similar to: Problems extracting p-value from summary(aov(...))

Dear R users, I would like to extract df and Mean Sq values. I tried several things (e.g., str(model1), names(model1)) but I can't find a way to extract these values. I also tried to search using RSiteSearch. Any help will be appreciated. Thanks Taka, model1<-aov(dv~iv.1*iv.2*iv.3) summary(model1) Df Sum Sq Mean Sq iv.1 1 3.200 3.200

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 ~

Hello. When one types "summary(fit)", where fit is an object from a call to aov(), one gets an ANOVA table. I want to save just one element of this summary. How does one extract this? As an example, here is my output from a split plot ANOVA: > summary(out)$"Error: soil.cores$block:soil.cores$treatment" Df Sum Sq Mean Sq F value Pr(>F)

Dear All It is a little hard to give a good small example of my question,so I will show the full data on the bottom and the attachment.Maybe some one could tell me an appropriate way to show it.I'm sorry for the inconvenience. Q:How to generate a 53*53 diagonal matrix by my data Some problems confused me are that: 1.Since it is a diagonal matrix,I have tried to transform col1 and col2 to

<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

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

Hi, This question is related to the bwplot issue I reported yesterday. I have a 3 factors (2x3x2) dataset that I collapsed into a 2 factors dataset (3x2 = sizexModality). For size==small, I have 2 observations per subject (Snr), for the other sizes only 1. I reckoned that aov (and underneath, lm) might handle this as it should, since the subjects are idendified, when I do > aov(

It doesn't print anything: the summary.aov (or summary.aovlist) print method does. ?summary.aov tells you the structure of the objects they return. On Tue, 15 Feb 2005, RenE J.V. Bertin wrote: > I'd like to annotate a plot with the output of summary(aov(model)), > ideally just with the significant effects. I don't find a means to > redirect what that command prints into

Dear all, glht (from the multcomp package) needs a term and a model component in it's fitted model. In fitted models from e.g. repeated measurements ANOVAs I do not find neither model nor term. Is it possible to build together a model and term component myself, so that glht will work for repeated measurements ANOVAs? If so, how would I do that? Best regards, Michael Zehetleitner

I could not find any documentation about aov() and Error() usage. Both help pages (for aov and Error) does not specify it. I'm mainly interested in aov with random effects. Thanks a lot. ---------------------------------------------------------------- This message was sent using IMP, the Internet Messaging Program.

Hi, I have been trying to work with error terms given back from aov to make confidence intervals. However, the numbers seem to be incorrect. If there is more than one term in the ANOVA then the error terms can be inflated by the number of factors in the extra terms. The F's are correct so it is right back to the SS. I was wondering if this is standard practice for stats programs or

Hi, I am trying to use mice() with an aov() model.I can fun mice() fine but when I try to pool the results it doesn't work. For example, I can run the following: fit=with(data=imp,exp=aov(Y~B*P*T + Error(S/(B*P*T)))) pool(fit) # doesn't work Where B, P, and T are within subject (S) factors. However, I can't go the next step to pool these results as I am guessing this hasn't been

Hello, I currently run aov in the following way: > throughput.aov <- aov(log(Throughput)~No_databases+Partitioning+No_middlewares+Queue_size,data=throughput) > summary(throughput.aov) Df Sum Sq Mean Sq F value Pr(>F) No_databases 1 184.68 184.675 136.6945 < 2.2e-16 *** Partitioning 1 70.16 70.161 51.9321 2.516e-12 *** No_middlewares 2 44.22

Try vcov() from the MASS library. -----Original Message----- From: Sven Garbade [mailto:garbade at psy.uni-muenchen.de] Sent: Monday, June 24, 2002 11:19 PM To: R-Help Subject: [R] Covarianz matrix from an aov model Hi all, how can I extract the covariance matrix from an object of class aov? Thanks, Sven -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-. -.- r-help

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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 there, some statistical programs (e.g. SPSS) calculate a correction of the degrees of freedom in a repeated measure analysis of variance (see Greenhouse-Geisser (1958) or Huynh-Feld (1976)) by a factor epsilon. This factor is used to correct the deg. of freedom to get a corrected f-test. Is this also possible with R? Thanks, Sven P.S.: I read in the lm help page: singular.ok logical,

Hi, All. I'd like to calculate effect sizes for aov or lme and seem to have a bit of problems. partial-eta squared would be my first choice, but I'm open to suggestions. Here is the aov version: > fit.aov <- (aov(correct ~ cond * palette + Error(subject), data=data)) > summary(fit.aov) Error: subject Df Sum Sq Mean Sq F value Pr(>F) Residuals 15 0.17326

Hello, Are there any access functions to the various residual variables that should result from a repeated measures ANOVA ? MyAOVObject$residuals does not exist, and simply printing MyAOVObject gives a very long print of all fields in the result list, many of which I can't see what they are exactly : $error.qr$qraux, for instance. What I would like basically is to inspect those residuals

Why would someone use lm and ANOVA (anova(lm(x))) instead of AOV (or the other way around)? The mean squares and sum of squares are the same, but the F values and p-values are slightly different. I am modeling a dependent~independent1*independent2. Thanks, Matt Bridgman [[alternative HTML version deleted]]