similar to: R-beta: SEs for one-param MLE in R?

I have a question about passing arguments to the function f that nlm minimizes. I have no problems if I do this: x<-seq(0,1,.1) y<-1.1*x + (1-1.1) + rnorm(length(x),0,.1) fn<-function(p) { yhat<-p*x+(1-p) sum((y-yhat)^2) } out<-nlm(fn,p=1.5,hessian=TRUE) But I would like to define fn<-function(x,y,p) { yhat<-p*x+(1-p) sum((y-yhat)^2) } so

Dear Sir, I am using R to estimate two parameters in Normal distribution. I generated 100 normal distributed numbers, on which to estimate the parameter. The syntax is: >fn<-function(x)-50*log((y)^2)+50*log(2*pi)-(1/2*(z^2))*(sum((x-y)^2)) >out<-nlm(fn, x, hessian=TRUE) but it does not work. Could you please help me to compose the syntax for the purpose that find maximum

hello, I have got some trouble with R functions nlm(), nls() or optim() : I would like to fit 3 parameters which must stay in a precise interval. For exemple with nlm() : fn<-function(p) sum((dN-estdata(p[1],p[2],p[3]))^2) out<-nlm(fn, p=c(4, 17, 5), hessian=TRUE,print.level=2) with estdata() a function which returns value to fit with dN (observed data vactor) My problem is that only

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 R-friends, Attached is the SAS XPORT file that I have imported into R using following code library(foreign) mydata<-read.xport("C:\\ctf.xpt") print(mydata) I am trying to maximize logL in order to find Maximum Likelihood Estimate (MLE) of 5 parameters (alpha1, beta1, alpha2, beta2, p) using NLM function in R as follows. # Defining Log likelihood - In the function it is noted as

I've been working on a new package and I have a few questions regarding the behaviour of the nlm function. I've been (for better or worse) using the nlm function to fit a linear model without suppling the hessian or gradient attributes in the objective function. I'm curious as to why the nlm requires 31 iterations (for the linear model), and then it doesn't work when I try to add

Hello, I'm new to this and am trying to teach myself some R by plotting biological data. The growth curve in question is supposed to be fitted to the Verhulst equation, which may be transcribed as follows: f(x)=a/(1+((a-0.008)/0.008)*exp(-(b*x))) - for a known population density (0.008) at t(0). I am trying to rework the example from "An Introduction to R" (p. 72) for my case and

I am trying to do a MLE fit of the weibull to some data, which I attach. fitweibull<-function() { rt<-scan("r/rt/data2/triam1.dat") rt<-sort(rt) plot(rt,ppoints(rt)) a<-9 b<-.27 fn<-function(p) -sum( log(dweibull(rt,p[1],p[2])) ) cat("starting -log like=",fn(c(a,b)),"\n") out<-nlm(fn,p=c(a,b), hessian=TRUE)

GENERAL REFERENCE ON NONLINEAR OPTIMIZATION? What are your favorite references on nonlinear optimization? I like Bates and Watts (1988) Nonlinear Regression Analysis and Its Applications (Wiley), especially for its key insights regarding parameter effects vs. intrinsic curvature. Before I spent time and money on several of the refences cited on the help pages for "optim",

Hi R-help! I'm trying to understand how R's nlm function updates its estimate of the Hessian matrix. The Dennis/Schnabel book cited in the references presents a number of different ways to do this, and seems to conclude that the positive-definite secant method (BFGS) works best in practice (p201). However, when I run my code through the optim function with the method as "BFGS",

Hi, R developers. when running mle inside a loop I found a nasty behavior. From time to time, my model had a degenerate minimum and the loop just crashed. I tracked it down to "vcov <- if (length(coef)) solve(oout$hessian)" line, being the hessian singular. Note that the minimum reached was good, it just did not make sense to calculate the covariance matrix as the inverse of a

Dear R experts, In R-intro, under the 'Nonlinear least squares and maximum likelihood models' there are ttwo examples considered how to use 'nlm' function. In 'Least squares' the Standard Errors obtained as follows: After the fitting, out$minimum is the SSE, and out$estimates are the least squares estimates of the parameters. To obtain the approximate standard

Hello: Below is a toy logistic regression problem. When I wrote my own code, Newton-Raphson converged in three iterations using both the gradient and the Hessian and the starting values given below. But I can't get nlm() to work! I would much appreciate any help. > x [1] 10.2 7.7 5.1 3.8 2.6 > y [1] 9 8 3 2 1 > n [1] 10 9 6 8 10 derfs4=function(b,x,y,n) {

Hi R users I'm trying to fit a model y=ax^b. I know if I made ln(y)=ln(a)+bln(x) this is a linear regression. But I obtain differente results with nls() and lm() My commands are: nls(CV ~a*Est^b, data=limiares, start =list(a=100,b=0), trace = TRUE) for nonlinear regression and : lm(ln_CV~ln_Est, data=limiares) for linear regression Nonlinear

Dear expeRts, first of all I'd like to thank you for the quick help on my last which() problem. Here is another one I could not tackle: I have data on an absorption measurement which I want to fit with an voigt profile: fn.1 <- function(p){ for (i1 in ilong){ ff <- f[i1] ex[i1] <- exp(S*n*L*voigt(u,v,ff,p[1],p[2],p[3])[[1]]) } sum((t-ex)^2) } out <-

In the course of revising a paper I have had occasion to attempt to maximize a rather complicated log likelihood using the function nlm(). This is at the demand of a referee who claims that this will work better than my proposed use of a home- grown implementation of the Levenberg-Marquardt algorithm. I have run into serious hiccups in attempting to apply nlm(). If I provide gradient and

It would be really nice if nlm took a set of "..." optional arguments that were passed through to the objective function. This level of hacking is probably slightly beyond me: is there a reason it would be technically difficult/inefficient? (I have a vague memory that it used to work this way either in S-PLUS or in some previous version of R, but I could easily be wrong.) Here's

Dear people, I'm trying to do some analysis of a data using the models by Royle & Donazio in their fantastic book, particular the following function: http://www.mbr-pwrc.usgs.gov/pubanalysis/roylebook/panel4pt1.fn that applied to my data and in the console is as follows: > `desman.y` <- structure(c(3L,4L,3L,2L,1L), .Names = c("1", "2", "3",

Admittedly I am using an old version 1.7.1, but can anyone tell if this is or was a problem. I can only get nlm (nonlinear minimization) to adjust the first three components of function variable. No gradient or hessian is supplied. E.G.; fnoise function(y) { y[5]/(y[4]*sp2) * exp(-((x[,3]-y[1]-y[2]*x[,1]-y[3] *x[,2])/y[4])^2/2) + (1-y[5])/(y[9]*sp2) * exp(-((x[,3]-y[6]-y[7]*x[,1]-y[8]

I'm trying to fit a Weibull distribution to some data via maximum likelihood estimation. I'm following the procedure described by Doug Bates in his "Using Open Source Software to Teach Mathematical Statistics" but I keep getting warnings about NaNs being converted to maximum positive value: > llfunc <- function (x) { -sum(dweibull(AM,shape=x[1],scale=x[2], log=TRUE))} >