search for: reparametrizations

Hi, I am trying to fit a function of the form: y = A0 + A1 * exp( -0.5* ( (X - Mu1) / Sigma1 )^2 ) - A2 * exp ( -0.5* ( (X-Mu2)/Sigma2 )^2 ) i.e. a mean term (A0) + a difference between two gaussians. The constraints are A1,A2 >0, Sigma1,Sigma2>0, and usually Sigma2>Sigma1. The plot looks like a "Mexican Hat". I had trouble (poor fits) fitting this function to toy data

Hello All, I would like to optimize a (log-)likelihood function subject to a number of linear constraints between parameters. These constraints are equality constraints of the form A%*%theta=c, ie (1,1) %*% 0.8,0.2)^t = 1 meaning that these parameters should sum to one. Moreover, there are bounds on the individual parameters, in most cases that I am considering parameters are bound between zero

Hi all, I am wondering if anyone has implemented (or at least tried to) an automatic reparametrization in order to satisfy "trivial" constraints (in the sense of Dennis & Schnabel, 1983) in optimization problems. To be perhaps clearer let us consider a simple bi-exponential model for some recorded signal (sorry for the LaTex notations I hope they aren't too confusing): $s(t) = A

The documentation (in ver 1.1) for contrasts and contrasts<- does not list all the arguments for those functions. In addition to x, the factor whose contrasts are being extracted or set, contrasts() has the argument 'contrasts=TRUE', and contrasts<-() has the argument 'how.many'. It was this latter that had me flummoxed, because I wanted to reparametrize a model by

Hi all, I want to use weights for a logistic regression. In SAS, all I have to do is to specify my weight vector (they are fractions) and use proc logistic on my binary output. When I tried to do the same in R, I got an error message because my weights were not integer. I understand that the weight option in R is to be used when the dependent variable is a proportion so that the weight is the

I'm running a series of simple bivariate linear regressions on grouped data. I want to test the slopes to see if they are parallel. I normally use analysis of covariance to do so, looking at interaction between the covariate and the factor to make this determination. VR3 pp.149 - 154 has a very nice example of an ANOCOVA, ending with a discussion of this very operation. My question has

Hi R-list, I am new to optimization in R and would appreciate help on the following question. I would like to minimize the following function using two constraints: ###### fn <- function(par,H,F){ fval <- 0.5 * t(par) %*% H %*% par + F%*% par fval } # matrix H is (n by k) # matrix F is (n by 1) # par is a (n by 1) set of weights # I need two constraints: # 1.

Dear all: First, apologies for cross-posting multiplicities and for a query that is more analytically related than S-language related. The bottom-line wish is: Could you please provide and advice, references, etc on S software approaches for fitting a distribution with density: p*g(x) + (1-p)*f(x) where g(x) is the familiar lognormal 2-parameter density and f(x) is Pareto as defined below?

> y [1] 1 11 42 64 108 173 214 > t [1] 1 2 3 4 5 6 7 > nls(1/y ~ c*exp(-a*b*t)+1/b, start=list(a=0.001,b=250,c=5), trace=TRUE) 29.93322 : 0.001 250.000 5.000 Error in numericDeriv(form[[3]], names(ind), env) : Missing value or an infinity produced when evaluating the model # the start value for b is almost close to final estimates, # a is usually

Hi, I'm having trouble using the constrOptim function to generate the 9-component vector argmin of the function ELfsds: ELfsds <- function(pvechat){ LG=0 for(i in 1:9){ LG=LG+log(pvechat[i]) } return(-LG) } with accompanying gradient function: gradfunc <- function(thetavec){ g=1/(9*thetavec) return(g) } The constraints on the optimization problem are: 1 - components of

Given X1,...,Xn ~ t_k(mu,sigma) student t distribution with k degrees of freedom, mean mu and standard deviation sigma, I want to obtain the MLEs of the three parameters (mu, sigma and k). When I try traditional optimization techniques I don't find the MLEs. Usually I just get k->infty. Does anybody know of any algorithms/functions in R that can help me obtain the MLEs? I am especially

...le" limits. They don't need to be too tight, just enough to keep the parameters from giving a silly objective function 2) do some evaluations of the objective to make sure it is really being properly calculated. Never hurts to have some "known" outcomes. Beyond this, we get into reparametrizations. Great idea, but far too much work for most of us, even if we work in the field. Best, JN On 01/17/2011 06:00 AM, r-help-request at r-project.org wrote: > From: Uwe Ligges <ligges at statistik.tu-dortmund.de> > To: Jinrui Xu <jinruixu at umich.edu> > Cc: r-help at r-project...

...le" limits. They don't need to be too tight, just enough to keep the parameters from giving a silly objective function 2) do some evaluations of the objective to make sure it is really being properly calculated. Never hurts to have some "known" outcomes. Beyond this, we get into reparametrizations. Great idea, but far too much work for most of us, even if we work in the field. Best, JN On 01/17/2011 06:00 AM, r-help-request at r-project.org wrote: > From: Uwe Ligges <ligges at statistik.tu-dortmund.de> > To: Jinrui Xu <jinruixu at umich.edu> > Cc: r-help at r-project...

Dear list, I'm currently writing a C code to compute the (composite) likelihood - well this is done but not really robust. The C code is wrapped in an R one which call the optimizer routine - optim or nlm. However, the fitting procedure is far from being robust as the parameter space depends on the parameter - I have a covariance matrix that should be a valid one for example. Currently,

Hi all, I did some analysis using package R2WinBUGS to call WinBUGS. I set the iterations to 50000 (fairly a large number, I think), but after the program was done, the effective posterior samples contained only 7 draws. I don't know why. By the way, I checked posterior sample size by using bugsobj$n.sims. And, for my previous practice with WinBUGS/R2WinBUGS, no such strange thing happend.

Hello everyone, I hope this is a fair enough question, but I don’t have access to a copy of Bates and Pinheiro. It is probably quite obvious but the answer might be of general interest. If I fit a fixed effect with an added quadratic term and then do it as an orthogonal polynomial using maximum likelihood I get the expected result- they have the same logLik.

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Dear all, I'm trying to use the "optim" function to replicate the results from the "glm" using an example from the help page of "glm", but I could not get the "optim" function to work. Would you please point out where I did wrong? Thanks a lot. The following is the code: # Step 1: fit the glm clotting <- data.frame( u =

I have to estimate the following model for several group of observations : y(1-y) = p[1]*(x^2-y) + p[2]*y*(x-1) + p[3]*(x-y) with constraints : p[1]+p[3] >= 1 p[1]+p[2]+p[3]+1 >= 0 p[3] >= 0 I use the following code : func <- sum((y(1-y) - p[1]*(x^2-y) + p[2]*y*(x-1) + p[3]*(x-y))^2) estim <- optim( c(1,0,0),func, method="L-BFGS-B" , lower=c(1-p[3], -p[1]-p[3]-1,

Dear R users, I?m a graduate students and in my master thesis I must obtain the values of the parameters x_i which maximize this Multinomial log?likelihood function log(n!)-sum_{i=1]^4 log(n_i!)+sum_ {i=1}^4 n_i log(x_i) under the following constraints: a) sum_i x_i=1, x_i>=0, b) x_1<=x_2+x_3+x_4 c)x_2<=x_3+x_4 I have been using the ?ConstrOptim? R-function with the instructions