search for: nlmixed

Hi, I am trying to convert the following NLMIXED code to NLME, but am running into problems concerning 'Singularity in backsolve'. As I am new to R/S-Plus, I thought I may be missing something in the NLME code. NLMIXED *********** proc nlmixed data=kidney.kidney; parms delta=0.03 gamma=1.1 b1=-0.003 b2=-1.2 b3=0.09 b4=0.35 b5=-1.43 varu=...

To account for likely differences between families in naturalization rates, we fitted a generalized linear mixed model, using PROC NLMIXED in SAS10, with the naturalization rate per genus (that is, the number of naturalized species in a genus as a proportion of the total number of introduced species in a genus) as the response variable, a variable coding genera as containing at least one native species or not as a fixed-effect predict...

...and later to the help-list and no answer could fully illuminate my question. So Im trying again with a more specific matter. Im trying to work on a script (function) to analyse data from a diagnostic test meta-analysis with random effects. This was first described by an author using SAS witn PROC NLMIXED. Im not an expert in R and much worst in SAS. So my obstacle right now how to translate this syntax, specially to choose the correct function in R that better fit this SAS syntax with PROC NLMIXED. nlme or lme4? or they both would work well? Original SAS syntax (if it makes any difference?).......

...bit more straightforward. Unfortunately, the code is in SAS and I'm not sure how to re-write it for something like nlme (if at all possible - with conditions etc). Does anyone know both languages enough to try translating it? Would very much appreciate your help! The code is as follows: proc nlmixed data=mydata; parms b0_f=0 b1_f=0 b0_h=0 b1_h=0 log_theta=0; eta_f = b0_f + b1_f*x1 ; p_yEQ0 = 1 / (1 + exp(-eta_f)); eta_h = b0_h + b1_h*x1; mu = exp(eta_h); theta = exp(log_theta); r = mu/theta; if y=0 then ll = log(p_yEQ0); else ll = log(1 -...

Hello! Can anyone help me. I am using the posterior.mode from the result of glmmML. It apears to be different from the BLUe estimate of the RANDOM statement in PROC NLMIXED in SAS. Why is that? Thank you Ronen [[alternative HTML version deleted]]

Dear All, reading an article by Rodolphe Thiebaut and Helene Jacqmin-Gadda ("Mixed models for longitudinal left-censored repeated measures") I have found this program in SAS proc nlmixed data=TEST QTOL=1E-6; parms sigsq1=0.44 ro=0.09 sigsq2=0.07 sigsqe=0.18 alpha=3.08 beta=0.43; bounds $B!](B1< ro < 1, sigsq1 sigsq2 sigsqe >= 0; pi=2*arsin(1); mu=alpha+beta*TIME+a i+b i*TIME; if OBS=1 then ll=(1/(sqrt(2*pi*sigsqe)))*exp(-(RESPONSE-mu)**2/(2*sigsqe)); if OBS=0 then ll=probn...

Hi everybody: I?m trying to rewrite some routines originally written for SAS?s PROC NLMIXED into LME4's glmer. These examples came from a paper by Nelson et al. (Use of the Probability Integral Transformation to Fit Nonlinear Mixed-Models with Nonnormal Random Effects - 2006). Firstly the authors fit a Poisson model with canonical link and a single normal random effect bi ~ N(0;Sigma^...

...y. Today, one of them made the assertion that he believes the numerical algorithms in SAS are superior to those in Splus and R -- ie, optimization routines are faster in SAS, the SAS Institute has teams of excellent numerical analysts that ensure its superiority to anything freely available, PROC NLMIXED is more flexible than nlme( ) in the sense that it allows a much wider array of error structures than can be used in R/Splus, &etc. I obviously do not subscribe to these views and would like to refute them, but I am not a numerical analyst and am still a novice at R/Splus. Do there exis...

...already. I've found some commentary about methods of fitting ordinal logistic regression with other procedures, however, and I would like to ask for your advice and experience with this. In this article, Ching-Fan Sheu, "Fitting mixed-effects models for repeated ordinal outcomes with the NLMIXED procedure" Behavior Research Methods, Instruments, & Computers, 2002, 34(2): 151-157. the other gives an approach that works in SAS's NLMIXED procedure. In this approach, one explicitly writes down the probability that each level will be achieved (using the linear predictor and const...

...Users of Nonlinear Mixed Effects Models Know Whether Their Software Really Works? Lesaffre et. al. (Appl. Statist. (2001) 50, Part3, pp 325-335) analyzed some simple clinical trials data using a logistic random effects model. Several packages and methods MIXOR, SAS NLMIXED were employed. They reported obtaining very different parameter estimates and P values for the log-likelihood with the different packages and methods. We thought it would be interesting to revisit this example using the AD Model Builder random effects module which we feel is the mos...

Hi there, I am using SAS Proc NLMIXED to maximize a likelihood with multivariate normal random effects. An example is the two part random effects model for repeated measures semi-continous data with a cluster at 0. I use the "model y ~ general(loglike)" statement in Proc NLMIXED, so I can specify a general log likelihood...

...Today, one of them made the assertion that he believes the numerical algorithms in SAS are superior to those in Splus and R -- ie, optimization routines are faster in SAS, the SAS Institute has teams of excellent numerical analysts that ensure its superiority to anything freely available, PROC NLMIXED is more flexible than nlme( ) in the sense that it allows a much wider array of error structures than can be used in R/Splus, &etc. I obviously do not subscribe to these views and would like to refute them, but I am not a numerical analyst and am still a novice at R/Splus. Do there exis...

...quot;Matrix")) : Leading minor of order 2 in downdated X'X is not positive definite Similar problem if I use quasibinomial. If I use glm, of course it thinks I have roughly twice the number of subjects so the standard errors would be smaller than they should be. I used SAS's NLMIXED and it converged without problems giving me parameter estimates close to what glm gives, but with larger standard errors. glmmPQL(MASS) gives very different parameter estimates. Is it reasonable to fit a mixed effects model in this situation? Is there some way to give starting values for lmer and...

Hi Folks, I'm seeking confirmation of something which is probably true but which I have not managed to find in the documentation. I have a binary response y={0.1} and a variable x and have fitted a probit response to the data with f <- glm( y~x, family=binomial(link=probit) ) and then, with a specified set of x-value X I have used the predict.glm function as p <- predict( f, X,

...ites (but the standard errors given by lmer are definitely larger - which would seem reasonable). The se's for the fixed factors differ slightly between the two models. Note also that the estimated random effect sd for siteid intercept is identical for both models. I ran both models using PROC NLMIXED in SAS. It gives be similar estimates but not identical for the fixed effects and random effects. The confidence intervals on the random effects for each site are very large. Am I getting these results because I don't really need to fit a random effect for siteid? The estimated random effects...

Hi, I was very much hoping someone could help me with the following. I am trying to convert some SAS NLMIXED code to NLME in R (v.2.1), but I get an error message. Does anyone have any suggestions? I think my error is with the random effect "u" which seems to be parametrized differently in the SAS code. In case it's helpful, what I am essentially trying to do is estimate parameters using ML...

...mixed models) to data that includes censoring. I've done some searching already on CRAN and through the mailing list archives, but haven't discovered anything. Since I may well have done a poor job searching I thought I'd ask here prior to giving up. I understand that SAS's proc nlmixed can accomodate censoring (though proc mixed apparently can't), so if I can't find something available in R, I'll have to break down and use that. Please, save me from having to use SAS! Thanks much, Doug

Hello, ? I believe that in R, it is not possible to analyze mixed effect-models when the distribucion is not gaussian (p.e. binomial or poisson), isn't? ? Somebody can suggest me alternative? ? thanks ? xavi ? -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

...limits in R (which I think is a 2gig max according to the FAQ for windows). So, to make this computationally feasible, I had to sample from my very big data set and then run the analysis. Even still, it would take something on the order of 45 mins to 1 hr to get parameter estimates. (BTW, SAS Proc nlmixed was even worse and kept giving execution errors until the data set was very small and then it ran for a long time) However, I just ran the same analysis on the Ubuntu machine with the full, complete data set, which is very big and lmer gave me back parameter estimates in less than 5 minutes. Bec...

Hello! I am using glmmML for a logitic regression with random effect. I use the posterior.mode as an estimate for the random effects. These can be very different from the estimates obtained using SAS , NLMIXED in the random with out= option. (all the fixed and standard error of random effect estimators are almost identical) Can someone explain to me why is that. The codes I use: R: glmm1<-glmmML(mort30 ~ x , data=dat2,cluster=hospital,family=binomial) print(sort(glmm1$posterior.mode)) SAS: * proc*...