similar to: R-beta: non-linear optimization in R

Could someone please point me to the documentation for using R SCHLIB. This (utility?) was mentioned in the R FAQ for creating shared libraries. Thanks in advance. -Matt mkramer at census.gov -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

I have a question about running an optimization function on an existing LOESS function defined in R. I have a very large dataset (1 million observations) and have run a LOESS regression. Now, I want to run a Newton-Raphson optimization to determine the point at which the slope change is the greatest. I am relatively new to R and have tried several permutations of the maxNR and nlm functions with

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",

Good day, Here I was trying to write a code for Garch(1,1) . As garch problem is more or less an optimization problem I also tried to get the algorithm for "nlminb" function. What I saw that if use this function 'nlminb" I can easyly get the estimate of parameters. But any other function is not working. I tried to write my own code for optimization using Quasi-Newton

Dear, R experts. I have two program codes, one is made by Splus and the other is made by transferring from Splus code. Because "nlminb" function in Splus is equivalent to "optim" in R, I expected to get exactly same result. But, sometime there is too large differece (greater than 2%) between two outputs. I looked two help files. According to those, in Splus, quasi-Newton

Hi, as you can see in the topic, I am trying to fit a normal mixture distribution with the approach suggested by Hamilton (1991). Since I couldn't find any existing packages including the quasi-bayesian mle, I have to write my own function. Unfortunately, I have absolutely no experience in doing this. If you're not familiar with the QB-MLE, I attached the formula as pdf. The idea

Craig A Faulhaber wrote: >I am interested in using a generalized linear mixed model with data > that best fits a beta distribution (i.e., the data is bounded between > 0 and 1 but is not binomial). .. >For clarification, here's what I'm trying to model: >I have a beta-distributed response variable (y). I have a fixed-effect >explanatory variable (treatment),

I have not included the previous postings because they came out very strangely on my mail reader. However, the question concerned the choice of minimizer for the zeroinfl() function, which apparently allows any of the current 6 methods of optim() for this purpose. The original poster wanted to use Newton-Raphson. Newton-Raphson (or just Newton for simplicity) is commonly thought to be the

Hi all; I'm trying to fit a reparameterization of the assymptotic regression model as that shown in Ratkowsky (1990) page 96. Y~y1+(((y2-y1)*(1-((y2-y3)/(y3-y1))^(2*(X-x1)/(x2-x1))))/(1-((y2-y3)/(y3-y1))^2)) where y1,y2,y3 are expected-values for X=x1, X=x2, and X=average(x1,x2), respectively. I tried first with Statistica v7 by LS and Gauss-Newton algorithm without success (no

Oups, I sent the email by error, as I was still writing my reply… Spencer, Le 22-avr.-09 à 03:33, spencerg a écrit : > Is your first model a special case of the second with eta1 = 0? > If yes, what about using 2*log(likelihood ratio) being approximately > chi-square? Yes, the first model is a special case of the second with eta1=0… Could you give me more explanation about

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) {

Dear R-ians, I want to perform an linear unmixing of image pixels in fractions of pure endmembers. Therefore I need to perform a constrained linear least-squares problem that looks like : min || Cx - d || ? where sum(x) = 1. I have a 3x3 matrix C, containing the values for endmembers and I have a 3x1 column vector d (for every pixel in the image). In theory my x values should all be in the

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

Hi, I'm using a non linear model to fit experimental survival curves. This model describes the fraction of "still active" experiments as a function of time t as follows: f(t)=(1+exp(-etaD*cD)) / (1+exp(etaD(t-cD))) Moreover, when experiments are still active, they may change of state (from 0 to 1). But they may fall inactive before changing their state (their state still

Hi, Is there available package on the optimization function using Newton-Raphson method (iterative quadratic approximation)? I have been using the 'optim' function in R and found it really unstable (it depends heavily on the initial values and functional forms). If I have to code it by myself, can I get some advice on how to start (any good reference or sample code)? I really

I would like to solve the equation is is the sum from k = i to N of choose(N,k) * MR ^ k * (1 - MR) ^ (N - k) - 0.50 = 0 I want to solve for MR. This seems like a non-linear equation to me. But I am having a hard time writing the function that implements the above. I could use 'for(...) as a brute force appoarch but I would like a more "elegant" solution. The variables 'N'

>>>>> "millanti" == millanti <millanti at libero.it> writes: millanti> Hi, I'm an R user and I have to answer a question millanti> about a problem I'm not able to solve by R code: millanti> the command nlm(....) to fit a non linear model millanti> requires an initial estimation of the model's millanti> unknown

I am trying to use the function "nlm" to find the mle. I want to use a generic function for the likelihood which would require me to use both the parameters and the data as arguments. But nlm requires the function to have only the parameters as arguments for this function (see example below). > testfun <- function(x,y) sum((x-y)^2) # x - parameters, y - data >

Hi, I have a quick question regarding estimation of a truncation regression model (truncated above at 1) using MLE in R. I will be most grateful to you if you can help me out. The model is linear and the relationship is "dhat = bhat0+Z*bhat+e", where dhat is the dependent variable >0 and upper truncated at 1; bhat0 is the intercept; Z is the independent variable and is a uniform

I am running a 3-parameter nonlinear fit using the default Gauss-Newton method of nls. initialValues.L = list(b=4,d=0.04,t=180); fit.nls.L = nls( myModel.nlm , fData.L, start = initialValues.L, control = nls.control(warnOnly = TRUE), trace=T ); summary.nls.L = summary(fit.nls.L); I run the same analysis in SAS proc NLIN. proc nlin data=apples outest=a; parms b=4 d=.04 t=180; model Y =