,340) y <-

Hello there, I am trying to fit an exponential fit using Least squares to some data. #data x <- c(1 ,10, 20, 30, 40, 50, 60, 70, 80, 90, 100) y <- c(0.033823, 0.014779, 0.004698, 0.001584, -0.002017, -0.003436, -0.000006, -0.004626, -0.004626, -0.004626, -0.004626) sub <- data.frame(x,y) #If model is y = a*exp(-x) + b then fit <- nls(y ~ a*exp(-x) + b, data = sub, start

I'm using nls to fit a variety of different models. Here I use SSgompertz as an example. I want the ability to fix one (or more) of the coefficients that would normally be optimised (e.g. fix b3=0.8). Examples; based on and using data from example(SSgompertz) #--------------------- # vanilla call to nls, no coefficients fixed, works fine nls(density ~ SSgompertz(log(conc), Asym, b2, b3),

Hi, All: What tools exist for diagnosing singular gradient problems with 'nls'? Consider the following toy example: DF1 <- data.frame(y=1:9, one=rep(1,9)) nlsToyProblem <- nls(y~(a+2*b)*one, DF1, start=list(a=1, b=1), control=nls.control(warnOnly=TRUE)) Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial

Hi,all: I am running a nonlinear regression and there is a problem. There is a data frame: data p s x t 1 875.0 12392.5 11600 0.06967213 2 615.0 12332.5 12000 0.06967213 3 595.0 12332.5 12000 0.06967213 4 592.5 12337.0 12000 0.06967213 5 650.0 12430.0 12000 0.06967213 6 715.0 12477.5 12000 0.06967213 . . . . str(data): 'data.frame': 234 obs. of 4 variables:

first, apologies for so many posts yesterday and today. I am wrestling with nls() and nls2(). I have tried to whittle it down to a simple example that still has my problem, yet can be cut-and-pasted into R. here it is: library(nls2) options(digits=12); y= c(0.4334,0.3200,0.5848,0.6214,0.3890,0.5233,0.4753,0.2104,0.3240,0.2827,0.3847,0.5571,0.5432,0.1326,0.3481) x=

For a list say; list1<-{1,2,3,4,5,2,1} How do I remove the duplicates please? My real list is 20,000 obs long of dates with many duplicates Regards Glenn [[alternative HTML version deleted]]

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