similar to: NLS

Dear all, Being an economics student, I am trying to put together a little tutorial/FAQ/... for those who have (more) background in econometrics rather than in statistics -- just as I have. I'v been looking for a hypothesis testing tool in R (just linear or also nonlinear) for model parameters, but could not find anything so far. I did find a similar but unanswered question in the archives,

Hi, I have a problem with R-devel and Stataread 2.5. Stataread installs (compiles, at gives no error messages). But typing > library(stataread) Error in dyn.load(x, as.logical(local), as.logical(now)) : unable to load shared library "/usr/local/lib/R/library/stataread/libs/stataread.so": /usr/local/lib/R/library/stataread/libs/stataread.so: undefined symbol: errorcall Error in

Hi, I am new to R, but a fairly `old' user of Stata. I read posts asking about survey methods and large datasets in the archive, so I will not ask those questions again. But some still remain: - R seems to consume more memory given the same set of data, say if I have only a data frame defined, than Stata. Am I right if I think that this is because the object oriented nature of R and can

Hi, I am new to R, but a fairly `old' user of Stata. I read posts asking about survey methods and large datasets in the archive, so I will not ask those questions again. But some still remain: - R seems to consume more memory given the same set of data, say if I have only a data frame defined, than Stata. Am I right if I think that this is because the object oriented nature of R and can

Dear all, Using the tsls package, I noticed that regression lists longer than 64 character are getting truncated. Looking at the original source, tsls.formula <- function(model, instruments, data, subset, weights, na.action, contrasts=NULL){ if (missing(na.action)) na.action <- options()$na.action m <- match.call(expand.dots = FALSE) if (is.matrix(eval(m$data,

Dear all, Playing with a toy problem, I wondered how much it costs to generate `internal' objects in a function that are not strictly needed. An example: mean(apply(t(matrix(as.vector(x) - x0 / h, , nrow=nrow(x), ncol=ncol(x))), c(2) epakern, dim)) / h^dim It would be more _readable_ if I defined the object inside the apply(), stick it there and rm() after, but my feeling is that creating

Dear all, I am running platform i386-pc-linux-gnu arch i386 os linux-gnu system i386, linux-gnu status major 1 minor 3.0 year 2001 month 06 day 22 language R and have my own library path set up having R_LIBS='~/lib/R'; export

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

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

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'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

This data are kilojoules of energy that are consumed in starving fish over a time period (Days). The KJ reach a lower asymptote and level off and I would like to use a non-linear plot to show this leveling off. The data are noisy and the sample sizes not the largest. I have tried selfstarting weibull curves and tried the following, both end with errors. Days<-c(12, 12, 12, 12, 22, 22, 22,

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 R-masters, I have a problem with nls() and my research data. Look this example: X2000<-c(1.205268,2.850695,5.100860,8.571610,15.324513,25.468599,39.623418,61.798856,91.470006,175.152509) age<-c(37,42,47,52,57,62,67,72,77,82) fit <- nls(X2000~R*exp(A*age),start=list(R=.1,A=.1)) Error mensage: Error in nls(X2000 ~ R * exp(A * age), start = list(R = 0.1, A = 0.1)) :

Hello colleagues, I am attempting to determine the nonlinear least-squares estimates of the nonlinear model parameters using nls. I have come across a common problem that R users have reported when I attempt to fit a particular 3-parameter nonlinear function to my dataset: Error in nls(r ~ tlm(a, N.fix, k, theta), data = tlm.data, start = list(a = a.st, : step factor 0.000488281

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,

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,

Hi I?m trying to fit a nonlinear model to a derivative of the logistic function y = a/(1+exp((b-x)/c)) (this is the parametrization for the SSlogis function with nls) The derivative calculated with D function is: > logis<- expression(a/(1+exp((b-x)/c))) > D(logis, "x") a * (exp((b - x)/c) * (1/c))/(1 + exp((b - x)/c))^2 So I enter this expression in the nls function:

Hi, You might need to check library(boot).? I have never used that before.? So, I can't comment much.? It is better to post on R-help list.? I had seen your postings on Nabble in the past.? Unfortunately those postings were not accepted in R-help.? You have to directly post at ? r-help at r-project.org after registering at: https://stat.ethz.ch/mailman/listinfo/r-help ?

I'm running into problems trying to use the nls function to fit the some data. I'm invoking nls using nls(s~k/(a+r)^b, start=list(k=1, a=13, b=0.59)) but I get errors indicating that the step has been reduced below the minimum step size or an inifinity is generated in numericDeriv. I've tried to use a variety of starting values for a, b, k but get similar errors. Is there