similar to: R-help Digest, Vol 112, Issue 25

Dear R users I have tried to install the optimx but met problems. I have gone to the website you suggested: https://r-forge.r-project.org/R/?group_id=395 and tried to install it with the following method: install.packages("optimx", repos="http://R-Forge.R-project.org") I have received the following information: package 'numDeriv' successfully unpacked and MD5

> I have a problem. I am using the NLME library to fit a non-linear model. There is a linear component to the model that has a couple parameter values that can only be positive (the coefficients are embedded in a sqrt). When I try and fit the model to data the search algorithm tries to see if a negative value for one of these parameter values will produce an optimal fit. When it does so,

I'll not be able to comment on the use of C like this, but will warn that I wrote the routines that became Nelder-Mead, CG, and BFGS in optim() in the mid 1970s. CG never did as well as I would like, but the other two routines turned out pretty well. However, in nearly 40 years, there are a few improvements, particularly in handling bounds and masks (fixed parameters). For all-R routines see

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

Hello. I am still a newbie in R. Excuse me if I am asking something obvious. My efforts to get an answer through browsing the mailing archives failed. I want to perform an augmented Dickey-Fuller test and to obtain AIC and BIC and to be able to impose some linear restrictions on the ADF regression so as to decide the correct order of autoregression. However I could find no obvious way to impose

Dear list, In my previous email, the model I'd like to estimate is rea=a*st+b*mod+error, where a+b=1 and a,b>0. My apologies for the misunderstanding. Thanks for all your help, Jorge On Wed, Jul 23, 2008 at 3:35 PM, Jorge Ivan Velez <jorgeivanvelez@gmail.com> wrote: > > Dear list, > > I have a data set which looks like myDF (see below) and I'd like to >

I'm using nls to fit a variety of different models. Here I use SSgompertz as an example. I want the ability to fix one (or more) of the coefficients that would normally be optimised (e.g. fix b3=0.8). Examples; based on and using data from example(SSgompertz) #--------------------- # vanilla call to nls, no coefficients fixed, works fine nls(density ~ SSgompertz(log(conc), Asym, b2, b3),

Hi, all. I didn't find something similar to this problem in past list. I have a data frame (named restr) where some columns are factors, like you can see: > table(restr[,"p1"]) 0 1 2 3 4 5 0 26 1 0 1 0 > table(restr[,"p2"]) 0 1 2 3 4 5 6 0 13 11 1 2 1 0 When I use apply, the factor structure is missed: >

Hello, The likelihood includes two parameters to be estimated: lambda (=beta0+beta1*x) and alpha. The algorithm for the estimation is as following: 1) with alpha=0, estimate lambda (estimate beta0 and beta1 via GLM) 2) with lambda, estimate alpha via ML estimation 3) with updataed alpha, replicate 1) and 2) until alpha is converged to a value I coded 1) and 2) (it works), but faced some

Hi, I'm trying to get maximum likelihood estimates of \alpha, \beta_0 and \beta_1, this can be achieved by solving the following three equations: n / \alpha + \sum\limits_{i=1}^{n} ln(\psihat(i)) - \sum\limits_{i=1}^{n} ( ln(x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} 1/(psihat(i)) - (\alpha+1) \sum\limits_{i=1}^{n} ( 1 / (x_i + \psihat(i)) ) = 0 \alpha \sum\limits_{i=1}^{n} (

Dear All I'm attempting to speed up my model building procedure, but need some help with the loops I've created...please bear with me through the explanation! My basic model call is something like: m0sulf.nlme<-nlme(conc~beta0*exp(-beta1*day)+beta2*exp(-beta3*day), data=m0sulf, fixed=(beta0+beta1+beta2+beta3~1),

Hi all, I am trying to create simulated data for exploring reclassfication measures in a logistic setting with two continuous predictors and I would like to set the average population probability of outcome rather than the logistic beta0. Is there a way to find a beta0 that will generate the desired average population probability of outcome given x,y and their odds ratios? #Here is an

I use "while" loop but it produces an errro. I have no idea about this. Error in "[<-"(`*tmp*`, i, value = numeric(0)) : nothing to replace with The problem description is The likelihood includes two parameters to be estimated: lambda (=beta0+beta1*x) and alpha. The algorithm for the estimation is as following: 1) with alpha=0, estimate lambda (estimate beta0

Hi all-- We are planning an intervention study for adolescent alcohol use, and I am planning to use simulations based on a hurdle model (using the hurdle() function in package pscl) for sample size estimation. The simulation code and power code are below -- note that at the moment the "power" code is just returning the coefficients, as something isn't working quite right. The

To whom it may concern: I am trying to better understand the functionality of 'R' when making arima predictions to avoid any "Black Box" disadvantages. I'm fitting a seasonal arima model using the following command (having already loaded 'stat' package). arimaSeason <- arima(Data,order=c(1,0,1),seasonal=list(order=c(1,0,1),period=12)) I can then generate

Background: I administer a cluster of RedHat EWS 3U4 Linux workstations at a university. I built R 2.1.0 from source: ./configure \ --prefix=/sscc/opt/R-2.1.0 \ --with-blas=no \ 2>&1 \ | tee NUInstall.configure R is now configured for i686-pc-linux-gnu Source directory: . Installation directory: /sscc/opt/R-2.1.0 C compiler:

Dear Users, I am new to both of things, so do not blame me too much... I am busy with semiparametric regression and use WinBUGS to sample posteriors. The code to call Winbugs is as follows: data <- list("y","X","n","m") #My variables inits.beta <- rep(0,K) inits.beta0 <- 0 inits <-

Hi, Mark et al.: Thanks, Mark. Three comments: 1. Rvmmin was one of the methods I tried after Ravi directed me to optimx. It returned NAs for essentially everything. See my email of this subject stamped 4:43 PM Central time = 21:43 UTC. 2. It would be interesting to know if the current algorithm behind optim and optimx with

Hi Spencer: See the link below about L-BFGS-B below because I had problems with it a good while back (and I think the link description is the cause but I can't prove it ) so eventually I moved to the Rvmmin(b) package. It's a package but really an algorithm. Rvmmin(b) uses a variable-metric algorithm similar to that of L-BFGS-B but without the problem below. It's not surprisingly a

Hi R-users, I am trying to estimate function parameters using optim(). My count observations follows a Poisson like distribution. The problem is that I wanna express the lambda coefficient, in the passion likelihood function, as a linear function of other covariates (and thus of other coefficients). The codes that I am using (except data frame) are the following (FYI the parameters need to be