similar to: Hessian in constrOptim

Dear R users, I am using the function "constrOptim" to minimize the -1*log-likelihood where \beta_i>=0 i=1,...,p and \beta_0 is unconstrained. I construct u_i as 0 0 0 ... 0 0 1 0 ... 0 0 0 1 ... 0 . . . ... 0 . . . ... 0 .

Dear R-users, We'd like to announce the release of our new package "grouped" (available from CRAN), for fitting models for grouped or coarse data, under the Coarsened At Random assumption. This is useful in cases where the true response variable is known only up to an interval in which it lies. Features of the package include: power calculations for two-group comparisons,

Dear R-users, We'd like to announce the release of our new package "grouped" (available from CRAN), for fitting models for grouped or coarse data, under the Coarsened At Random assumption. This is useful in cases where the true response variable is known only up to an interval in which it lies. Features of the package include: power calculations for two-group comparisons,

Hi Is there any way to fit a bivaraite longitudinal mixed model using R. I have a data set with col names resp1 (Y_ij1), resp2 (Y_ij2), timepts (t_ij), unit(i) j=1,2,..,m and i=1,2,..n. I want to fit the following two models Model 1 Y_ij1, Y_ij2 | U_i = u_i ~ N(alpha + u_i + beta1*t_ij, Sigma) U_i ~ iid N(0, sigu^2) Sigma = bivariate AR structure alpha and beta are vectors of order 2.

i want to ask 2 questions. 1) can R do Random-effects GLS regression which i can get from Stata? the following result is frome Stata.can I get the alike result from R? xtreg lwage educ black hisp exper expersq married union, re Random-effects GLS regression Number of obs = 4360 Group variable (i) : nr Number of groups = 545 R-sq:

Hi Folks, I am dealing with data which have been presented as at each x_i, mean m_i of the y-values at x_i, sd s_i of the y-values at x_i number n_i of the y-values at x_i and I want to linearly regress y on x. There does not seem to be an option to 'lm' which can deal with such data directly, though the regression problem could be algebraically

Dear People, I have a vexing problem related to pseudo-random number generation, and would appreciate any help and advice. This problem is not directly related to R, and the only reason I am posting it to this list is that my implementation is using R. Let me describe my problem by giving an example, that is close to what I am trying to do. Suppose we are given a stream of pseudo-random numbers,

Hallo everybody, I'm new in r and I"ll appreciate some help! I have a matrix of nrow=30 and ncoll=54,and I would like to generate 50 simulations with tha same size of the matrix!!!That is to say that I want to generate 50 matrices -for my 50 simulations - with the same dimensions! I took my 1st matrix according to the formula that I want to implement: D<-mean_m + U_i*mat_DELTA

Dear R users, I am writing to you because I have a few question on how to fix the error term variances in lme in the hope that you could help me. To my knowledge, the closest possibility is to fix the var-cov structure, but not the whole var-cov matrix. I found an old thread (a few years ago) about this, and it seems that the only alternative is to write the likelihood down and use optim or a

Dear helpers, I am using the function "constrOptim" to estimate a model with ML with an inequality constraint using the option method='Nelder-Mead'. When I specify the option: hessian = TRUE I obtain the response: Error in f(theta, ...) : unused argument(s) (hessian = TRUE) I guess the function "constrOptim" does not allow this argument which, on the other hand, is

Dear Helper, I used "constrOptim.nl" and got the value of par. The estimations looks good even if the number of iterations is only 16. But the values of hessian and convergence are both "NULL". I tested the objective function and gradient function by "optim" and didn't see any problem there. With these functions, "optim" gives the convergence value

Hi, Use the `auglag' function in "alabama" if you want to get the Hessian at convergence. This typically tends to perform better than `constrOptim.nl'. Also, `constrOptim.nl' does not compute the Hessian. You should not specify method="L-BFGS-B". The default method "BFGS" is better in this setting. Hope this helps, Ravi

Hi, Is there any way to get the hessian matrix from the "constrOptim" function without supplying gradient function? Thanks. --------------------------------- Bring words and photos together (easily) with [[alternative HTML version deleted]]

Hi, i have made a code for optimizing a function using "constrOptim". i need hessain matrix of the parameters. how could i get hessain matrix when i will use "constrOptim"? May i get get any help from anyone? thank you in advance. Kanak Choudhury. [[alternative HTML version deleted]]

a simple question how does one produce plots on two different graphics devices? / allan

Dear all, I need to use R for one estimation, and i have readily available stata command, but i need also the R version of the same command. the estimation in stata is as following: 1. Compute mean values of relevant variables . sum inno lnE lnM Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------

Hi.., is there any funcrion in R to fit a periodic B-spline Surface Harsh --------------------------------- Brings words and photos together (easily) with [[alternative HTML version deleted]]

Dear R-users Coming from a proc mixed (SAS) background I am trying to get into the use of (n)lme. In this connection, I have some (presumably stupid) questions which I am sure someone out there can answer: 1) With proc mixed it is easy to get a hold on the estimated variance parameters as they can be put out into a SAS data set. How do I do the same with lme-objects? For example, I can see the

From: saint-filth@hotmail.com To: saint-filth@hotmail.com Subject: RE: Date: Wed, 9 Jun 2010 09:53:20 +0000 OK! sorry thats my fault, here the translations of the stata commands 1st step is to get the mean values of the variables, well that doesnt need explanation i guess, 2nd step is to estimate the model on panel data estimation method which is:

Thanx for your response, yeah, i know i didnst specified the indexes when i wrote the 2nd mail, in fact in the 1st mail i wrote already that i dont have problem with the estimation of the model... thats the reason why i didnt write in fact since the issue is not to estimate the model but to get the marginal effect, anyway, i figured out that predict(), doesnt work for panel data... and well, my