similar to: Denominator Degrees of Freedom in lme() -- Adjusting and Understanding Them

Hello, I am struggling to understand how denominator degrees of freedom and subsequent significance testing based upon them works in nlme models. I have a data set of 736 measurements (weight), taken within 3 different age groups, on 497 individuals who fall into two morphological catagories (horn types). My model is: Y ~ weight + horn type / age group, random=~1|individual I am modeling

Dear all, I am starting to use the lme package (and plan to teach a course based on it next semester...). To understand what lme is doing precisely, I used balanced datasets described in Pinheiro and Bates and tried to compare the lme outputs to that of aov. Here is what I obtained: > data(Machines) > summary(aov(score~Machine+Error(Worker/Machine),data=Machines)) Error: Worker

Hi, I'm having some problems regarding the packages lme4 and nlme, more specifically in the denominator degrees of freedom. I used data Orthodont for the two packages. The commands used are below. require(nlme) data(Orthodont) fm1<-lme(distance~age+ Sex, data=Orthodont,random=~1|Subject, method="REML") anova(fm1) numDF DenDF F-value p-value (Intercept) 1

Dear All, I am analysing some data for a colleague (not my data, gotta be published so I cannot divulge). My response variable is the number of matings observed per day for some fruitlies. My factors are: Day: the observations were taken on 9 days Regime: 3 selection regimes Line: 3 replicates per selection regime. I have 81 observations in total The lines are coded A to I, so I do not need

Hello, I've just carried out my first good-looking model using glmmPQL, and the output makes perfect sense in terms of how it fits with our hypothesis and the graphical representation of the data. However, please could you clarify whether my degrees of freedom are appropriate? I had 106 subjects, each of them was observed about 9 times, creating 882 data points. The subjects were in 3

Hi, I am trying to run a linear mixed effect model on data. I have 17 longitudinal subjects and 36 single subjects, and this is the code I'm using (below). So, INDEX1 is the column with brain volumns, and the predictors are gort and age, by time ID (time they were seen). I believe my data is set up the right way, but when I run it, I get DF for Intercept is 49, and DF for slope is 13?

Dear forum members, What is the formula to calculate denominator degrees of freedom (den df) for nonlinear mixed-effect models with covariates? My model is similar to a CO2 uptake example from Pinheiro and Bates (2000, page 376). In this CO2 dataset, there are two treatments and two types (84 observations in total), but den df for each parameter of the model is 64. Isn’t it too high? Your

Hello everyone, I am trying to regress applicants' performance in an assessment center (AC) on their gender (individual level) and the size of the AC (group level) with a multi-level model: model.0 <- lme(performance ~ ACsize + gender, random = ~1 | ACNumber, method = "ML", control = list(opt = "optim")) I have 1047 applicants in 118 ACs: >

Sorry, by mistake I sent this to Professor Bates instead of r-help. Han -------- Original Message -------- Subject: Re: [R] degrees of freedom for t-tests in lme Date: Thu, 25 Apr 2002 09:16:16 -0700 From: Han-Lin Lai <Han-Lin.Lai at noaa.gov> To: Douglas Bates <bates at stat.wisc.edu> References: <3CC6E87F.5400277D at noaa.gov> <6rg01lottu.fsf at franz.stat.wisc.edu>

I have some questions about the use of lme(). Below, I constructed a minimal dataset to explain what difficulties I experience: # two participants subj <- factor(c(1, 1, 1, 1, 2, 2, 2, 2)) # within-subjects factor Word Type wtype <- factor(c("nw", "w", "nw", "w", "nw", "w", "nw", "w")) # within-subjects factor

I have a dataset called "results" that looks like this: arrive depart intercept 1 1 1 1 2 1 1 3 1 1 2 2 1 3 2 1 3 3 2 2 2 2 3 2 3 3 3 where arrive is the period of arrival, depart is the period of departure, and intercept

Hi, I have very simple balanced randomized block design where I total have 48 observations of a measure of weights of a product, the product was manufactured at 4 sites, so each site has 12 observations. I want to use lme() from nlme package to estimate the standard error of the product weight. So the data look like: MW site 1 54031 1 2 55286 1 3 54396 2 4 52327 2 5 55963

tl;dr anova.lme() claims to provide sums of squares, but it doesn't. And some names are misspelled in ?lme. I can submit all this stuff as a bug report if that's preferred. ?anova.lme says: When only one fitted model object is present, a data frame with the sums of squares, numerator degrees of freedom, denominator degrees of freedom, F-values, and P-values The output of fm1

Hi, I have trouble to figure out how the df is derived in LME. Here is my model, lme(y~x+log(den)+sex+dep,data=lwd,random= list(group=~x)) Number of total samples (N) is 3237 number of groups (J) is 26 number of level-1 variables (Q1) is 3, i.e., x, log(den) and sex number of level-2 variables (Q2) is 1, i.e., dep x and den are continuous variable sex is associated with individual samples

Dear all, I am using the "mgcv" package by Simon Wood to estimate an additive mixed model in which I assume normal distribution for the residuals. I would like to test this model vs a standard parametric mixed model, such as the ones which are possible to estimate with "lme". Since the smoothing splines can be written as random effects, is it correct to use an (approximate)

Hi All, I am frustrated by mixed-effects model! I have searched the web for hours, and found lots on the nested anova, but nothing useful on my specific case, which is: a random factor (C) is nested within one of the fixed-factors (A), and a second fixed factor (B) is crossed with the first fixed factor: C/A A B A x B My question: I have a functioning model using the aov command (see

From a quick look at the paper in the SAS proceedings, the simulations seem limited to nested designs. The major problems are with repeated measures designs where the error structure is not compound symmetric, which lme4 does not at present handle (unless I have missed something). Such imbalance as was investigated was not a serious issue, at least for the Kenward and Roger degree of freedom

Dear list, I ran a mixed effect model using R 1.4.1 and SAS 8.0 on the SIMS data found in the SASmixed package and found that the degrees of freedoms for fixed effects are very different. From R, df = n - v -1 where n is total # of observations, v is the # of levels for the grouping factor. From SAS df = v -1. Am I wrong about this or can somebody explain which is correct and why? Thanks a

Hi, this is not a new doubt, but is a doubt that I cant find a good response. Look this output: > m.lme <- lme(Yvar~Xvar,random=~1|Plot1/Plot2/Plot3) > anova(m.lme) numDF denDF F-value p-value (Intercept) 1 860 210.2457 <.0001 Xvar 1 2 1.2352 0.3821 > summary(m.lme) Linear mixed-effects model fit by REML Data: NULL AIC BIC

Hi all, I have an experiment where 5 raters assessed the quality of 24 web sites. (each rater rated each site once). I want to come up with a measure of reliability of the ratings for the web sites ie to what extent does each rater give the same (or similar) rating to each web site. My idea was to fit a random effects model using lme and from that, calculate the intraclass correlation as a