similar to: balance in AoV (was aov() and NaN)

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

Hi all, I am trying to get to grips with rpart, and find it not very easy given the information that comes with the package. Contrary to e.g. the ctest package docs, it doesn't say when "an rpart" could be used, and/or how to interpret the results. Here are a few of the open questions I have: 1) Read in ?rpart: ...method: one of.... If y is a survival object... A similar

I now have versions of code that is destined (I believe) for 0.63 which is in a suitable state for comment. The files are at ftp://ftp.stats.ox.ac.uk/pub/R (Our www server is being moved, so may be intermittently down, but this ftp server should be stable.) All are R packages, for the moment for personal use only (no re-distribution). Use with 0.62.3 or 0.63 (although I am aware of some

Dear r-bugs, I looked over the FAQ. Hope I'm reporting this correctly. I ran this on both solaris and windows. I've provided terminal snapshots which include how R was called from the command line, and the result of version at the R prompt. I have attached the .r file, and the data file and the output snapshots. Below also find everything except only a few lines of the data file. Note

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 ~

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

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

Dear R developers, while experimenting with repeated measures ANOVA, I found out that it is possible to construct a model specification that is syntactically valid, but causes aov() to abort with an error. A minimal reproducer and its output are attached to this mail. I was able to reproduce this problem with the latest SVN revision. The root cause is similar to that of bug 15377: aov()

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