similar to: nlme varFixed

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: 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),

Thank you Michael, Curves for each level of the factor sounds very interesting, Do you have a suggestion how to plot them? Thank you! Alina *Alina Vodonos Zilberg* On Thu, Aug 10, 2017 at 7:39 AM, Michael Dewey <lists at dewey.myzen.co.uk> wrote: > Dear Alina > > If I understand you correctly you cannot just have a single predicted > curve but one for each level of your

Dear Alina If I understand you correctly you cannot just have a single predicted curve but one for each level of your factor. On 09/08/2017 16:24, Alina Vodonos Zilberg wrote: > 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)

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

Dear R-expert I'm fitting a non linear model (energy allocation model to individual growth data) using your nlme routine. For each individual I have thus a number of observations (age and size) to which I fit the nonlinear function, with random effects for the individuals on the estimated parameters (individual as the grouping factor). The sampling of these individuals was stratified (size

R users doing data analysis may be interested in the following paper: http://methodsblog.wordpress.com/2009/11/13/first-paper-now-online/?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+wordpress%2Fmethodsblog+(methods.blog) All data and R code is available. Alain ----- -------------------------------------------------------------------- Dr. Alain F. Zuur First author of:

Hi List,   I’ m analysing the selectivity of resting site use by forest carnivores through mixed modelling techniques and I wonder which will be the best r package to deal with several aspects simultaneously: -          binomial variable response; -          possible spatial and/or temporal correlation; I have tried nlme (lme function) and lme4 (lmer function) packages, however I realize that the

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

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 have what I hope is a simple question - is it possible to do time series analysis on a small data set specifically only four data points? I have collected human threat data (mean number of threats per kilometre walked/ survey) every 3 months in eight different sites (four with an experimental element and four control sites). I am trying to determine the best way to determine if there is a

Dear R Community, I?m relatively new in the field of R and I hope someone of you can help me to solve my nerv-racking problem. For my Master thesis I collected some behavioral data of fish using acoustic telemetry. The aim of the study is to compare two different groups of fish (coded as 0 and 1 which should be the dependent variable) based on their swimming activity, habitat choice, etc.

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.

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 list-members, I am trying to use R to conduct a meta-analysis, i.e. I'd like to use a multi-level model to integrate the findings of a number of primary research studies. I set up a simple two level-model (only summary statistics are provided by each study) as follows: sapp.lme <- lme(D ~ 1, data = sapp.frame, random = ~ 1 | STUDYNR, weights=varFixed(~-1+STDERR_D),na.action =

Dear R-users: I can't run the following code from "Mixed Effects Models in S and S-plus". library( nlme ) options( width = 65, digits = 5 ) options( contrasts = c(unordered = "contr.helmert", ordered = "contr.poly") ) # Chapter 5 Extending the Basic Linear Mixed-Effects Models # 5.1 General Formulation of the Extended Model data( Orthodont ) vf1Fixed

Dear all, We are trying to do a meta regression in R using the lme function. The reason for doing this with lme function is that we have covariates and studies within references. In S-Plus this is possible by using the following command: lme(outcome ~ covars, random = ~1 | reference/study, weights = varFixed(~var.outcome), data = mydata, control = lmeControl(sigma = 1)) This means that the