similar to: nlme: Initial parameter estimates

Hi everyone, Does the selfstart function SSlogis of the "nlme" library allows the introduction of time varying covariates ? For example how can I interpret the xmid parameter (reperesenting the age at which we reach the half of the asymptote) if I want to explain it by a some time varying covariate? Thanks in adavance, Abderrahim Abderrahim Oulhaj, Phd in Statistics Oxford

Hi, I am looking for any procedure in R to fit "Latent variables models" in particular "Latent trait models (i.e. for binary data)". could anyone help me? Many thanks , Dr Abderrahim Oulhaj Oxford University Department of Pharmacology Mansfield Road Oxford OX1 3QT Tel: +44 1865 224098 Fax: +44 1865 224099 Email: abderrahim.oulhaj@pharmacology.oxford.ac.uk

Dear All, I wonder if there is an efficient way to fit the generalized linear mixed model for multivariate outcomes. More specifically, Suppose that for a given subject i and at a given time j we observe a multivariate outcome Yij = (Y_ij1, Y_ij2, ..., Y_ijK). where Y_ijk is a binomial(n_ijk, p_ijk). One way to jointly model the data is to use the following specification: g(p_ijk) =

Dear R help list, I am desperately looking for any reference explaining by examples how to write R codes in order to fit the parameters of a given model using maximum likelihood or any other criteria function. I know the general structure: First write a code for the maximum likelihood function and afterwards write a code to maximize it using optim and then invert the Hessian to get the

Dear all, I am looking for an R package that fits multivariate gaussian or non-gaussian longitudinal outcomes. I am especially interested to non-gaussian outcomes since the outcomes I've got are discrete (some are binomial and some are count data). Many thanks in advance, Abderrahim [[alternative HTML version deleted]]

Dear All, I wonder if there is a way to fit a generalized linear mixed models (for repeated binomial data) via a direct Maximum Likelihood Approach. The "glmm" in the "repeated" package (Lindsey), the "glmmPQL" in the "MASS" package (Ripley) and "glmmGIBBS" (Myle and Calyton) are not using the full maximum likelihood as I understand. The

Dear All, I have the following error message when I fitted lmer to a binary data with the "AGQ" option: Error in family$mu.eta(eta) : NAs are not allowed in subscripted assignments In addition: Warning message: IRLS iterations for PQL did not converge Any help? Thanks in advance, Abderrahim [[alternative HTML version deleted]]

Dear All, I wonder if there is an R package to estimate the generalized linear mixed models but with a random effects having a mixture of normals as a prior distributinon .. Thank you, Abderrahim [[alternative HTML version deleted]]

Dear R users, I have a question concerning the nltm package. Before posting to the R list I first contacted the author of the package twice but no succes. May be I've got the wrong email! My question is about the object "surv" given in the package "nltm". As explained, the object "surv" represents the MLE estimates of the baseline survival function evaluated at

Dear all, I didn't get any suggestion for my querry concerning the NLTM package so I am re-posting it hoping that someone can give me any clue. I apologize for doing so. Please find attached a copy of my previous email: Dear R users, I have a question concerning the nltm package. Before posting to the R list I first contacted the author of the package twice but no succes. May be I've

The code in X86TargetLowering::IsEligibleForTailCallOptimization() has this part: // The callee has to preserve all registers the caller needs to preserve. const X86RegisterInfo *TRI = Subtarget.getRegisterInfo(); const uint32_t *CallerPreserved = TRI->getCallPreservedMask(MF, CallerCC); if (!CCMatch) { const uint32_t *CalleePreserved = TRI->getCallPreservedMask(MF, CalleeCC);

Hello Mentors, I did analyze assembly files generated for IPRA + PGO. (1) I observed that I did not considered the scope of the optimization so changing callee saved register set for non local function is bad because IPRA can not pass this information to other modules. (2) applying this change to indirect function also has no effect because for such case IPRA is currently not able to propagate

----- Original Message ----- > From: "vivek pandya" <vivekvpandya at gmail.com> > To: "Hal Finkel" <hfinkel at anl.gov> > Cc: "llvm-dev" <llvm-dev at lists.llvm.org>, "Quentin Colombet" > <qcolombet at apple.com>, "Mehdi Amini" <mehdi.amini at apple.com> > Sent: Friday, July 29, 2016 5:02:44 AM >

On Fri, Jul 29, 2016 at 9:01 AM, Hal Finkel <hfinkel at anl.gov> wrote: > ----- Original Message ----- > > From: "vivek pandya" <vivekvpandya at gmail.com> > > To: "Mehdi Amini" <mehdi.amini at apple.com> > > Cc: "llvm-dev" <llvm-dev at lists.llvm.org>, "Hal Finkel" <hfinkel at anl.gov>, > "Quentin