similar to: Error in nlme ranef plot()

Hi I am following the model building strategy that is outlined in the Pinheiro and Bates book wrt including covariates but am having a problem with the plot. Basically I am using 4 covariates (1 of them is continuous) and 3 of them are fine but the 4th one is being shown as a scatterplot despite the fact that it is a factor. I have explicitly declared this to be a factor (pcat<-as.factor(pcat))

library(nlme) data(Phenobarb) na.include <- function(x)x phe1 <- nlme(conc~phenoModel(Subject, time, dose, lCl, lV), data = Phenobarb, fixed = lCl+lV~1, random= pdDiag(lCl+lV~1), start = c(-5,0), na.action = na.include, naPattern = ~!is.na(conc)) phe.ranef <- ranef(phe1,augFrame=TRUE) plot(phe.ranef, form=lCl~Wt+ApgarInd) [Error in max(length(x0),

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

> To sum up, I can't figure out how MLWin calculates the > standardized residuals. But I understand that this is not a > question for the R list. > Nevertheless, it would help if someone could point me to some > arguments why not to use them and stick to the results > obtainable by ranef(). Hi Dirk: Well, it is interesting that mlWin and lmer generate the same exact

Dear R-help, I am trying to create groupedData objects using the nlme library. I'm missing something basic, I know: Here is the first example in ch.1 of Pinheiro & Bates (2000): library(nlme) x2=Rail$travel;x1=Rail$Rail;eg1=data.frame(x1,x2);eg1gd=Rail print(eg1gd) x11();print(plot(eg1gd)) femodel=lm(x2~x1-1,data=eg1gd) print(femodel$coefficients) Result: x12 x15 x11

Greetings, I have a groupedData (nmGroupedData) object created with the following syntax: Soil <- groupedData( ksat ~ conc | soil_id/sar/rep, data=soil.data, labels=list(x='Solution Concentration', y='Saturated Hydraulic Conductivity'), units=list(x='(cmol_c)', y='(cm/s)') ) the original data represents longitudinal observations in the form of:

Hola! I am encountering the following problem, in a multilevel analysis, using glmmPQL from MASS. This occurs with bothj rw1062 and r-devel, respectively with nlme versions 3.1-38 and 3.1-39 (windows XP). > S817.mod1 <- glmmPQL( S817 ~ MIEMBROScat+S901+S902A+S923+URBRUR+REGION+ + S102+S103+S106A+S108+S110A+S109A+S202+S401+S557A+S557B+ + YHOGFcat,

Hi, Does anyone know how to make the color of the lines all black when plotting groupedData with an outer factor: For example, library(nlme) plot(Dialyzer, outer=~QB, key=F) This generated colored curves in R.2.4.1. How to make all the curves black ? (or how to alter the color (type) of lines for the nlme groupedData plotting function in general?) Thanks Qiong

Dear List: I am having difficulties with the fitted values at different levels of a multilevel model. My data set is a series of student test scores over time with a total of 7,280 observations, 1,720 students nested witin 60 schools. The data set is not balanced. The model was fit using eg.model.1<-lme(math~year, random=~year|schoolid/childid, data=single). When I call the random

I generate a groupedData object library(nlme) obj <- groupedData(mg10 ~ time | gp, data = common, outer = ~pct) gp has 101 levels, and pct has 3. There are 38, 25, 38 gps in each of the levels of pct respectively. I fit my model fit.rtg <- lme(mg10 ~ time * group, data = obj, random = ~time * group | gp) Now I try to plot the results. I would like to print 40 panels on each

Hi All. I'm trying to run nlsList an getting an error that makes no sense to me. I have accuracy and reaction time data over many trials for each person (id) When I use nlsList code that is virtually identical to the example in the doc file I get the following error. I've tried everything I could think of and can't get around it. Any ideas what I'm doing wrong? **************

In the non linear mixed effects package a groupedData object can be created to facilitate modeling. The gD object includes a formula of the form 'response variable' ~ 'primary covariate' | 'grouping factor'. In experiments creating response surfaces there are 2 or more primary covariates. Is there any way to use the groupedData() function to include 2 primary

I really hope sombody could help me with the following, I'm having problems accessing the random effect samples following the example on MCMCsamp: (fm1 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)) set.seed(101); samp0 <- mcmcsamp(fm1, n = 1000, saveb=TRUE) str(samp0) Formal class 'merMCMC' [package "lme4"] with 9 slots ..@ Gp :

Hello. Im hoping someone can help with a grouping question related to the "random=" statement within the nlme function. How do you specify that some grouping levels are inner to others? I tried several things, given below. Lets say I have a data frame with five variables, resp, cov1, ran1, ran2, group1, and group 2. The formula is resp~cov1 + ran1 + ran2, where the ran are random

Dear all; Using read.ssd, I tried to import a SAS dataset from a network drive; an error occurred: > stemattr <- read.ssd("g:/olmn2/dyang/siteprod/datasasv8", "stemattr") SAS failed. SAS program at C:\DOCUME~1\dyang\LOCALS~1\Temp\file7785.sas a log and other error products should be in the vicinity Warning messages: 1: sas not found 2: ls not found 3: SAS return

The coxme package has a ranef() method, as does lme4. I'm having trouble getting them to play together, as shown below. (The particular model in the example isn't defensible, but uses a standard data set.) The problem is that most of the time only one of lme4 or coxme will be loaded, so each needs to define the basic ranef function as well as a method for it. But when loaded together

Hi R-experts, I have a problem using nlme. I use the following code to group my data: Parameterg <- groupedData( result ~ time | Batch, data = Batchdata, labels = list( x = "Time", y = "analysis") ) and then uses the nlme function to fit a nonlinear mixed model that includes Process as a fixed covariate: nlme.model001epr <- nlme(result ~ A0 * exp(- ( exp(A1)

Hi I'm getting the intercepts of the Random effects as 0. Please help me to understand why this is coming Zero This is my R code Data<- read.csv("C:/FE and RE.csv") Formula="Y~X2+X3+X4 + (1|State) + (0+X5|State)" fit=lmer(formula=Formula,data=Data) ranef(fit). My sample Data State Year Y X2 X3 X4 X5 X6 S2 1960 27.8 397.5 42.2 50.7 78.3 65.8 S1 1960 29.9 413.3 38.1

I have been struggling all day to import a particular function/method combination (ranef(), which extracts the random effects from a mixed model fit) from the nlme package into another package ... so far without success. The NAMESPACE for nlme contains the following lines: export(..., ranef, ...) S3method(ranef, lme) ranef is defined as a standard S3 generic, function (object, ...)

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",