similar to: names() function and lmer()

I'm trying to use the lmeSplines package together with lme4. Below is (1) an example of lmeSplines together with nlme (2) an attempt to use lmeSplines with lme4 (3) then a comparison of the random effects from the two different methods. (1) require(lmeSplines) data(smSplineEx1) dat <- smSplineEx1 dat.lo <- loess(y~time, data=dat) plot(dat.lo) dat$all <- rep(1,nrow(dat)) times20

Using ranef() (package nlme, version 3.1-75) with an 'lme' object I can obtain random effects for intercept and slope of a certain level (say: 1) - this corresponds to (say level 1) "residuals" in MLWin. Maybe I'm mistaken here, but the results are identical. However, if I try to get the standardized random effects adding the paramter "standard=T" to the

Hello everyone, a problem with the plot.zoo function. In the parameters of the function, cex.lab is ignored. I tried to reduce the size of the yaxis labels by at least 50%. ------------------ Example: sample <- as.zoo(EuStockMarkets) par(las=1) plot.zoo(sample, plot.type="multiple", main="Time Series", xlab="Date", yaxt="n", cex.lab=0.5,

My question relates to problems that I'm having matching lme and lmer examples in P&B. using Matix 0.995 In the Oxide example in p167-170 I can't get the level 2 coefficient estimates to match the fm1Oxide model in lme is data(Oxide,package="nlme") lme(Thickness~1,Oxide) which I translate in Lmer syntax to fm3Oxide<-lmer(Thickness~

Full_Name: Din Chen Version: 2.4.0 OS: Windows XP Submission from: (NULL) (66.17.122.18) I just updated the R.2.4.0. and got the error message for random effect modelling, which was working on R.2.3.1. library(lme4) mmod <- lmer(bright ~ 1+(1|operator), pulp) summary(mmod) Then when I tried to extract the residuals and random effect using: resid(mmodr) ranef(mmodr) I got error message:

Hello All R Users, Function loglm() in library MASS can be cajoled to accomodate structural zeros in a cross-classification table. An example from Fienberg demonstrates how this can be done. My question is: Can the function glm() perform the same task? Can glm() estimate a log-linear model with fixed zeros like loglm()? Thanks for your help, Andrew ## Fienberg, The Analysis of Cross-Classified

User of the coxme library (mixed effects Cox models) are instructed to use ranef(), fixed(), VarCorr(), etc to retrieve bits out of a fitted model; it purposely uses the same methods as nlme and/or lmer. The current behavior is to "depend" on nlme. If I defined the methods myself in coxme, then someone who had both nlme and coxme loaded will suffer from "last loaded wins",

I have a model: mod1<-lmer( x ~ (1|rtr)+ trth/(1|cs) , data=dtf) # Here, cs and rtr are crossed random effects. cs 1-5 are of type TRUE, cs 6-10 are of type FALSE, so cs is nested in trth, which is fixed. So for cs I should get a fit for 1-5 and 6-10. This appears to be the case from the random effects: > mean( ranef(mod1)$cs[[1]][1:5] ) [1] -2.498002e-16 > var(

I'm estimating two models for data with n = 179 with four clusters (21, 70, 36, and 52) named siteid. I'm estimating a logistic regression model with random intercept and another version with random intercept and random slope for one of the independent variables. fit.1 <- lmer(glaucoma~(1|siteid)+x1 +x2,family=binomial,data=set1,method="ML",

I am fitting models to the responses to a questionnaire that has seven yes/no questions (Item). For each combination of Subject and Item, the variable Response is coded as 0 or 1. I want to include random effects for both Subject and Item. While I understand that the datasets are fairly small, and there are a lot of invariant subjects, I do not understand something that is happening

Hi all! How is it possible to estimate standard errors for coef obtained from lme? Is there sth like se.coef() for lmer or what is the anaytical solution? Thank you!

Buenos días; Estoy intentando aprender algo de sobre los modelos lineales mixtos. Estoy siguiendo el libro "Multilevlel analysis: techniques and applications" de Joop Hox. Estoy intentado reproducir las tablas y gráficos que trae el libro con el paquete lmer. En este libro se indica que en la comprobación de los supuestos del modelo hay que estudiar los gráficos de residuales tanto

Hi helpeRs, I've been working through some examples in Pinhiero & Bates( 2000) trying to understand how to translate to the new Lme4 syntax but without much luck. Below is what I think I should do, but either the answers don't come out the same or I get errors. In the Oxide problems I'm particularly interested in obtaining the levels coeficients but this options no longer seems

Hola a todos. Estoy utilizando la función lmer del paquete lme4 para ajustar un modelo mixto. Tengo varias variables en mi data.frame, unas son a nivel individual y otras a nivel de comarcas. Listo algunas. ingre_6 : Ingresos (nivel individual) iscs_a : un indicador sintético resumen de otras variables, calculado mediante componentes principales. sau_com : superficie agraria útil de cada una

I am fitting models to the responses to a questionnaire that has seven yes/no questions (Item). For each combination of Subject and Item, the variable Response is coded as 0 or 1. I want to include random effects for both Subject and Item. While I understand that the datasets are fairly small, and there are a lot of invariant subjects, I do not understand something that is happening here, and in

Hi I want to extract the random slopes from a lmer (I am doing a random regression), but are the answers obtained from ranef or coef? My model is: mod1<-lmer(B~ A +(A|bird), family=quasibinomial) And I want to obtain a slope for each individual bird but am not sure which output I need and can't find the answer anywhere. Thanks Sam Dr Samantha Patrick EU INTERREG Post Doc Davy 618

R múltiple Estoy pensando en un problema que tendré que solucionar pero aún no comencé a escribirlo, por lo tanto no hay código en R como para compartir, sin embargo no tengo idea de cómo realizarlo desde R. El planteo es el siguiente: Los datos son en una cantidad necesaria para que el procesamiento estadístico demore (minutos, horas). Supongamos dos variables (serían más), la A y la B,

Javier, Creo que aquí aplica la ley de Linus que dice: "Dado un número suficientemente elevado de ojos, todos los errores se convierten en obvios". La persona que revisa y encuentra un error no necesariamente tiene que ser la misma que la que lo escribe. Una motivación muy importante al compartir un código es la de recibir los beneficios del control de calidad por parte de tus pares.

Hi, all. In setting up my package for post-processing regression models, I am trying to use standard notation as much as possible: thus, I use coef() to access estimated coefficients. I wrote a function called se.coef() to grab standard errors, and se.fixef() and se.ranef() to grab se's from coefficients estimated from lmer(). I also need a function to access sigma-hat (the residual sd

hello R help I am trying to analyze a data set that has been collected from a hierarchical sampling design. The model should be a mixed model nested ANOVA. The purpose of my study is to analyze the variability at each spatial scale in my design (random factors, variance components), and say something about the variability between regions (fixed factor, contrast of means). The data is as