search for: multistratum

Hello, I routinely use aov and and the Error term to perform analyses of variance of experiments with 'within-subject' factors. I wonder whether a notion like 'multistratum models' exists for glm models when performing a logit analysis (without being 100% sure whether this would make sense). I have data of an experiment where the outcome is a categorical variable: 20 individuals listened to 80 synthetic utterances (distributed in 4 types) and were ask classi...

Dear all, I need to understand a thing in the beheaviour of the two functions aov and anova in the following case involving an analysis of ANOVA with repeated measures: If I use the folowing command I don´t get any problem: >aov1 = aov(response ~ stimulus*condition + Error(subject/(stimulus*condition)), >data=scrd) > summary(aov1) Instead if I try to fit the same model for the

Hi R-help members, I have read a lot in the Archive about the "Type I" vs "Type III" sum of square. I think I have read confusing post so I want to have a clear idea of the problem. Here is an example. I have 3 groups of subjects of unequal sample size (G1 (n=7), G2 (n=7), G3 (n=4)). for Each subject I have 4 measures corresponding to the crossing of 2 factor (A & B)

Hi all, I search the archive for finding a simple solution for using TukeyHSD with a multistratum aov result (a repeated emasure anova). The Question have been asked but I've found no clear answer. res<-aov(y~Fa*Fb+Error(Subject/(Fa*Fb)) ) I think that the problem is that res is an aovlist object instead of the "aov" object required by TukeyHSD. Is there an easy solution t...

...o plots (irrigated or not) . Each plot is divided into two secondary parcels for the two varieties. Each of these parcels is divided into three subplots corresponding to three ordered levels of nitrogen. We found in Venables & Ripley (Modern Applied Statistics with S-plus, 3rd edition) the multistratum model for the same type of dataset but for three levels (without the "Irrigation" partition): aov(Y~N*V+Error(B/V), qr=T) which we adapted to our model: aov(Y~N*V*I+Error(B/V/I)) In Pinheiro & Bates (Mixed-effect models in S and S-plus) and as we saw in the message "Re: lme a...

...: you could try `help.search("multtest")' >help.search("multtest") ------------R Information Window ------------------------------ Help files with alias or title matching 'multtest' using fuzzy matching: eff.aovlist(base) Compute Efficiencies of Multistratum Analysis of Variance dataDensityString(Hmisc) Internal Hmisc functions .mt.BLIM(multtest) Internal multtest functions and variables SSI(spatial) Simulates Sequential Spatial Inhibition Point Process Strauss(spatial) Simulates Strauss Spa...

...roblem > > > WPhantom <wp1 at tiscali.fr> writes: > >> Thanks Brian for the reference. >> I just discover that it is available in our >> library so I going to take it & read it soon. >> Actually, I don't even know the difference >> between a multistratum vs a single-stratum AOV. A >> quick search on google returned me the R materials so that I imagine >> that these concepts are quite specific to R. > > You have to be careful not to confuse Google's view of the world with > Reality... > > The concept of error strata is...

Hi to all R lovers, I am thinking of investigating and perhaps improve the anova "aov" function in R. However before I do so I just like to see if anyone knows whether R is capable of multiple comparison tests (eg "Standard Difference of the Means, SED", "Fisher's Least Significant Difference" ...etc)? If it is, how can I do it? Thanks. Ko-Kang Wang

Hello there: Dear Dr. Murdoch, I'm a statistician at Princess Margaret Hospital. Could you please help me with the bootstrapping? Can boot function (along with boot.ci) handle multivariate statistics? My intention is that I want to group the outcome data and evaluate the proportion of patients who are positive for a marker within each group. (i.e. I have two variables, a predictor and

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

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]]

Dear all I was looking at the aov documentation page and came across the following which seems like a contradiction to me: " This provides a wrapper to |lm| for fitting linear models to balanced or unbalanced experimental designs." (I presume 'This' refers to aov) and "|aov| is designed for balanced designs, and the results can be hard to interpret without

I have a problem moving from multistratum aov analysis to lmer. My dataset has observations of ampl at 4 levels of gapf and 2 levels of bl on 6 subjects levels VP, with 2 replicates wg each, and is balanced. Here is the summary of this set with aov: >> summary(aov(ampl~gapf*bl+Error(VP/(bl*gapf)),hframe2)) > >Error: VP >...

...irst-order interaction between each factor and subjects in the model. However, this still falls short of the ideas suggested with using Error() for testing.On the other hand, Mead's "the design of experiments puts repeated measures in the same chapter with split unit design, which are multistratum design. Jonathan Baron's and Yuelin Li's document also references a number of books on this matter. I know that this point is not really suitable for r-help, but it is related indirectly with regard to the use of strata.formula in Error(). Any comments would be grateful Peter Ho ISR...

...is still falls short of the ideas >>>>> suggested with using Error() for testing.On the other hand, >>>>> Mead's "the >>>>> design of experiments puts repeated measures in the same chapter with >>>>> split unit design, which are multistratum design. Jonathan Baron's >>>>> and >>>>> Yuelin Li's document also references a number of books on this >>>>> matter. >>>>> >>>>> I know that this point is not really suitable for r-help, but it is >>>>...

I performed an aov() analysis and got the following results: Df Sum Sq Mean Sq F value Pr(>F) block 1 0.0040 0.0040 0.3282 0.5672 Residuals 269 3.2766 0.0122 Can anyone tell me how to extract the F value column and Pr(>F) column from the summary output of aov analysis? Many thanks in advance, Liqing Eco. Evol. Biol. UCIrvine

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

...: you could try `help.search("multtest")' >help.search("multtest") ------------R Information Window ------------------------------ Help files with alias or title matching 'multtest' using fuzzy matching: eff.aovlist(base) Compute Efficiencies of Multistratum Analysis of Variance dataDensityString(Hmisc) Internal Hmisc functions .mt.BLIM(multtest) Internal multtest functions and variables SSI(spatial) Simulates Sequential Spatial Inhibition Point Process Strauss(spatial) Simulates Strauss Spa...

I am using R 1.1 with Redhat 6.2 and RW 1.001 with Win98 (the upkey doesn't work on my IBM either as has been previously reported by others). The function aov doesn't return either the residuals or the residual degrees of freedom for split-plot designs. If you use the following code from Baron and Li's "Notes on the use of R for psycology experiments and questionnaires"

Hello all. This may be a trivially simple question to answer, but I'm a little bit stumped with respect to the calculation of the F statistics in nested anovas in R. If I understand correctly, the F statistic for the among-subgroups but within groups hypothesis is calculated as MS_subgroups/MS_error, while the F statistic for the factor is calculated as MS_factor/MS_subgroups (I'm