search for: u_ies

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.

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

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:

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

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 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

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 -------------+--------------------------------------------------------

In a classical meta analysis model y_i = X_i * beta_i + e_i, data {y_i} are assumed to be independent effect sizes. However, I'm encountering the following two scenarios: (1) Each source has multiple effect sizes, thus {y_i} are not fully independent with each other. (2) Each source has multiple effect sizes, each of the effect size from a source can be categorized as one of a factor levels

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,

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 .

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:

Dear R-users, In the function constrOptim there is an option to get an approximation to the hessian of the surrogate function R at MLE by declaring hessian=TRUE in the calls to the function optim. I would like to ask if it is advisable to get an approximate hessian for the funcrion f as follows: f''(theta)=R''(theta|theta_k)-B''(theta) where

The compositional data xi=(x_i1,x_i2,...,x_in), for each fixed i , xij>0, and sum(xij)=1; I want to compare the mean( u_i) of several groups i.e. H0: u_1=u_2=...=u_N or H0: u_11=u_21=...=u_N1 Are there any ANOVA tpye tools to do this work in R? Thanks, WEN S Q [[alternative HTML version deleted]]

Hi, I have a right-censored (positive) random variable (e.g. failure times subject to right censoring) that is observed for N subjects: Y_i, I = 1, 2, ..., N. Note that Y_i = min(T_i, C_i), where T_i is the true failure time and C_i is the censored time. Let us assume that C_i is independent of T_i. Now, I would like to generate another random variable U_i, I = 1, 2, ..., N, 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

Dear all, My question might be more of a statistics question than a question on R, although it's on how to apply the 'quantreg' package. Please accept my apologies if you believe I am strongly misusing this list. To be very brief, the problem is that I have data on only a random draw, not all of doctors' patients. I am interested in the, say, median number of patients of

Dear useRs (called Frank Harrell, most likely), after having preached for years to my medical colleagues to be cautious with stepwise selection procedures, they chanted back asking for an alternative when using mixed models. There is a half dozen laXXX packages around for all types of linear models, but as far I see there is none for mixed models such as lme. Even boot.stepAIC (which I

Dear R users, I am a mathematics postdoc at UC Berkeley. I have written a package in a Computational Algebra System named Singular http://www.singular.uni-kl.de to compute the Maximum Likelihood of a given probability distribution over several discrete random variables. This package gives exact answers to the problem. But more importantly, it gives All MLE solutions. My understanding is that

The compositional data xi=(x_i1, x_i2,..., x_in), for each fixed i , xij>0, and sum(xij)=1; I want to compare the mean( u_i) of several groups i.e. H0: u_1=u_2=...=u_N or Hj0: u_1j=u_2j=...=u_Nj Are there any ANOVA tpye tools to do this work in R? Thanks, WEN S Q [[alternative HTML version deleted]]

...I'm hoping someone can help shed some light on this. I simply want to get asterisk/sangoma to send the same signalling that Paetec does. I am contacting them for details on the setup they are using also. http://www.dialogic.com/manuals/docs/isdn_api_v5.pdf The Dialogic system is using the U_IES[1] with cc_GetCallInfo() to get the information sent from Paetec, our provider, but the information isn't being sent the same way from the Sangoma card, even though I have it set to pri_net in zapata.conf. Alternatively, we can check the UUI[2] message to see if the Dialogic card will receive...