similar to: Use of step() with an unbalanced ANOVA model

I have a three-way unbalanced ANOVA that I need to calculate (fixed effects plus interactions, no random effects). But word has it that aov() is good only for balanced designs. I have seen a number of different recommendations for working with unbalanced designs, but they seem to differ widely (car, nlme, lme4, etc.). So I would like to know what is the best or most usual way to go about working

Dear all, I need an help because I am really not able to find over internet a good example in R to analyze an unbalanced table with Anova with repeated measures. For unbalanced table I mean that the questions are not answered all by the same number of subjects. For a balanced case I would use the command aov1 = aov(response ~ stimulus*condition + Error(subject/(stimulus*condition)), data=scrd)

Hello Everyone, *Question: *How do you calculate the sum of squares for a two-way _unbalanced_ ANOVA? *What I have done:* I have found many useful tutorials online for running a balanced two-way ANOVA but I haven't had much luck for running a unbalanced two-way ANOVA. From what I have read, the trouble with running an unbalanced two-way ANOVA, is that things get tricky when calculating

Hi, i'm looking for an R function to fit a one-way ANOVA with one factor containing 10 levels. The factor levels have different numbers of observations (varying between 20 to 40). For most of the dependent variables i'm testing there are unequal variances among the factor levels. I see the function oneway.test: oneway.test(variable ~ factor, data=dataset) which by default does not

Hi, I have an unbalanced dataset on which I would like to perform a one-way anova test using R (aov). According to Wannacott and Wannacott (1990) p. 333, one-way anova with unbalanced data is possible with a few modifications in the anova-calculations. The modified anova calculations should take into account different sample sizes and a modified definition of the average. I was wondering if the

Hello R-users, I am trying to obtain Type III SS for an ANOVA with subsampling. My design is slightly unbalanced with either 3 or 4 subsamples per replicate. The basic aov model would be: fit <- aov(y~x+Error(subsample)) But this gives Type I SS and not Type III. But, using the drop() option: drop1(fit, test="F") I get an error message: "Error in

Hello, I'm wondering what's the best way to analyse an unbalanced design with a low number of replicates. I'm not a statistician, and I'm looking for some direction for this problem. I've a 2 factor design: Factor batch with 3 levels, and factor dose within each batch with 5 levels. Dose level 1 in batch one is replicated 4 times, level 3 is replicated only 2 times. all

Hi all I'm trying to complete a textbook example originally designed for SPSS in R, and I therefore need to find out how to compute an unbalanced ANOVA in R. I did a search on the mailinglist archives an found a post by Prof. Ripley saying one should use the lme function for (among other things) unbalanced ANOVAs, but I have not been able to use this object. My code gives me an error.. Why

hi all - i'm having trouble using lme to specify a mixed effects model. i'm pretty sure this is quite easy for the experienced anova-er, which i unfortunately am not. i have a data frame with the following columns: col 1 : "Score1" (this is a continuous numeric measure between 0 and 1) col 2 : "Score2" (another continuous numeric measure, this time bounded between 0

Hi all, Say I have the following data: a<-data.frame(col1=c(rep("a",5),rep("b",7)),col2=runif(12)) a_aov<-aov(a$col2~a$col1) summary(aov) Note that there are 5 observations for a and 7 for b, thus is unbalanced. What would be the correct way of doing anova for this set? Thanks, Sachin [[alternative HTML version deleted]]

Does someone out there have an example of R-code for a split-split plot ANOVA using aov or another function? The design is not balanced. I never set up one in R before and it would be nice to see an example before I tackle a very complex design I have to model. Thanks, Mike Mike Saunders Research Assistant Forest Ecosystem Research Program Department of Forest Ecosystem Sciences University of

Dear R-list members, I've been struggling with the proper setup for analysing my data. I've performed a route choice experiment, in which participants had to make a choice at each junction for the next road. During the experiment they received traffic information, but also encountered two different accidents. They also made trips without accidents. What I'm interested in is to

Hello all, This may be a simple question to answer, but I'm a little bit stumped with respect to the calculation of the F statistics in nested anovas with unbalanced design in R. In my case, I have 11 vegetation transects (with 1000 10cmx10cm squares), where we estimated shrub cover. We have two different treatments: wildfire (4 transects) and prescribed burning (7 transects) and we want to

Hello, For an experiment, I selected plots of land within a forest either with honeysuckle or without honeysuckle. Thus, my main factor is fixed, with 2 levels: "honeysuckle present"(n=11) and "honeysuckle absent"(n=8). Within each plot of land, I have a "trenched" subplot and an "untrenched" subplot. Within each subplot of every plot, I measured soil

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(),

Dear list, Could someone show me where can I find the detailed formula on how to calculate the two way anova with unbalanced design? Say, if I have 2*2 design with 10,20,30,40 samples in each of the 2*2 cells. Most of the places I've googled only show how to calculate using software such as R, but not clear the detailed formula for calculating this. Thanks, Jack [[alternative HTML version

Hello everybody, I have got a data set with about 400 companies. Each company has a score for its enviroment comportment between 0 and 100. These companies belong to about 15 different countries. I have e.g. 70 companies from UK and 5 from Luxembourg,- so the data set is pretty unbalanced and I want to do an ANOVA. Somthing like aov(enviromentscore~country). But the aov function is just for

Dear R people: I wish to switch from SPSS to R, but there is one particular type of ANOVA design that cannot be done in R. Or more likely, it can be done, but it is nowhere documented. The problem is typical for psychologists: You have a repeated measures design with different groups of subjects. Now, this can be done with the aov command, but the number of subjects in both groups must be

Dear R-list! My question is related to an Anova including within and between subject factors and unequal group sizes. Here is a minimal example of what I did: library(car) within1 <- c(1,2,3,4,5,6,4,5,3,2); within2 <- c(3,4,3,4,3,4,3,4,5,4) values <- data.frame(w1 = within1, w2 = within2) values <- as.matrix(values) between <- factor(c(rep(1,4), rep(2,6))) betweenanova <-

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