similar to: nlme weighted

Dear R-nlme expert We need two pieces of information about the fitting of a nlme model which we cannot extract from the R help files and would be most grateful if you could help us. We fit an energy allocation growth model with 4 parameters to individual growth curves using the nlme routine. We thus have repeated age and size measurements of individuals and therefore allow for random

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:

Hello there! I'm creacting a loop for(i in 1:n){...}within which I build a nls model at each iteration. for some of the values of i, the algoritm in the nls function doesn't converge or cannot find a solution and consequently an error message is produced, and so my loop is interupted. The errors don't really matter to me as all the other values might still be useful and therefore I

Dear R-project team, I have a problem with the function varFixed() of the nlme-package. I used it with the squid-data of Zuur et. al 2009 (chapter 4), to fix increasing residuals (heterogenetiy) (see graph in the email) I get the message ' Variance function structure of class varFixed with no parameters, or uninitialized Could you help me please? Kind regards Franz

Hallo, I have a question that is more statistic related than about nlme and R functioning. I want to fit a complicated nonlinear model with nlme with several different measures of transpiration taken on each of the 220 trees grouped in 8 families. The unknown parameters of the model are three + their variances (and covariances). I want to estimate the variances among families of the

Hi, I would like to do a meta-analysis, i.e., a mixed-effects regression, but I don't seem to get what I want using both the nlme or metafor packages. My question: is there indeed no way to do it? And if so, is there another package I could use? Here are the details: In my meta-analysis I'm comparing different studies that report a measure at time zero and after a certain followup

Greetings- I am in the process of constructing a nonlinear model using nlme. The model is attempting to fit a nested data structure from some public-opinion data (the data are from individuals nested within organizations). The question I have is fairly simple (I hope). The data were collected in two stages: a 15,000-subject randomly-sampled telephone interview (the SCREEN), with 2,517 subjects

I have a data set with 50 different x values and 5 values for the sampling variance; each of the 5 sampling variances corresponds to 10 particular x values. I am trying to fit a mixed effect linear model and I'm not sure about the syntax for specifying the fixed variance structure. In Pinheiro's book my situation appears to be similar to the example used for varIdent, where there is a

Hi, I would like to fit a model for a factorial design that allows for unequal variances in all groups. If I am not mistaken, this can be done in lm by specifying weights. A function intended to specify weights for unequal variance structures is provided in the nlme library with the varIdent function. Is it apropriate to use these weights with lm? If not, is there another possibility to do

  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

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

Dear R-List users, Can anyone explain exactly the difference between Weights options in lm glm and gls? I try the following codes, but the results are different. > lm1 Call: lm(formula = y ~ x) Coefficients: (Intercept) x 0.1183 7.3075 > lm2 Call: lm(formula = y ~ x, weights = W) Coefficients: (Intercept) x 0.04193 7.30660 > lm3 Call:

Dear listers, glmmPQL (package MASS) is given to work by repeated call to lme. In the classical outputs glmmPQL the Variance Structure is given as " fixed weights, Formula: ~invwt". The script shows that the function varFixed() is used, though the place where 'invwt' is defined remains unclear to me. I wonder if there is an easy way to specify another variance

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.

I am using lme from the nlme package to fit a mixed model. We have observations nested in patients(encounters) and patients nested in groups (2 different treatments). We are interested in the differences between the 2 groups, both the means and the standard deviations (are patients in group A less variable than those in group B? both within patient and between patient within group). Here is

Dear R-help, I am trying to set up linear mixed effects models in R using the (recommended) nlme package (R version 2.3.1 on a Linux platform). When trying to reproduce an example from Jose Pinheiro & Douglas Bates (2000, p 210) I get the following error message (code to produce message pasted as well): library("nlme") data("Orthodont") vf1Ident <- varIdent(

Hi everyone, I have tried to solve a simple problem for days but I can't figure out how to run it properly. If someone could give me a hint, this would be really great. Basically, I want to run a standard economist's fixed, and random effects regression (corresponds to xtreg in STATA) but with _variable_ weights (they correspond to changing industry shares in the market). Here is

Dear List: Earlier this week I posted a question and received no response, and I continue to struggle with my model. My original question is pasted below. I am using lme and want to fix the variance of the within group residual at 1 (e~n(0,1). I think the varFixed function should be used to accomplish this, but I am struggling to figure out how to do this. Can anyone offer suggestions on how

Hi, I am performing meta-regression using linear mixed-effect model with the lme() function that has two fixed effect variables;one as a log transformed variable (x) and one as factor (y) variable, and two nested random intercept terms. I want to save the predicted values from that model and show the log curve in a plot ; predicted~log(x) mod<-lme(B~log(x)+as.factor(y),

Dear all, Edmond Ng (http://multilevel.ioe.ac.uk/softrev/reviewsplus.pdf) provides an example to fit the mixed effects meta-analysis in Splus 6.2. The syntax is: lme(fixed=d~wks, data=meta, random=~1|study, weights=varFixed(~Vofd), control=lmeControl(sigma=1)) where d is the effect size, study is the study number, Vofd is the variance of the effect size and meta is the data frame.