similar to: [OT] 1 vs 2-way anova technical question

Hello, I have one replication (r=1 of the 2^k*r) of a 2^k experimental design in the context of performance analysis i.e. my response variables are Throughput and Response Time. I use the "aov" function and the results look ok: > str(throughput) 'data.frame': 286 obs. of 7 variables: $ Time : int 6 7 8 9 10 11 12 13 14 15 ... $ Throughput : int 42 44 33 41

Hi, I've done a repeated ANOVA looking as follows: model<-aov(y~a*b*c+Error(d)) Now I want to get rid of some or all of the interactions. But neither AIC nor anova work. What is the appropriate function to test my model?

Hello to All. I'd want to use a one-way ANOVA. This means that I have only one factor, with, lets say, 5 levels. I made a dataframe, called "DATA", with two Columns: A, that is my response, and it is "class numerical". B, that defines the different levels of my factor, and it is "class factor". If I want to use a fixed effect model, I know that the formula I have

I have 3 questions (below). Background: I am teaching an introductory statistics course in which we are covering (among other things) repeated measures anova. This time around teaching it, we are using R for all of our computations. We are starting by covering the univariate approach to repeated measures anova. Doing a basic repeated measures anova (univariate approach) using aov() seems

Hi I have data from 12 subjects. The measurement is log(expression) of a particular gene and can be assumed to be normally distributed. The 12 subjects are divided into the following groups: Infected, Vaccinated, Lesions - 3 measurements Infected, Vaccintaed, No Lesions - 2 measurements Infected, Not Vaccinated, Lesions - 4 measurements Uninfected, Not Vaccinated, No Lesions - 3 measurements

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

Hi All, I have performed a 3-way ANOVA analysis for my experimental data using aov function. My simple R funtion for this is: 3aof <- function(x){ m <- data.frame(R,S,T, x); anova(aov(x ~ R+S+T+R*S+R*T+S*T+R*S*T, m) ) } Now, I am getting P values for all the main and interactions effects. If I want to perform postdoc test on one of my main effects, say T, what method I should use (i have

Hi there, Is there a function that can do a Fisher's LSD multiple comparisons in a two-way ANOVA? I hope to get a result similar with TukeyHSD(). Especially, I hope to know the significance of comparisons between the interactions of two factors. In the following example: x <- c(76, 84, 78, 80, 82, 70, 62, 72, 71, 69, 72, 74, 66, 74, 68, 66, 69, 72, 72, 78, 74, 71, 73, 67, 86, 67, 72,

In my undertstanding of the problem, the model lme1 <- lme(resp~fact1*fact2, random=~1|subj) should be ok, providing that variances are homogenous both between & within subjects. The function will sort out which factors & interactions are to be compared within subjects, & which between subjects. The problem with df's arises (for lme() in nlme, but not in lme4), when

Hi all, I'm doing a 3-way ANOVA like this: summary(aov(formula('FP ~ (lum * obj * man)3 - Error(vp/(lum * obj * man)3)'),data=dataf)) But in the output I only get 1- and 2-way effects, like this one: Error: vp:obj:man Df Sum Sq Mean Sq F value Pr(>F) obj:man 1 1.5291e-34 1.5291e-34 5.7011 0.0542 . Residuals 6 1.6093e-34 2.6822e-35 --- Signif. codes: 0 '***' 0.001

Dear ExpeRts, Suppose I have a typical psychological experiment that is a within-subjects design with multiple crossed variables and a continuous response variable. Subjects are considered a random effect. So I could model > aov1 <- aov(resp~fact1*fact2+Error(subj/(fact1*fact2)) However, this only holds for orthogonal designs with equal numbers of observation and no missing values.

Hi all, I've spent quite a lot of time searching through the help lists and reading about how best to run perform a 2-way ANOVA with unbalanced data. I realize this has been covered a great deal so I was trying to avoid adding yet another entry to the long list considering the use of different SS, etc. Unfortunately, I have come to the point where I feel I have to wade in and see if someone

Hi list, I was searching how to properly write a command line for a mixed ANOVA. Well honestly, there are so many material on the older post of the list that just confused me. I have five factors. Season (fixed) Beach (fixed) Line (fixed) Block (random) Strata (random) nested in Block And for each of the tree strata per block I got 3 replicates. I saw lots of things about

Dear R-help list, I am trying to do a mixed ANOVA on a 8960 x 5 dataframe. I have 3 factors for which I want to test all main effects and interactions : f1 (40 levels), f2 (7 levels), and f3 (4 levels). I also have a subject factor, subject, and a dependent variable, dv. Some more information about the factors: f2 is a between-subject factor. That is, for each level of f2 there are 8 nested

Dear R users, I have posted this question several days ago and received not a single suggestion. I believe I have provided sufficient information for at least some help. Here I repost the question with several modifications. I want to run nested fixed-factor Anova in R on different experiments. I have 48 levels of the main factor x1 and 242 levels of the nested factor z1, and continuous response

Dear All, I have a problem in understanding how the interactions of 2 ways ANOVA work, because I get conflicting results from a t-test and an anova. For most of you my problem is very simple I am sure. I need an help with an example, looking at one table I am analyzing. The table is in attachment and can be imported in R by means of this command: scrd<-

Hello, Can anyone point me to an online tutorial or book containing the easiest way to do ANOVA over the result data from a 2^k*r experiment. It is not clear to me if I can pass the raw data corresponding to each experiment or just the summarized data i.e. mean, sse, std, etc. I would like to get the: - box plot showing the effect for the different factors and levels - plot showing whether there

I am executing a Repeated Measures Analysis of Variance with 1 DV (LOCOMOTOR RESPONSE), 2 Within-Subjects Factors (AGE, ACOUSTIC CONDITION), and 1 Between-Subjects Factor (SEX). Does anyone know whether the between-subjects factor (SEX) belongs in the Error Term of the aov or not? And if it does belong, where in the Error Term does it go? The 3 possible scenarios are listed below: e.g., 1.

I have Data like this: Tone OQtil4 OQ0 GOtil4 GO0 SKrhsJ SK0 RCrhsJ RC0 1 HsLG -9.1347877 -2.97 -7.409590 -6.40 9.389357 20.60 10.688854 24.40 2 HsLG -7.9270569 -2.03 -7.861541 -6.90 10.165324 26.40 10.640183 23.10 3 HsLG -7.0394106 -1.26 -7.509566 -6.53 12.033194 30.87 9.401959 20.37 4 HsLG -6.8625610 -1.03 -7.645118 -6.68 10.372605 24.22 9.378803 17.82 5

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