similar to: ANOVA in R

Hello, I am getting output from anova() and summary(aov()) that depends on the order of the factors in the fitted model object, and this has me baffled. I see this dependency with the data.frame below but not with an example (table 6.4) from Montgomery's DOE book. This is with R 1.3.0 on Debian GNU-Linux. Where have I gone wrong? > centerpts run sample CH50mg 1 day1 dev126 0.56 2

thanks to your guys help I am closer to solving my problem but I have some small problem. So let's say I start with >data number day hour 1 17 10 2 17 11 3 17 6 4 18 4 5 18 10 6 19 8 7 19 8 I want to split to odd days, which I am able to do, I call this object frames, which looks like: > frames $`1` c1 day1 hour1 1 1 17 10 2 2 17 11 3 3 17 6 $`2` c1 day1

Dear community, can anyone provide a snippet of code to force the lm() to fit a model with terms in the formula in an arbitrary order? I am interested in something like: lm(y ~ A * B + C, data=data) where the interaction of A and B should be in the formula before C. My goal is to simplify my presentation of models using the anova() statement. I have found that this should be possible using

Hi, what is the appropriate syntax to get the random error correct when performing repeated measures anova with 'lme'. let's say i have 3 independent variables, with 'aov', i would write something like: aov(dep_var~(indep_var1*indep_var2*indep_var3) + Error(subject/(indep_var1*indep_var2*indep_var3)). With 'lme' however, i can't find the right formula. i tried

Hi everyone, I'm witnessing an odd modelling phenomenon that I can't explain. If anyone has seen this before, or can explain what's going on would let me know, I'd be very grateful! Especially if I'm just being dim. I'm fitting a pair of continuous variates and their interaction to some residuals from another model. The sequential anova statement changes with the term

Hi, I'm working on leaf characteristics of trees. Each tree is characterised by about 10 leaf traits. The trees were sampled at 9 different locations (about 20 to 30 trees/location, NOT balanced), grouped in 3 different climatic zones (Sahelian, Soudanian and Guinean) (NOT balanced). Further, each tree is characterised by some degree of human pressure (mutilation degree), in total 4

Dear R users, I have a rather knotty analysis problem and I was hoping that someone on this list would be able to help. I was advised to try this list by a colleague who uses R but it is a statistical inquiry not about how to use R. In brief I have a 3x2 anova, 2 tasks under 3 conditions, within subjects. I also took a variety of personality measures that might influence the results under the

I would like to include this in a package. The S3 methods on R CMD check says * checking S3 generic/method consistency ... WARNING window: function(x, ...) window.chron: function(data, day1, hour1, day2, hour2, ...) See section 'Generic functions and methods' of the 'Writing R Extensions' manual. I have looked and can not figure it out. This function is for convience. What

Hi, I have a data.frame SAMPLES with columns: Site Site# Season Day1 Day2 Day3 Day1, Day2, Day3 are class "Date", the other columns are numeric or factor. I have a date "mydate" that may or may not be listed in my data.frame and I need to find that out. If "mydate" is there, I want to get the number of the data.frame row where it occurs.

Hi all, I have been bumbling around with r for years now and still havent come up with a solution for plotting reliable graphs of relationships from a linear regression. Here is an example illustrating my problem 1.I do a linear regression as follows summary(lm(n.day13~n.day1+ffemale.yell+fmale.yell+fmale.chroma,data=surv)) which gives some nice sig. results Coefficients:

I have an ANOVA model with 2 factors "Environment" and "Site", "Diameter" is the response variable. Site should be nested within Environment. Site is also a random factor while Environment is fixed. I can do this analysis using the "aov" function by using these commands: >model<-aov(Diam~Env+Error(Env%in%Site),data=environ) >summary(model)

HI all, raw_urine = read.table("Z:\\bruce.9.3.09.sample.stability.analysis\\urine\\mz.spot.sam.dat.new", header = TRUE ) pvalue = read.table("Z:\\bruce.9.3.09.sample.stability.analysis\\urine\\all.urine.features.t.test.result", header = TRUE ) library(compositions) p = function(a,b){ y = pvalue[,a] if(y<0.01){ index = which(y, arr.ind=TRUE) day1 = raw_urine[index,3:7] day2 =

Can someone help me with the proper usage of the reshape function? Let's say I have a dataset with columns like this (wide format): Id Sex Group Test Day1 Day 2 Day 3... And I want to transpose this into something like this (thin): Id Sex Group Test Time Where the new column labeled time contains all the time variables (Day 1, Day 2, Day 3...) that were in the wide

Dear R users I have built several glmm models using glmmPQL in the following structure: m1<-glmmPQL(dev~env*har*treat+dens, random = ~1|pop/rep, family = Gamma) (full script below, data attached) I have tried all the methods I can find to obtain some sort of model fit score or to compare between models using following the deletion of terms (i.e. AIC, logLik, anova.lme(m1,m2)), but I

Hello all, I want to assign relative score to the predictor variables on the basis of its influence on the dependent variable. But I could not find any standard statistical approach appropriate for this purpose. Please suggest the possible approaches. Thanks in advance, Rupendra Chulyadyo Institute of Engineering, Tribhuvan University, Nepal [[alternative HTML version deleted]]

I'm investigating a number of dependent variables using mixed models, e.g. data.lmer45 = lmer(ampStopB ~ (type + stress + MorD)^3 + (1|speaker) + (1|word), data=data) The p-values for some of the 2-way and 3-way interactions are significant at a 0.05 level and I have been trying to find out how to understand the exact nature of the interactions. Does anyone know if it is possible to run

Hello, helpeRs, I recently used a linear mixed effects model followed by ANOVA to assess the relationship between a categorical predictor variable with 2 levels (and random effects) and a numeric response variable. As I was concerned about the lack of a power analysis prior to data collection, it was suggested that I use an equivalence test to complement the conventional hypothesis test.

Hello, suppose I have a multivariate multiple regression model such as the following: > DF<-data.frame(x1=rep(c(0,1),each=50),x2=rep(c(0,1),50)) > tmp<-rnorm(100) > DF$y1<-tmp+DF$x1*.5+DF$x2*.3+rnorm(100,0,.5) > DF$y2<-tmp+DF$x1*.5+DF$x2*.7+rnorm(100,0,.5) > x.mlm<-lm(cbind(y1,y2)~x1+x2,data=DF) > coef(x.mlm) y1 y2 (Intercept)

I am analyzing data from an experiment with two factors: Carbon (+/-) and O3 (+/-), with 4 replicates of each treatment, and 4 harvests over a year. The treatments are assigned in a block design to individual Rings. I have approaches this as a repeated measures design. Fixed factors are Carbon, O3 and Harvest, with Ring assigned as a random variable. I have performed repeated measures analysis

Hi everyone, I'm involved in a discussion with a colleague. He suggested a sample design for a finite-sized process that (to all intents and purposes) involves tossing a coin and examining the unit if the coin shows Heads. I should emphasize that we're both approaching the problem from a design-based sampling theory point of view. So I have no argument about the appropriateness of the