similar to: testing two-factor anova effects using model comparison approach with lm() and anova()

Hi there, I wonder if anyone can help me. I'm trying to use bargraph.CI in the Sciplot package when there is a missing combination of the factor levels. Unfortunately the standard errors on the plot do not appear to be correct. Consider an analysis consisting of two factors A and B. When all factor level combinations are present all appears fine: library(sciplot) #all data

How to generate a new factor variable by two other factor variables? For example, if I have two factor variables, factorA and factorB, factorA factorB 0 0 0 0 1 0 0 1 1 1 Is there a simple way to generate a new 4-levels factor variable as factorC factorA factorB 0 0 0 0 0 0 1 1 0 2 0 1

Hello list, I wish to perform a randomization test on the F-statistics of a 2 way ANOVA but have not been able to find out how to do so - is there a package / function that can perform this that I am unaware of? FactorA has 6 levels (0,1,2,3,4,5) whereas FactorB has 3 (1,2,3). A sample: Resp. FactorA FactorB 2 0 2 3 1 2 1 2 2 0 3 2 0 4 2 0 5 2 4 0 1 6 1 1 1 2 1 0 3 1 1 4 1 0 5 1 2 0 2 3 1 2 1

Hi, I have a lattice xyplot that contains panels according to FactorA, and curves for the 2 levels of Factor B within a panel. I try to add text in the panels of a lattice graph. I suppose I have to write a custom function (panel.txt). What I really would like is to adapt the text in the panel according to the levels of FactorA. In the manuals, I find examples for the strips using which.given

Dear All, I'm struggling a little with the behaviour of R with GLM interactions. In particular, I have a dataset with two factors - call them factor A and factor B, where I would like to fit a GLM that is factor A + (grouped factor A):factor B. To try to isolate this, I've ignored the original "factor A" part, as that I have this as a separate column in my data. So, it

Hello every body, I am trying to do a factorial anova analysis following this model: model<-anova(lm(responsevariable~factorA*factorB)) model<-anova(lm(luz$dosel~luz$estado*luz$Bosque)) Df Sum Sq Mean Sq F value Pr(>F) estado 1 6931.1 6931.1 41.6455 7.974e-06 *** Bosque 1 36.6 36.6 0.2197 0.6456 estado:Bosque 1 36.6 36.6 0.2197 0.6456 Residuals

---------- Forwarded message ---------- From: Freddy Gamma <freddy.gamma@gmail.com> Date: 2011/1/21 Subject: TRADUCING lmer() syntax into lme() To: r-sig-mixed-models@r-project.org Dear Rsociety, I'd like to kingly ask to anyone is willing to answer me how to implement a NON NESTED random effects structure in lme() In particular I've tried the following translation from lmer to

Hello everyone, I would like to perform a 2-way manova test, but I'm having some issues. I implemented like this Y<-cbind(Resp1,Resp2,Resp3,....,Respn) model<-manova(Y "tilda" FactorA*FactorB) summary.aov(model) 1. I don't know at what level I have to do the Type I error correction. Is it on p-values returned by "summary.aov(model)? Or is it when I compare each

Dear list, i have a quick and (hopefully) straightforward question regarding the plot-function after running aov. if i plot an equation like this: plot(dataSubjects~factorA, data=mydata) R gives me the boxplots for this particular factor A. my model, however contains several factors. is there a straightforward way to plot barplots for a specific factor with the constraint that those values

Dear expeRts, I am trying to wrap up a package "utilities" (for my internal use). After adding a function datNAtreat that uses mapply, R CMD check gives WARNINGs for "S3 generic/method consistency", "checking replacement functions" and?"checking foreign function calls", all of which are accompanied by the following error message: Error in .try_quietly

Dear all, A question on modelling proportional data in R. I have a test experiment that was designed in a particular way, and which I can analyse "by hand" to an extent. I am really struggling to get R to give me sensible results in modelling it "properly", so must be doing something wrong here. As background, I conduct a series of experiments and count the

Suppose you have 4 related vectors: a.id<-c(1:25, 1:25, 1:25) a.vals <- c(101:175) # same length as a.id (the values for those IDs) a.id.levels <- c(1:25) a.id.ratings <- rep(letters[1:5], times=5) # same length as a.id.levels What I would like to do is specify a rating from a.ratings (e.g. "e"), get the vector of corresponding IDs from a.id.levels (via

Based on the examples I've seen in using statistical analysis packages such as lmer, it seems that people usually tabulate all the input data into one file with the first line indicating the variable names (or labels), and then read the file inside R. However, in my case I can't do that because of the huge amount of imaging data. Suppose I have a one-way within-subject ANCOVA with

Hi, As a follow up to Patch D23646 <https://reviews.llvm.org/D23646>, I'm trying to figure out if there should be an alignment check and what the correct approach is. Some background: For stores, the pass turns: %i.vec = shuffle <8 x i32> %v0, <8 x i32> %v1, <0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11> store <12 x i32> %i.vec, <12 x i32>* %ptr

Hello! Can anyone give a tip how to plot parametric effects in an Generalized Additive Model, from mgcv package? Thanks, PM

Hi, I have found quite a few posts on normality checking of response variables, but I am still in doubt about that. As it is easy to understand I'm not a statistician so be patient please. I want to estimate the possible effects of some predictors on my response variable that is nº of males and nº of females (cbind(males,females)), so, it would be:

Hi Renato, Thank you for the answers! First, let me clarify a couple of things and give some context. The patch it looking at VSTn, rather than VLDn (stores seem to be somewhat harder to get the "right" patterns, the pass is doing a good job for loads already) The examples you gave come mostly from loop vectorization, which, as I understand it, was the reason for adding the

Dear R users, I would like to aggregate a data frame using several functions at once (e.g., mean plus standard error). How can I make this work using aggregate()? The help file says scalar functions are needed; can anyone help? Below is the code for my "meanse" function which I??d like to use like this: aggregate(dataframe, list(factorA,factoB),meanse) Thanks for your help!

I'm sorry for what I'm sure is a terribly simple question. I have a large dataframe along these lines: S<- 1:3 d<- data.frame(cbind(S=rep(paste('S',S,sep=""),each=30), trial=rep(1:3,each=10), FactorA=rep(paste('L',1,sep=""),each=30), Acc= c(rep(1,each=20),rep(0,each=10)), Sample=rep(1:10,3), DV= sample(runif(10),10))) but where each

Greetings R-help! I'm fairly new to R and am trying to expand my knowledge beyond using R for simple summary statistics and basic tests. To that end I am attempting to write an interactive R-script that will perform a general rational function fit to a given dataset based on the example given at http://www.itl.nist.gov/div898/handbook/pmd/section6/pmd64.htm. The problem that I seem to have