search for: overparameterize

Dear all, I would like to fit a non-linear model of the form: y=g*x/(a+b*x) with nls(). However this model is somehow overparameterized and I get the error message about singular gradient matrix at initial parameter estimates. What I am interested in is to make inference about parameters b and g, so this has to be taken into account in the model formulation. What options do I have? Also, how is it possible to fit a partially line...

...with 20 rows and three columns. For the model to be estimable in a region of the current estimates, this matrix must have full column rank. When it fails to have full column rank the "singular gradient" message is given and the iterations stop. Generally this indicates that the model is overparameterized or that the starting estimates were poorly chosen. Try using trace = TRUE in the call to nls and watching the progress of the iterations. This will often show that the estimates are wandering into unreasonable regions of the parameter space. -- Douglas Bates bates@stat...

Hi I have the problem with fitting curve to data with lm and glm. When I use polynominal dependiency, fitted values from model are OK, but I cannot recive proper values when I use coefficents to caltulate this. Let me present simple example: I have simple data.frame: (dd) a: 1 2 3 4 5 6 b: 3 5 6 7 9 10 I try to fit it to model: model=glm(b~poly(a,3),data=dd) I have following data

...al. fitted two forms of the model a simpler one and a more complicated model. They reported some difficulty fitting the more complicated model. We found that we could reliably fit (MLE) both the complicated and simpler model in 20 seconds or less (although the more complicated turns out to be overparameterized) Using the random effects module of AD Model Builder we have developed a shared library (Windows dll) that can be called from R via the driver function glmm.admb(). The function can be downloaded from http://otter-rsch.com/admbre/examples/nbmm/nbmm.html The two models of Booth et al are fit b...

Dear all, I'm building binomial mixed-model using lme4 package. I'm able to obtain outputs properly except when I include two particular variables: date (from 23 to 34; 1 being to first sampling day) and Latitude (UTM/100 000, from 55.42 to 56.53). No "NA" is any of those variables. In those cases, I get the warning message: "nlminb failed to converge" I tried to

...7 0.1853 Random effects: Formula: ~1 | stateid (Intercept) Residual StdDev: 0.01543434 0.3163471 BUT the intervals around the random effects are sd((Intercept)) 2.440728e-08 0.01543434 9760.155 Which is obviously nonsense. Now, I know some of what's going on here. The model is overparameterized, and I should be dropping some group-level variables. And if I do that, everything is kosher, and none of these variables matter there either. OTOH, I can also get everything to come out apparently-kosher if I estimate on a theoretically-relevant reduced dataset -- that is, if I drop some observa...

Hi everyone, ----------------------------- Coefficients: ar1 ar2 ma1 ma2 sar1 intercept drift 1.5283 -0.7189 -1.9971 0.9999 0.3982 0.0288 -9e-04 s.e. 0.0869 0.0835 0.0627 0.0627 0.1305 NaN NaN sigma^2 estimated as 0.04383: log likelihood = 4.34, aic = 7.32 Warning message: NaNs produced in: sqrt(diag(object$var.coef))

Greetings, I have a question that I'd like to get input on. I have a classic toxicology study where I artificially fertilized and exposed embryos to a chemical and counted defects. In addition, I kept track of male-female pairs that I used to artificially fertilize and generate embryos with. I need to use logistic regression to model the response, but also check that the genetics of the

consider p as random effect with 5 levels, what is difference between these two models? > p5.random.p <- lmer(Y ~p+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1)) > p5.random.p1 <- lmer(Y ~1+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1)) thanks, Aimin Yan

Hi, I have a dataset like this, > testdata Grouped Data: expr ~ visit | subject expr visit subject 1 6.502782 V1 A 2 6.354506 V1 B 3 6.349184 V1 C 4 6.386301 V2 A 5 6.376405 V2 B 6 6.758640 V2 C 7 6.414142 V3 A 8 6.354521 V3 B 9 6.396636 V3 C I tried the command >

...whether the hypothesis which is being tested is of any actual interest. This would go much further toward bringing the desciple to true enlightenment. Point 3 --- what hypothesis is being tested by SSA? Let factor A correspond to index i, and B to index j. Let the cell means be mu_ij. (In the overparameterized notation, mu_ij = mu + alpha_i + beta_j + gamma_ij.) The hypothesis being tested is H_0: mu_1.-bar = mu_2.-bar = ... = mu_a.-bar where factor A has a levels, and ``mu_i.-bar'' means the average (arithmetic mean) of mu_i1, mu_i2, ..., mu_ib. (Note --- factor B has b levels.) I.e. the...

Hello R-Community, we are running aprogram to fit Non-linear differential equations to Aphid population Data and to estimate the birth and death parameters, here is the code: dat<-data.frame(Time=c(0:60),Cur=c(5,6.2,59,39,38,44,20.4,19.4,34.2,35.4,38.2,48.2,55.4,113.2, 97,112,115,126,136.6,140.6,147.2,151.6,157.8,170,202,210.4,221.2,224.4,248.2,266,

Hi, I am hoping some one can help with this. I am using nlme to fit a random coefficients model. It ran for hours before returning Error: Singularity in backsolve at level 0, block 1 The model is > plavix.nlme<-nlme(PLX_NRX~loglike(PLX_NRX,PD4_42D,GAT_34D,VIS_42D,MSL_42D,SPE_ROL,XM2_DUM,THX_DUM,b0,b1,b2,b3,b4,b5,b6,b7,alpha), + data=data, + fixed=list(b0 +

I saw a similar question but I still don't fully understand how to implement optim. Can someone help me out with this? Thanks. Keun-Hyung > vol<-rep(c(0.03, 0.5, 2, 4, 8, 16, 32), 3) > time<-rep(c(2,4,8),each=7) > p.mated<-c(0.47, 0.48, 0.43, 0.43, 0.26, 0.23, "null", 0.68, 0.62, 0.64, 0.58, 0.53, 0.47, + 0.24, 0.8, 0.79, 0.71, 0.56, 0.74, 0.8, 0.47) >

Hi, All: What tools exist for diagnosing singular gradient problems with 'nls'? Consider the following toy example: DF1 <- data.frame(y=1:9, one=rep(1,9)) nlsToyProblem <- nls(y~(a+2*b)*one, DF1, start=list(a=1, b=1), control=nls.control(warnOnly=TRUE)) Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial

Dear R friends, I know that this topic has been mulled over before, and that there is a substantial difference between the convergence criteria for JMP and those for R. I apologize that this is somwehat raking cold coals. Summary: A model/data combination achieves convergence in JMP, and survives a reasonably rigorous examination (sensible parameter estimates, well-behaved surface,

Hello. I am working on an analysis involving the nonlinear mixed model function (nlme) in R. The data consist of measures of carbon fixation by leaves as a function of light intensity and the parametric function (standard in this area because it has a biological interpretation) is a non-rectangular hyperbola. I cannot get the nonlinear mixed model (nlme) function to converge cleanly. I am

Greetings. Here is sample code, with some comments. It shows how I can simulate data and estimate glm with binomial family when there is no individual level random error, but when I add random error into the linear predictor, I have a difficult time getting reasonable estimates of the model parameters or the variance component. There are no clusters here, just individual level responses, so

Good morning reader, I have encountered a, probably, simple issue with respect to the *formulae* of a *regression model* I want to use in my research. I’m researching alliances as part of my study Business Economics (focus Strategy) at the Vrije Universiteit in Amsterdam. In the research model I use a moderating variable, I’m looking for confirmation or help on the formulation of the model.

Hello Folks, Very new to R so bear with me, running 5.2 on XP. Trying to do a zero-inflated negative binomial regression on placental scar data as dependent. Lactation, location, number of tick larvae present and mass of mouse are independents. Dataframe and attributes below: Location Lac Scars Lar Mass Lacfac 1 Tullychurry 0 0 15 13.87 0 2 Somerset 0 0 0