similar to: ANOVA with two random factors

Sorry, I forgot to be more specific. I am using Windows XP. I am using R.12.2 I installed both packages from the install packages menu. I always write library(name.of.library), and it is enough. But when I write library(nlme), R does not find nlme right away I load nlme first and it says package was downloaded succesfully. However, when I try to do this again in another day, R cannot find

Hi, all. I have R installed in my computer. I guess I did something in my previous session, and now every time I start R, I find the following message: "Fatal error: unable to restore saved data in .RData" I uninstalled R and installed it again and I'm still getting this message. Can anyone help me? Gilbert

Hi, I need a help. I am new in R and I need to run a nested anova with fixed and random factors (Mixed Model). I have a design with three factors: Day, Area and Plot and the dependent variable is density. The factors Day and Area are fixed while Plot is random, factor Area is nested in factor Day, and factor Plot is nested in Area. I can do it using aov by: mod1<-aov(density~ day +

Hi, I am having trouble using the lme function to perform a nested ANOVA with a random nested factor. My design is as follows: Location (n=6) (Random) Site nested within each Location (n=12) (2 Sites nested within each Location) (Random) Dependent variable: sp (species abundance) By using the aov function I can generate a nested ANOVA, however this assumes that my nested

Dear R user, I am trying to reproduce the results in Montgomery D.C (2001, chap 13, example 13-1). Briefly, there are three suppliers, four batches nested within suppliers and three determinations of purity (response variable) on each batch. It is a two stage nested design, where suppliers are fixed and batches are random. y_ijk=mu+tau_i+beta_j(nested in tau_i)+epsilon_ijk Here are the

Dear R user, I am trying to reproduce the results in Montgomery D.C (2001, chap 13, example 13-1). Briefly, there are three suppliers, four batches nested within suppliers and three determinations of purity (response variable) on each batch. It is a two stage nested design, where suppliers are fixed and batches are random. y_ijk=mu+tau_i+beta_j(nested in tau_i)+epsilon_ijk Here are the

Hi R list, I'm new to R software, so I'd like to ask about it is capabilities. What I'm looking to do is to run some statistical tests on quite big tables which are aggregated quotes from a market feed. This is a typical set of data. Each day contains millions of records (up to 10 non filtered). 2011-05-24 750 Bid DELL 14130770 400 15.4800 BATS

I'm a bit confused about the anova/aov functions. Both seem to rely on data models, where the relationship between the dependent variables and the independent variables can be expressed as a formula. In what I am trying to do, all of my independent variables are qualitative, not quantitative. For example, for each of two options, "option A" and "option B" I have

Dear Prof. Ripley and Christoph, thank you very much for your comments. You have helped me a lot. Thanks, Tomas Goicoa >Dear Prof. Ripley > >Thank you for your email. Yes, this is of course the correct >syntax to save us the extra calculation. And I forgot the >"lower.tail = FALSE" for pf() in my example to obtain the >p-value. > >Thank you for the

Dear R-Community! For example I have a study with 4 treatment groups (10 subjects per group) and 4 visits. Additionally, the gender is taken into account. I think - and hope this is a goog idea (!) - this data can be analysed using lme as below. In a balanced design everything is fine, but in an unbalanced design there are differences depending on fitting y~visit*treat*gender or

Dear all, I am quite new at R and have a question about using lme with crossed random factors. I followed the instructions of Pinheiro & Bates, but that did not work because of the non grouping of my data. Reading prior threads ( http://www.mail-archive.com/r-help@stat.math.ethz.ch/msg10849.html), I found a solution to deal with non grouped data and crossed random factors in lme, by defining

On page 165 of Mixed-Effects Models in S and S-Plus by Pinheiro and Bates there is an example of using lme() in the nlme package to fit a model with crossed random factors. The example assumes though that the data is grouped. Is it possible to use lme() to fit crossed random factors when the data is not grouped? E.g., y <- rnorm(12); a=gl(4,1,12); b=gl(3,4,12). Can I fit an additive model

Hello list members, this is rather a simple question but I can't find the solution myself, so I would appreciate your help and I apologise in advance for my ignorance. I have a data frame with a header containing variable names and I have 4 independent variables and 5 dependent variables. It looks like this: x1 x2 x3 x4 y1 y2 y3 y4 y5 1 1 33 55 4

Hi, what is the appropriate syntax to get the random error correct when performing repeated measures anova with 'lme'. let's say i have 3 independent variables, with 'aov', i would write something like: aov(dep_var~(indep_var1*indep_var2*indep_var3) + Error(subject/(indep_var1*indep_var2*indep_var3)). With 'lme' however, i can't find the right formula. i tried

Hello, I have a question regarding the syntax of the lme function in the nlme package. What I'm trying to do is to calculate an estimate of R^2 based on the likelihood ratio test. For this calculation, I need to determine the maximum log-likelihood of the intercept-only model and the model of interest (with the desired factors and interactions). My model has four independent factors (i.e. A,

Hi. I'm using lmer() from the lme4 package (version 0.8-3) and I can't get anova() to group variables properly. I'm fitting the mixed model Response ~ Weight + Experimenter + (1|SUBJECT.NAME) + (1|Date.StudyDay) where Weight is numeric and Experimenter is a factor, ie, > str(data.df) `data.frame': 4266 obs. of 5 variables: $ SUBJECT.NAME : Factor w/ 2133 levels

I have been using lme from nlme to do a 3-way anova with all the effects treated as random. I was wondering if someone could direct me to an example of how to do this using lmer from lme4. I have 3 main effects, tim, trt, ctr, and all the interaction effects tim*trt*ctr. The response variable is ge. Here is my lme code: dat <-

And a last post from Paul Gilbert. Thanks to all! This disscusion was really beneficial for me! -----Original Message----- From: Paul Gilbert [mailto:pgilbert at bank-banque-canada.ca] Sent: sre 2005-06-08 21:01 To: Gorjanc Gregor Subject: Re: [R] Random seed problem in MCMC coupling of chains Gorjanc Gregor wrote: > Thanks to Paul and Gabor for additional tips/examples. Actually, I find

I've tried without success to calculate interaction and main effects contrasts using R. I've found the functions C(), contrasts(), se.contrasts() and fit.contrasts() in package gmodels. Given the url for a small dataset and the two-way anova model below, I'd like to reproduce the results from appended SAS code. Thanks. --Dale. ## the dataset (from Montgomery) twoway <-

Hi all, I am not a very proficient R-user yet, so I hope I am not wasting people?s time. I want to run a linear mixed model with 3 random factors (A, B, C) where A and B are partially crossed and C is nested within B. I understand that this is not easily possible using lme but it might be using lmer. I encountered two problems when trying: Firstly, I can enter two random factors in lmer but