similar to: nls

Hi, I am a grad student at the university of Louisiana at Lafayette. I have a question. I have an equation with 3 unknowns. f= P/ (r+p) pow(B). I have values of f for different r's. Can i use R to find the P,p,B values which are constants. The equation is Manderbolts equation. Thanks and regards, Sai Charan Komanduru Research Assistant, CACS ULL

Hi, I am using nls library df <- read.table("data.txt", header=T); library(nls); fm <- nls(y ~ a*(x+d)^(-b), df, start=list(a=max(df->y,na.rm=T)/2,b=1,d=0)); coef(fm); q(); When i am using the above routine i am getting the following error Error in nlsModel(formula, mf, start) : singular gradient matrix at initial parameter estimates Can some one help me in this.

Hi All. I've run into a problem with the plinear algorithm in nls that is confusing me. Assume the following reaction time data over 15 trials for a single unit. Trials are coded from 0-14 so that the intercept represents reaction time in the first trial. trl RT 0 1132.0 1 630.5 2 1371.5 3 704.0 4 488.5 5 575.5 6 613.0 7 824.5 8 509.0 9

Dear R gurus, As part of finding initial values for a much more complicated fit I want to fit a function of the form y ~ a + bx + cx^d to fairly "noisy" data and have hit some problems. To demonstrate the specific R-related problem, here is an idealised data set, smaller and better fitting than reality: # idealised data set aDF <- data.frame( x= c(1.80, 9.27, 6.48, 2.61, 9.86,

Its possible to corrupt the binary if you actually open it in a browser, and the browser tries to display it, and then you save it. Have you tried right-clicking on the link to download it? Robert. On Thu, 30 Jan 2003, saicharan komanduru wrote: > Hi, > I copied the binary setup file for windows. I tried > to install but it says the binary is corrupted. I > tried getting the files

Dear all i want to estimated the parameter of the gamma density(a,b,d) f(x) = (1/gamma(b)*(a^b)) * ((x-d)^(b-1)) * exp{-(x-d)/a)} for x>d f(x) = Age specific fertility rate x = age when i run this in R by usling nls() gamma.asfr <- formula(asfr ~ (((age-d)^(b-1))/((gamma(b))*(a^b)))* exp(-((age-d)/a))) gamma.asfr1 <- nls(gamma.asfr, data= asfr.aus, start = list(b = 28, a = 1, d= 0.5),

Hi, I am trying to install R on Windows. I copied the set up binary. But it is corrupted on all the mirror sites. Please let know an alternative for getting the software. Thanks, Sai

Dear R users,   I am struggling to fit expo-linear equation to my data using "nls" function. I am always getting error message as i highlighted below in yellow color:     ### Theexpo-linear equation which i am interested to fit my data:       response_variable =  (c/r)*log(1+exp(r*(Day-tt))), where "Day" is time-variable   ## my response variable   rl <-

Dear R users, I am struggling to fit expo-linear equation to my data using "nls" function. I am always getting error message as i highlighted below in yellow color:  Theexpo-linear equation which i am interested to fit my data:       response_variable =  (c/r)*log(1+exp(r*(Day-tt))), where "Day" is time-variable my response variable rl <-

Hello, i have a problem with the function nls(). This are my data in "k": V1 V2 [1,] 0 0.367 [2,] 85 0.296 [3,] 122 0.260 [4,] 192 0.244 [5,] 275 0.175 [6,] 421 0.140 [7,] 603 0.093 [8,] 831 0.068 [9,] 1140 0.043 With the nls()-function i want to fit following formula whereas a,b, and c are variables: y~1/(a*x^2+b*x+c) With the standardalgorithm

I have found some "issues" (bugs?) with nls confidence intervals ... some with the relatively new "port" algorithm, others more general (but possibly in the "well, don't do that" category). I have corresponded some with Prof. Ripley about them, but I thought I would just report how far I've gotten in case anyone else has thoughts. (I'm finding the code

Hi everyone, I'm trying to perform a bi exponential Fit with the package NLS. the plinear algorithm seems to be a good choice see: p<-3000 q<-1000 a<--0.03 b<--0.02 t<-seq(0:144);t y<-p*exp(a*t) + q*exp(b*t)+rnorm(t,sd=0.3*(p* exp(a*t) + q*exp(b*t))) fittA <- nls(y~cbind(exp(a*t), exp(b*t)), algorithm="plinear",start=list(a=-.1, b=-0.2), data=list(y=y, t=t),

Hi Everyone, I am trying to use NLS to fit a dataset using a Kappa function, but I am having problems. Depending on the start values that I provide, I get either: Error in numericDeriv(form[[3L]], names(ind), env) : Missing value or an infinity produced when evaluating the model Or Error in nls(FldFatRate ~ funct3(MeanDepth_m, h, k, z, a), data = data1, : singular gradient I think these

hello all i havnt had a chance to read through the references provided for the "nls" function (since the libraries are closed now). can anyone shed some light on how the "plinear" algorithm works? also, how are the fitted values obtained? also, WHAT DOES THE ".lin" below REPRESENT? thanking you in advance ###################################### i have a quick

I'm trying to fit a function y=k*l^(m*x) to some data points, with reasonable starting value estimates (I think). I keep getting "singular matrix 'a' in solve". This is the code: ox <- c(-600,-300,-200,1,100,200) ir <- c(1,2.5,4,9,14,20) model <- nls(ir ~ k*l^(m*ox),start=list(k=10,l=3,m=0.004),algorithm="plinear") summary(model) plot(ox,ir) testox <-

The documentation for nls says the following about the starting values: start: a named list or named numeric vector of starting estimates. Since R 2.4.0, when 'start' is missing, a very cheap guess for 'start' is tried (if 'algorithm != "plinear"'). It may be a good idea to document that when algorithm = "port", if start is a named

Hi When I type: > ?nls I come across this section: algorithm: character string specifying the algorithm to use. The default algorithm is a Gauss-Newton algorithm. Other possible values are '"plinear"' for the Golub-Pereyra algorithm for partially linear least-squares models and '"port"' for the 'nl2sol'

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,

Hi, I am trying to bootstrap the difference between each parameters among two non linear regression (distributed loss model) as following: # data.frame > Raies[1:10,] Tps SolA Solb 1 0 32.97 35.92 2 0 32.01 31.35 3 1 21.73 22.03 4 1 23.73 18.53 5 2 19.68 18.28 6 2 18.56 16.79 7 3 18.79 15.61 8 3 17.60 13.43 9 4 14.83 12.76 10 4 17.33 14.91 etc... # non

I'm trying to fit the Bass Diffusion Model using the nls function in R but I'm running into a strange problem. The model has either two or three parameters, depending on how it's parameterized, p (coefficient of innovation), q (coefficient of immitation), and sometimes m (maximum market share). Regardless of how I parameterize the model I get an error saying that the step factor has