similar to: Complicated nls formula giving singular gradient message

Dear list, Has anyone created a version of SSmicmen that allows testing for group differences? The basic Michaelis-Menten equation is: (Bmax * X) / (Kd + X). The nlme package allows modeling of random effects for Bmax and Kd as needed, but I curious how I can build in group differences? I have receptor binding data for strains of mice, and following Pinheiro and Bates' lead in their

Dear R Helpers, I have attempted "optim" function to solve a two-dimensional optimization problem. It took around 25 second to complete the procedure. However, I want to reduce the computation time: less than 7 second. Is there any optimization function in R which is very rapid? Best Regards, Wonsang ----- Wonsang You Leibniz Institute for Neurobiology -- View this message in

There has recently been some discussion on the list about AD Model builder and the suitability of R for constructing the types of models used in fisheries management. https://stat.ethz.ch/pipermail/r-help/2006-January/086841.html https://stat.ethz.ch/pipermail/r-help/2006-January/086858.html I think that many R users understimate the numerical challenges that some of the typical

Simple typos, don't repost until there's more substantive feedback. On Thu, Apr 27, 2023 at 07:44:26PM +0900, Shunsuke Mie wrote: > The Linux PCIe Endpoint framework supports to implement PCIe endpoint > functions using a PCIe controller operating in endpoint mode. > It is possble to realize the behavior of PCIe device, such as virtio PCI > device. This patch introduces a

Dear R-users, Half a year ago we put out the R package "glmmADMB" for fitting overdispersed count data. http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html Several people who used this package have requested additional features. We now have a new version ready. The major new feature is that glmmADMB allows Bernoulli responses with logistic and probit links. In addition there

Hi I implemented the age-structure model in Gove et al (2002) in R, which is a nonlinear statistical model. However running the model in R was very slow. So Dave Fournier suggested to use the AD Model Builder Software package and helped me implement the model there. ADMB was incredibly fast in running the model: While running the model in R took 5-10 minutes, depending on the

Hi I am running an lmer which works fine with family=poisson mixed.model<-lmer(nobees~spray+dist+flwabund+flwdiv+round+(1|field),family="poisson",method="ML",na.action=na.omit) But it is overdispersed. I tried using family=quasipoisson but get no P values. This didnt worry me too much as i think my data is closer to negative binomial but i cant find any examples of

Hi R users, I have a question about adding uncertainty bars to stacked bar plots. DF: year A B C Amin Amax Bmin Bmax Cmin Cmax 2009 40 45 15 30 61 23 56 14 17 2010 36 41 23 26 54 22 51 22 24 I use the code below: DF.refm = melt(subset(DF[,c(1:4)]),id.vars='year',variable_name='Legend') fig1 =

> > Hi, > > I used > GDALinfo("MOD13Q1.A2001049.h13v11.005.2007002215512.250m_16_days_EVI.tif") and > got the results: > > rows 10 > columns 11 > bands 1 > origin.x 150701.4 > origin.y 7744897 > res.x 250 > res.y 250 > ysign -1 > oblique.x 0 > oblique.y 0 > driver GTiff >

Hello, I'm running a simulation study of a multilevel model with binary response using the binomial probit link. It is a random intercept and random slope model. GLMMPQL and lmer fail to converge on a *significant* portion of the *generated* datasets, while MlWin gives reasonable estimates on those datasets. This is unacceptable. Does anyone has similar experiences? Regards, Roel de

On 18/06/17 12:10, lily li wrote: > Hi R users, > > I have a question about adding uncertainty bars to stacked bar plots. > > DF: > year A B C Amin Amax Bmin Bmax Cmin Cmax > 2009 40 45 15 30 61 23 56 14 17 > 2010 36 41 23 26 54 22 51 22 24 > > I use the code below: > >

Hello, I am using odesolve to simulate a group of people moving through time and transmitting infections to one another. In Matlab, there is a NonNegative option which tells the Matlab solver to keep the vector elements of the ODE solution non-negative at all times. What is the right way to do this in R? Thanks, Jeremy P.S., Below is a simplified version of the code I use to try to do this,

To estimate saturation binding parameters Bmax and Kd in a receptor saturation binding experiment, I use the following nonlinear equation and the nls() function: bmax*X*dummy ------------ + ns*X + background = total binding kd+X where X is concentration, and dummy is an indicator to allow shared estimation of the nonspecific binding parameter ns. This equation describes two fitted

I am using the 'glmmPQL function in the 'MASS' library to fit a mixed effects logistic regression model to simulated data. I am conducting a series of simulations, and with certain simulated datasets, estimation of the random effects logistic regression model unexpectedly terminates. I receive the following error message from R: Error in lme.formula(fixed=zz + arm.long,random=~1 |

Has there been any follow up to this question? I have found myself wondering the same thing: How then does SAS fit a beta distributed GLMM? It also fits the negative binomial distribution. Both of these would be useful in glmer/lmer if they aren't 'illegal' as Brian suggested. Especially as SAS indicates a favorable delta BIC of over 1000 when I fit the beta to my data (could be the

Does someone have code, or point to a source to get it, to model repeated measures zero-inflated poisson data. The data come from a replicated field trial comparing two treatments - a control and a test treatment. Thanks in advance ------------------------------------ Subhash Chandra, DSc Senior Biometrician Primary Industries Research Victoria Department of Primary Industries 1 Ferguson Road

One of my colleagues has a data set from a two-level nested design from which we would like to estimate variance components. But we'd like some idea of what the inevitable outliers are doing, so we were looking for something in R that uses robust (eg Huber) treatment and returns robust estimates of variance. Nothing in my collection of R robust estimation packages (robust, robustbase and MASS

Dear R-users, A recent post (Feb 16) to R-help inquired about fitting a glmm with a negative binomial distribution. Professor Ripley responded that this was a difficult problem with the simpler Poisson model already being a difficult case: https://stat.ethz.ch/pipermail/r-help/2005-February/064708.html Since we are developing software for fitting general nonlinear random effects models we

Dear R People: When looking at the previous postings regarding TRAMO-SEATS, I am somewhat puzzled. Is it true that we CANNOT replicate TRAMO-SEATS because of licensing or ownership issues, please? If not, would anyone be interested in an R version of it, please? Thanks, Sincerely, Erin Hodgess Associate Professor Department of Computer and Mathematical Sciences University of Houston -

Hello, I am currently testing a Vector AR of dim 3 over not a lot of data (135 * 3 observations) . To test the adequation of my vecot ar, I use the Schwarz Bayesian Criterion and the classic modified Portmanteau test on the residuals (it can be found for instance in http://www.iue.it/PUB/ECO2004-8.pdf , page 15) -> the null hypothesis is "the residuals process are a vectorila white