similar to: Long model specification causes aov() to abort with error

Dear R Users, I want to write a function that applies to the dataframe and variables that were used in a previous call to lm or aov. In order to do this, I need to write a function that applies to the output of lm or aov, and yields the names of the dataframe and variables that were used in the lm or aov analysis. For example, suppose that I give the command: aov.out <- aov( Rt ~

Dear listmembers, I would like to define a class with a slot that takes either an object of class aov or NULL. I have been reading "S4 Classes in 15 pages more or less" and "Lecture: S4 classes and methods" #First I tried with list and NULL setClass(listOrNULL") setIs("list", "listOrNULL") setIs("NULL", "listOrNULL") #doesn't

Hi, I have a question of aov. e.g. aov.ex = aov(x~y) summary(aov.ex) The aov summary will print to the screen. How can I extract the aov result, in particular the values of Pr(>F) and F value into a vector so that I can use them for other use? Thanks. -- Waverley @ Palo Alto

Hello, I have a simple question about using the aov function syntax (ie. * + or :) for the interaction of 2 factors. I have read the help files, and researched other sites, and have included my source files. My goal is to measure the signifigance of the interaction between population and condition (aka. population:condition). I can't seem to figure it out. 1. In the first example the

Dear Madame, Dear Sir, I am able to obtain the coefficients from a 'summary' of 'lm', but NOT from a 'summary' of 'aov'. The following example shows my steps. ## Initialize rm(list = ls()) # remove (almost) everything in the working environment utils::data(npk, package="MASS") # get data model <- yield ~ block + N*P*K ## Using lm npk.lm <-

Hi everyone, I'm having a little trouble with working out what formula is better to use for a repeated measures two way anova. I have two factors, L (five levels) and T (two levels). L and T are both crossed factors (all participants do all combinations). So, I do: summary(aov(dat~L*T+Error(participant/(L*T)),data=dat4)) But get different results with:

Dear R-Community! The example "oats" in MASS (2nd edition, 10.3, p.309) is calculated for aov and lme without interaction term and the results are the same. But I have problems to reproduce the example aov with interaction in MASS (10.2, p.301) with lme. Here the script: library(MASS) library(nlme) options(contrasts = c("contr.treatment", "contr.poly")) # aov: Y ~

Just as a note.. Xquartz is going to have RANDR capabilities in Xquartz 2.6.0, thanks to the work of Jeremy Huddleston and Jan Hauffa. Right now on Macports if you install xorg-server-devel it will be a build with what is currently in of RandR... its still not totally finished, but it would be nice if some Mac Users could test how it works so far with Wine... if we find bugs now, it could help

I've recently run into the problem of using aov with nested factors, and wanting to get the type II and III sums of squares. Normally Anova from the car package would do fine, but it doesn't like having an Error included, so my.aov <-aov(Response ~ Treatment + Error(Treatment:Replicate)) Anova(my.aov, type="II") yields Error in Anova(nested.anova) : no applicable method

<ripley at stats.ox.ac.uk> wrote: > Hint 2: in the absence of balance, ...., and lme can do that Would it be possible to make aov like wrappers to the various special lm variants allowing for a uniform syntax for anova? aov(resp~f1*f2+Error(S/(f1*f2))) ## uses lm aov.lme(resp~f1*f2+Error(S/(f1*f2))) ## uses lme aov.rlm(resp~f1*f2+Error(S/(f1*f2))) ## uses rlm ... I'd do it, but I

Dear R-help, I'm facing a problem with defining a repeated measures anova with weighted data. Here's the code to reproduce the problem: # generate some data seed=11 rtrep <- data.frame(rt=rnorm(100),ti=rep(1:5,20),subj=gl (20,5,100),we=runif(100)) # model with within factor for subjects/repeated measurements, no problem aov(rt~ti + Error(subj/ti),data=rtrep) #model with weights

Dear useRs, I've just encountered a serious problem involving the F-test being carried out in aov() and anova(). In the provided example, aov() is not making the correct F-test for an hypothesis involving the expected mean square (EMS) of a factor divided by the EMS of another factor (i.e., instead of the error EMS). Here is the example: Expected Mean Square

Greetings to all, This is my model: aov.fit<-aov(Y~A+B+C+D+E+A:C+A:E) In summary(aov.fit) all F values are comptuted by eg MS(A)/MS(Residuals). This is not correct (or what I want), except for F(B) and F(A:E). I suppose P values are not correct either. Is it possible with aov to define the way F computations will be done? I 'd like them to be like this: F(A)=MS(A)/MS(E),

Hi R-listers, I am wondering if the function aov() in plyr is appropriate for two different types of tests: 1) > summary(aov(EDI ~ VegIndex, data=data.to.analyze)) AND 2) > summary(aov(HSuccess ~ VegIndex + Aeventexhumed + VegIndex:Aeventexhumed, data=data.to.analyze)) the later inclusive of an interaction of the two explanatory variables. This is for obtaining P value and F

Hello R gurus, I am a beginner with R. I am doing an ANCOVA analysis using 'aov,' and need some help understanding how 'aov' works. I have a dataset (taken from http://faculty.vassar.edu/lowry/ch17pt2.html) looking at hypnotic induction. The variable 'X' is a measure of how susceptible the subject is to being hypnotized, the variable 'Y' is how well the

How do we specify a contrast interaction matrix for an ANOVA model? We have a two-factor, repeated measures design, with Cue Direction (2) x Brain Hemisphere(2) Each of these has 2 levels, 'left' and 'right', so it's a simple 2x2 design matrix. We have 8 subjects in each cell (a balanced design) and we want to specify the interaction contrast so that: CueLeft>CueRght

Hi all, I have a question regarding the anova function aov(). I want to perform an anova calculation using one grouping variable but more than one numerical variables: So instead of: aov(v ~ g) I want something like aov(v1 + v2 + v3 ~ g) Essentially I want to find out whether the variables v1, v2, v3, etc can collectively discriminate between different values of variable g. Could

hi, another anova question !! how does aov() treat missing values ? since it might depend on my experimental design: i have a within subject design with two factors ... i use aov() like this: aov(performance~type*block+Error(subj/(type+block)),data=anovaData) now it might happen that i have a couple of NaN data points in my result dataFrame. how can i know/control how aov() is treating those

(A repost of this request with a bit more detail) Hi, All. I'd like to calculate effect sizes for aov or lme and seem to have a bit of a problem. partial-eta squared would be my first choice, but I'm open to suggestions. I have a completely within design with 2 conditions (condition and palette). Here is the aov version: > fit.aov <- (aov(correct ~ cond * palette +

Hi all, I posted this question several days ago, but did not get any answer until now. Since I still have no clue about the source of this error message, I repost a description of the problem including some code: A student at our institute fitted an aov model, and got the following error message: > plot(p.aov) Hit <Return> to see next plot: Hit <Return> to see next plot: