similar to: main effect & interaction in 2-way ANOVA

R 2.9.1 Windows XP I am fitting a random effects ANOVA with two factors Group which has two levels and Time which has three levels: fita<-lme(Post~Time+factor(Group)+factor(Group)*Time, random=~1|SS,data=blah$alldata) I want to get the omnibus significance tests for each factor and the interaction. I believe I can get the omnibus test for the interaction by running the model:

I've tried without success to calculate interaction and main effects contrasts using R. I've found the functions C(), contrasts(), se.contrasts() and fit.contrasts() in package gmodels. Given the url for a small dataset and the two-way anova model below, I'd like to reproduce the results from appended SAS code. Thanks. --Dale. ## the dataset (from Montgomery) twoway <-

Hi, I use anova() to find interaction effect and then I need to find data with main effect from those data with interaction effect. How to do that? I used : anova(lm(data~factor1*factor2)), then select data with interaction effect. Then I need to select those data also with main effect of factor1 or factor2, from previous selected data. How to do that? Many thanks for your time on my

When I perform a two-way anova on my dataframe "pin", I can't get any indication about the interaction among the two factors "gen" and "con" while the statistics about the significance relative to the two factors alone is correct. I wrote this: *pvalues_genotipo<-sapply(pin[,1:(length(pin)-2)],FUN=function(x) summary(aov(x~gen*con)))* Where is the mistake?

This question will probably get me in trouble on theoretical grounds, but I will pose it anyway. The situation: I recently ran a field study looking for differences in sugarbeet cultivar tolerance to a specific herbicide. The study was set up so that 37 cultivars were treated with 4 different applications of the herbicide (37*4 factorial). In doing so, we found that the interaction effect was

Hi all, this is OT. I've performed a two way anova with repeated mesurements. There's no significant main effect, but the interaction is significannt. Are there any tests in R to examine the interaction? Many thanks, Sven -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send

Hi all I am running a 2-way repeated measure anova with 1 between-subjects factor (Group=treatment, control), and 1 within-subject factor (Time of measurement: time1, time2). I extract the results of the anova with: summary(aov(effect ~ Group*Time + Error=Subj/Time, data=mydata)) Now, this must be clearly a dumb question, but how can I quickly extract in R all the post-hoc t-tests for the

Hi, let's say I have a simple ANOVA model with 2 factors A (level A1 and A2) and B (level B1 and B2) and their interaction: aov(y~A*B, data=dat) It turns out that the interaction term is not significant (e.g. P value = 0.2), but if I used glht() to compare A1 vs. A2 within each level of B, I found that the comparison is not significant when B=B1, but is very significant (P<0.01) when

I've been searching for an answer to this for a while but no joy. I have a simple 2-way ANOVA with an interaction. I'd like to determine the power of this test for each factor (factor A, factor B, and the A*B interaction). How can I do this in R? I used to do this with "proc Glmpower" in SAS, but I can find no analogue in R. [[alternative HTML version deleted]]

Dear R experts I am running a negative-binomial GLM (glm.nb) to test the null hypotheses that species 1 and 2 are equally abundant between site 1 and site2, and between each other. So, I have a 2x2 factorial design with factors Site (1,2) and Taxon (1,2). Since the Site:Taxon interaction is significant, I need to do the equivalent to a "post-hoc test" for ANOVA, however, the same tests

HI Dave, My comment was based? on: " >The main question with this test was if the interaction term is significant (i.e. growth rate). However, my question is could I also look at the p-values of the main effects to ?>say if body mass increase significant with body mass?" Here, the result shown were from the summary of the linear model.?? We report the p-values of the main

Hello, I've been running some HLMs using the lme function quite happily; it does what I want and I'm pretty sure I understand it. The issue is that I'm currently trying to estimate a model with a 14-level "nusiance" factor as an independent variable...which makes the output quite ugly. All I'm really interested in is the question of whether these factor as a whole

Hi, I wish to compute multivariate test statistics for a within-subjects repeated measures design with anova.mlm. This works great if I only have two factors, but I don't know how to compute interactions with more than two factors. I suspect, I have to create a new "grouping" factor and then test with this factor to get these interactions (as it is hinted in R News 2007/2), but

Dear Rgurus, Interaction terms in the linear models function lm are specified by the colon : eg: x ~ a + b + a:b a shortcut for the above is: x ~ a*b the output if calling anova on the lm object will be the same in both cases a .... b .... a:b ... Resdiuals ... What I am wondering is how the interaction term (a:b) given above should be written in a table in an manuscript? a ) a*b b )

Hi, I'm a bit new to R and I would like to know how can I compare simple main effects when using the aov function. I'm doing a mixed model ANOVA with two between subjects variables and one within. When I get an interaction of two of the variables I don't know how to check for simple main effect of that interaction (A at B1 and A at B2 for example). The aov function is very simple but

Dear R-experts! In a meta-analysis (metafor) I would like to assess the effect of two categorical covariates (A & B) whereas they both have 4 levels. Is my understanding correct that this would require to dummy-code (0,1) each level of each covariate (A & B)? However I am interested in the main-effects and the interaction of these two covariates and the dummy-coding would only allow to

Hello, as far as I see, R reports type I sums of squares. I'd like to get R to print out type III sums of squares. e.g. I have the following model: vardep~factor1*factor2 to get the type III sum of squares for factor1 I've tried anova(lm(vardep~factor2+factor1:factor2),lm(vardep~factor1*factor2)) but that didn't yield the desired result. Could anyone give me a hint how to proceed?

I've noticed with certain datasets that when I try to do an anova and test for main effects and interaction for two explanatory variables, sometimes the main effect results are given but not the interaction results. For example, ex1 = aov(Score ~ var1*var2, data=myData) summary(ex1) gives me only the main effects for var1 and var2, but not the interaction. I also tried ex1 = aov(Score ~

Hi So carrying on my use of analysis of variance to check for the effects of two factors. It's made simpler by the fact that both my factors have only two levels each, creating four unique groups. I have a highly significant interaction term. In the context of the experiment, this makes sense. I can visualise the data graphically, and sure enough I can see that both factors have different

Hi all, This could be naivety/stupidity on my part rather than a problem with model output, but here goes.... I have fitted a fairly simple model m1<-glm(count~siteall+yrs+yrs:district,family=quasipoisson,weights=weight,data=m[x[[i]],]) I want to know if yrs (a continuous variable) has a significant unique effect in the model, so I fit a simplified model with the main effect ommitted...