similar to: manual recreation of varConstPower using new fixed effects variables in nlme

  Hola,   Por favor, ¿podríais indicarme qué recursos (librerías o ideas) pueden resultar de utilidad para crear un gráfico del estilo del de la figura 3.8 del siguiente link?   http://www.tsc.uvigo.es/BIO/Bioing/ChrLDoc3.html#3.5   Actualmente estoy utilizando funciones muy básicas y la verdad es que no me encuentro muy satisfecha con el resultado.   Muchas gracias.   Eva [[alternative HTML

Hello, Excuse me for posting two questions in one day, but I figured it would be better to ask my questions in separate emails. I will again give the caveat that I'm not a statistician by training, but have a fairly decent understanding of probability and likelihood. As before, I'm trying to fit a nonlinear model to a dataset which has two main factors using nlme. Within the dataset

I have been using gnls with the weights argument (and varConstPower) to specify a variance structure for curve fits. In attempting to extract the parameters for the variance model I am seeing results I don't understand. When I simply display the model (or use "summary" on the model), I get what seem like reasonable values for both "power" and "const". When I

Works perfectly. Thank you. -Hugh Rand -----Original Message----- From: Spencer Graves [mailto:spencer.graves at pdf.com] Sent: Sunday, January 15, 2006 6:41 PM To: Rand, Hugh Cc: 'r-help at lists.R-project.org' Subject: Re: [R] trouble with extraction/interpretation of variance structure para meters from a model built using gnls and varConstPower How about this: >

Dear Thomas Lumley, and R-help list members, I have read your article "Network meta-analysis for indirect treatment comparisons" (Statist Med, 2002) with great interest. I found it very helpful that you included the R code to replicate your analysis; however, I have had a problem replicating your example and wondered if you are able to give me a hint. When I use the code from the

Hi, I am trying to add multiple variance structures such as the first example below: vf1 <- varComb(varIdent(form = ~1|Sex), varPower()) However my code below will not work can anybody please advise me? VFcomb<-varComb(varExp(form=~depcptwithextybf),varFixed(form=~FebNAO)) also if you have two variables with the same weights function would you write that as:

Dear list, I was trying to create a VarClass for nlme to work with Fay-Herriot (FH) models. The idea was to create a modification of VarComb that instead of multiplying the variance functions made their sum (I called it varSum). After some fails etc... I found that the I was not getting the expected results because I needed to make sigma fixed. Trying to find how to make sigma fixed I run into

I'm getting an error from nlme that has me stymied. I have a data set ,'mydata', with variables: AChE, Dose, sex, set, and mrid; 'set' and 'mrid' indicate two levels of nesting, with 'set' nested within 'mrid'. I want to fit the model: mod <- nlme(AChE ~ Cexp(Dose, A, B, m), data=mydata, fixed = A+B+M~sex, random=A+B+m~sex | mrid/set,

Dear R-Users, I have a question concerning extraction of parameter estimates of variance function from lme fit. To fit my simulated data, we use varConstPower ( constant plus power variance function). fm<-lme(UPDRS~time,data=data.simula,random=~time,method="ML",weights=varConstPower(fixed=list(power=1))) I extract the results of this function by using the following codes:

Hello R users, When trying to fit a nonlinear mixed model to a respiration time series, I get the following error message: Error in recalc.varFunc(object[[i]], conLin) : dims [product 30] do not match the length of object [34] In addition: Warning message: In conLin$Xy * varWeights(object) : longer object length is not a multiple of shorter object length Below is an example that generates

Greetings, 1) Is there a nice way of extracting the variance estimates from an lme fit? They don't seem to be part of the lme object. 2) In a series of simulations, I am finding that with ML fitting one of my random effect variances is sometimes being estimated as essentially zero with massive CI instead of the finite value it should have, whilst using REML I get the expected value. I guess

I am having trouble fitting correlation structures within nlme. I would like to fit corCAR1, corGaus and corExp correlation structures to my data. I either get the error "step halving reduced below minimum in pnls step" or alternatively R crashes. My dataset is similar to the CO2 example in the nlme package. The one major difference is that in my case the 'conc' steps are

R 1.6.2 for Windows, Win2k: I have fitted a weighted least squares model using the code "wls.out <- gls(y ~ x1 + x2 + x3 + x4 + x5 + x6 - 1, data = foo.frame, weights = varConstPower(form = ~ fitted(.), fixed = list(power = 0.5), const = 1))" The data has 62 rows and the response is zero when the covariates are zero. The purpose of the model was to account for the the fact that

Hello all. I was wondering if the logLike.nls() and logLike.nlme() functions are still being used. Neither function seems to be available in the most recent release of R (1.9.1). The following is contained in the help file for logLik(): "classes which already have methods for this function include: 'glm', 'lm', 'nls' and 'gls', 'lme' and others in

Hello everybody. I am trying to upgrade from R-1.9.0 to R-1.9.1 on a RedHat linux 2.4.18 system. I get the following error after "tar -xvzf R-1.9.1.tgz ; cd ./R-1.9.1/ ; ./configure" and "make" : [make works for 10 minutes ... snip ...] varExp text html latex varFixed text html latex varFunc

Dear R-user I used lme to fit a linear mixed model inlcuding weights=varPower(). Additionally I wanted to use glht to calculate Tukey-Kramer multiple comparision. error: > glht(modelF, linfct=mcp(Species="Tukey")) Error in glht.matrix(model = list(modelStruct = list(reStruct = list(SubPlot = -0.305856275920955, : ?ncol(linfct)? is not equal to ?length(coef(model))? >

Dear all, I am estimating a mixed-model in Ubuntu Raring (13.04¸ amd64), with the code: fm0 <- lme(rt ~ run + group * stim * cond, random=list( subj=pdSymm(~ 1 + run), subj=pdSymm(~ 0 + stim)), data=mydat1) When I check the approximate variance-covariance matrix, I get: > fm0$apVar [1] "Non-positive definite

Hello everybody, I have a dataset where each row has number of subjects and that gives me natural weights for the variance function. Additionally I see that variance increases with Age, which is a regressor. So using gls I have weights=varFixed(~1/n) but don't know how to include the extra effect of the regressor. Fitted values show a quadratic curve vs. age, not sure if that helps.

Hi everybody, I'm interested in evaluating the effect of a continuous variable on the mean and/or the variance of my response variable. I have built functions expliciting these and used the 'mle2' function to estimate the coefficients, as follows: func.1 <- function(m=62.9, c0=8.84, c1=-1.6) { s <- c0+c1*(x) -sum(dnorm(y, mean=m, sd=s,log=T)) } m1 <- mle2(func.1,

Dear All, I'm trying to model heteroscedasticity using a multilevel model. To do so, I make use of the nlme package and the weigths-parameter. Let's say that I hypothesize that the exam score of students (normexam) is influenced by their score on a standardized LR test (standLRT). Students are of course nested in "schools". These variables are contained in the