similar to: Question about correct syntax to use with nlme / nlmer

Hi, I have R 2.6.0 with updated lme4 and Matrix packages, and I am trying to fit a nonlinear multilevel model. I get the following error message: Error in nlmer(f ~ grModel(x, w, Tmin, Tmax, Topt, kopt, m) ~ kopt | flat, : gradient attribute of evaluated model must be a numeric matrix and I wonder what this may indicate. The nonlinear model I try to fit is as follows: > grModel

I am trying to explore nlmer by running some nlme examples from Pinheiro & Bates (2000). I do not seem to find information how to specify fixed effects covariates to nlmer models. Specifically, I tried to run the "Carbon Dioxide Uptake" example from p. 368 onwards in the PB200 book. The model without fixed effects covariates runs well but how to tell nlmer to include Type and

Hello, I'm trying to adjust a non linear model in which the biological response variable (ratio of germinated fungus spores) is dependent on 2 covariates (temperature and time). The response to temperature is modeled by a kind of beta function with 2 parameters (optimal and maximum temperatures) and the time function is a 2-parameter Weibull. Adjustments with nls or gnls work, but I need to

I am getting an error message in a call to nlme and cannot understand what is happening. I explain the steps below in the hope that someone can explain the error and how to correct it. STEP 1: Data set: name: marouane.data. This is a data frame whose first few lines are as follows: > marouane.data[1:13,] species plant leaf irradiance photosynthesis chlorophyll 1

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 all, I'm looking for smooth monotone estimation packages, preferably using splines. I downloaded the 'cobs' package and intend to use it, but since it offers only quadratic splines based on L1 minimization, I'd like to compare its performance to that of a more 'mainstream' cubic-spline, L2-norm minimizing spline. Preferably a smoothing spline. Does anyone know of such

Hi Rick Thanks to Dieter Menne I did manage to solve the problem of imposing bounds on the parameter space duirng an nlsList fit. He suggested using optim to optimize the parameters prior to each fit. This worked well for me as I had a customized selfStart function that then optimized the parameters for each individual separately. if you rewrote your selfStart function as: powrDpltInit <-

Hi all, I want to create a rather standard overlaid qqnorm plot on a single variable, with different subgroups of the same dataset plotted using different colors/symbols/etc. (I don't want side-by-side, rather different-colored curves on the same graph) I managed to do it rather tediously using "lapply" and "split", and wondered whether there is any single-command

Hi All. I'd like to send some control arguments to the nls function when performing a nlsList analysis. I'm fitting a power model to some grouped data and would like to impose lower bounds on the estimates using the "port" algorithm. Obtaining the lower bound constraint works fine with a direct call to nls for a single level of the grouping variable. ?However, the bounds

Hi, I have great problems with my work in R. I look for to model the growth of fish. I have "Longitudinal data", a serie of repeated measures for each individual. Using the corresponding packages "nlme" in R. I treat to fit to the data different growth functions, wich were entered by me. Unfortunately for no it was arrived at the convergence, several error messages appeared. I

Hi All I'm analysing growth rates using a gompertz (logistic) curve and am attempting to fit parameters for all of my study birds using nlsList. I've been looking for an option in nlsList to set min and max limits for parameter values during estimation, although I have failed to find any under 'control'. Other users seem to have reported that using control in the port algorithm

Hello, I'm having problems creating and using a selfStart model with nlme. Briefly, I've defined the model, a selfStart object, and then combined them to make a selfStart.default model. If I apply getInitial to the selfStart model, I get results. However, if I try usint it with nls or nlsList, these routines complain about a lack of initial conditions. If someone could point out

I found this by accident yesterday. The program that crashes is the first two lines of the example from the help page for nlmer. That example hasn't changed in a long time, so I assumed that it is an R-devel issue. It could also be a long latent nlmer bug. The second run with valgrind is puzzling. Terry T. > library(lmer) > sessionInfo() R Under development (unstable)

Thanks, posted to https://github.com/lme4/lme4/issues/412 for further discussion ... On Wed, Feb 22, 2017 at 10:03 AM, Therneau, Terry M., Ph.D. <therneau at mayo.edu> wrote: > I found this by accident yesterday. The program that crashes is the first > two lines of the example from the help page for nlmer. That example hasn't > changed in a long time, so I assumed that it is

Hello, I am trying to fit a nlme (non linear mixed effect). I am using the SelfStart function SSlogis. However the data in my hand contains few observations per subject (4 or less), so the nlsList doesn't work... In this case I should fixe initial parameter estimates. I remark that values of initial estimates have a greater effect on the model fit (i.e. loglikelihood, AIC and also on

Hello, I'm trying to do some non-linear regression with 2 cell types and 4 tissue type treatments using selfStart models Following Ritz and Streibig (2009), I wrote the following routines: ##Selfstart expDecayAndConstantInflowModel <- function(Tb0, time, aL, aN, T0){ exp(-time*aL)*(T0*aL+(-1+exp(time * aL))*Tb0 * aN)/aL } expDecayAndConstantInflowModelInit <- function(mCall, LHS,

Hi, I am using ns() to model the effect of time on some outcome y [ specifically, I am using polr() in a model of the form mod1=polr(y~x1+x2*ns(Year,df=3),...) , with x1 and x2 denoting several covariates each ] I understand how to use the spline basis as recorded in the model matrix in order to reproduce the model fit and to generate curves of the point estimates of the time effect,

Dear community, I'm trying to model growth with this function: Yi = A* exp(-k*(1/ti^m)) ; A asymptote, k rate of decrease of the relative growth rate, m shape parameter. I don't have variable time so, finally, following some papers, I try to fit Yi+a = A*exp(-k* (1/(-k/(log(Yi/A)))^(1/m)+a)^m); a= 10 I've tried: a) nls.1 <- nls(Yi+a ~A*exp(-k*

Hi Everyone, I'd omitted the non-optional 'parameters' argument to selfStart. Making this change to SSbatch gives the same (successful) result from the two calls to nls. SSbatch<-selfStart( model=function(Batch, Coeffs) { Coeffs[Batch] } ,initial=function(mCall, data, LHS) { # Estimate coefficients as mean of each batch xy <- sortedXyData(mCall[["Batch"]],

Hi All, Another nls related problem (for background, I'm migrating a complicated modelling package from S-plus to R). Below I've reduced this to the minimum necessary to demonstrate my problem (I think); the real situation is more complicated. Two similar selfStart functions, ssA and ssB. The 'initial' function for ssB modifies its arguments a little and then calls getInital