similar to: Initialize varFunc in R

Hello. I am trying my hand at modifying the varFunc class varExp, but I must be missing a step. All I want to do right now is make a working copy of varExp, call it varExp2, and then later change it. coef.varExp2, coef<-.varExp2, and Initialize.varExp2 all seem to work properly after I construct them. I can successfully use the commands: v2 <- varExp2(form = ~age|Sex,fixed =

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(

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

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

Greetings, I have a meta-analysis problem in which I have fixed effects regression coefficients (and estimated standard errors) from identical models fit to different data sets. I would like to use these results to create pooled estimated regression coefficients and estimated standard errors for these pooled coefficients. In particular, I would like to estimate the model \beta_{i} = \mu +

Hello - I've been using R for years, but have always been able to find what I need already available. Now I find that I would like to write a new varFunc class for the lme() or nlme() packages. There is some guidance for this in Problem 4 Chapter 5 of Bates and Pinheiro Mixed Effects Models in S and S-Plus. However, I find that I am unable to even get started and so have just purchased

Hey there I am estimating a multilevel model using lmer. I have 5 imputed datasets so I am using mitools to pool the estimates from the 5 > > datasets. Everything seems to work until I try to use > MIcombine to produced pooled estimates. Does anyone have any suggestions? The betas and the standard errors were extracted with no problem so everything seems to work smoothly up until

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

Hello, As I am relatively new to the R environment this question may be either a) Really simple to answer b) Or I am overlooking something relatively simple. I am trying to add a VarIdent structure to my gam model which is fitting smoothing functions to the time variables year and month for a particular species. When I try to add the varIdent weights to variable Month I get this error returned.

Dear R users, I am currently analyzing a dataset using lme(). The model I use has the following structure: model<-lme(response~Covariate+TreatmentA+TreatmentB,random=~1|Block/Plot,method="ML") When I plot the residuals against the fitted values, I see a clear positive trend (meaning that the variance increases with the mean). I tried to solve this issue using weights=varPower(),

Hello, I have a problem concerning logistic regressions. When I add a quadratic term to my linear model, I cannot draw the line through my scatterplot anymore, which is no problem without the quadratic term. In this example my binary response variable is "incidence", the explanatory variable is "sun": > model0<-glm(incidence~1,binomial) >

Hello: I have an lme object, say lme_res2, which was generated using the varIdent. I'm trying to extract the double 1.532940 from the object, but I can't find it by attributes(lme_res2) or attributes(summary(lme_res2)). How can I pull it out (so that I can save it to another variable)? Thanks. Shin Linear mixed-effects model fit by REML Data: dat Log-restricted-likelihood:

Let's say I have a program that returns variables whose names may be any string within the vector NAMES=c("varA","varB","varC","varD","varE","varF"..."varZ"), but I do not ever know which ones have actually been created. So in one example output, "varA", "varC", and "varD" could exist, but

I''m using Ferret on a Swedish website and I get some unexpected behaviour on searches containing the swedish charchters ???. An exampel, if I index a string "Varf?r fungerar det inte" ("Why doesnt it work" in swedish) and search for "f?r" I''ll get one (1) match. The expected behaviour would be no matches since ''f?r'' is part of

I am looking for a book that discusses the theory of multiple imputation (and other methods of dealing with missing data) and, just as importantly, how to implement these methods in R or S-Plus. Ideally, the book would have a structure similar to Faraway (Regression), Pinheiro&Bates (Mixed Effects) and Wood (GAMs) and would be very modern (i.e. published within the last couple of years).

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 alltogether, I am searching for a way to determine "effect size" in multi-level models by using lme(). Coming from Psychology, for ordinary OLS there are measures (for meta-analysis, etc.) like CohensD <- (mean_EG - mean_CG) / SD_pooled or (p)eta^2 <- SS_effect / (SS_effect + SS_error) I do not intend to lead a discussion of the usefulness of such measures as long as

Hello R-Helpers, I am not sure if it is a very simple question but I would like to use two (...) in a function, for example, this is a script where I would like to input the variable names (in one of the (...)) and the variances associated to those variables which are not calculated in the script because there is a specific software to calculate it (the other (...)) data <- function

Hi, how can I change the significance level in test F to select variable in step command? I used step(model0, ~x1+x2+x3+x4, direction=c("forward"), test='F', alpha=.05) but it does't work. -------------------------------------- Silvano Cesar da Costa Departamento de Estat?stica Universidade Estadual de Londrina Fone: 3371-4346

1. The function "KalmanLike" seems to change its inputs AND PREVIOUSLY MADE copies of the inputs. Consider the following (using R 2.1.0 patched under Windows XP): > Fig2.1 <- StructTS(x=Nile, type="level") > unlist(Fig2.1$model0[2:3]) a P 1120 286379470 > tst2 <- tst <- Fig2.1$model0 > tst23 <- tst[2:3] > tst23u <-