similar to: 2 way ANOVA with possible pseudoreplication

Kia ora list members: I'm having a little difficulty getting the correct standard errors from a glm.object (R 1.9.0 under Windows XP 5.1). predict() will gives standard errors of the predicted values, but I am wanting the standard errors of the mean. To clarify: Assume I have a 4x3x2 factorial with 2 complete replications (i.e. 48 observations, I've appended a dummy set of data at the

Hi, I have some data on the effect of cycle shape (categorical) and frequency (continuous) on the efficiency of muscle contraction. My minimum adequate model is: m15<-glm(efficiency~cycle.shape*freq, family=quasipoisson) However, I wish to know where significant differences lie between specific combinations of treatments. I guess I want an equivalent of a post hoc test following an

If possible I would like to combine two different character arrays in combinations Array1 <- c("height","weight","age","sex") Array2 <- c("trt0","trt1","trt2") I would like to combine these two character vectors to end up with such ... Array3 "height.trt0.trt1" "height.trt0.trt2"

Dear R users; Ive got two questions concerning nlme library 3.1-65 (running on R 2.2.0 / Win XP Pro). The first one is related to augPred function. Ive been working with a nonlinear mixed model with no problems so far. However, when the parameters of the model are specified in terms of some other covariates, say treatment (i.e. phi1~trt1+trt2, etc) the augPred function give me the following

Hi, As part of my dissertation, I'm going to be doing an Anova, comparing the "dead zone" diameters on plates of microbial growth with little paper disks "loaded" with antimicrobial, a clear zone appears where death occurs, the size depending on the strength and succeptibility. So it's basically 4 different treatments, and I'm comparing the diameters (in mm) of

On 25/02/2011 21:22, Ben Ward wrote: > > -------- Original Message -------- > Subject: Re: [R] ANOVA and Pseudoreplication in R > Date: Fri, 25 Feb 2011 12:10:14 -0800 > From: Bert Gunter<gunter.berton at gene.com> > To: Ben Ward<benjamin.ward at bathspa.org> > CC: r-help<r-help at r-project.org> > > > > I can hopefully save bandwidth here by

Hi all, I'm trying to use a gam (mgcv package) to analyse some data with a roughly U shaped curve. My model is very simple with just one explanatory variable: m1<-gam(CoT~s(incline)) However I just keep getting the error message "Error in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots) : A term has fewer unique covariate combinations than specified maximum degrees of

Hello. A quick question about incorporating variation due to study in the metafor package. I'm working with a particular data set for meta-analysis where some studies have multiple measurements. Others do not. So, let's say the effect I'm looking at is response to two different kinds of drug treatment - let's call their effect sizes T1 and T2. Some studies have multiple

#Dear All, #I'm having a bit of a trouble here, please help me... #I have this data set.seed(4) mydata <- data.frame(var = rnorm(100), temp = rnorm(100), subj = as.factor(rep(c(1:10),5)), trt = rep(c("A","B"), 50)) #and this model that fits them lm <- lm(var ~ temp * subj, data = mydata) #i want to

I have been trying for hours now to perform an orthogonal contrast through an ANOVA in R. I have done a two-factor factorial experiment, each factor having three levels. I converted this dataset to a dataframe with one factor with nine treatments, as I couldn't work out what else to do. I have set up a matrix with the eight orthogonal contrasts that I wish to perform, but despite

Hello, Everyone, I am sorry that my message got truncated. I resend it again as below: Hello, R Users, I have a list (say listexp) of 10,000 elements, each of which consists of a matrix (5X6). It likes: $"a" trt1rep1 trt1rep2 trt2rep1 trt2rep2 ctlrep1 ctlrep2 [1,] 50 54 98 89 40 45 [2,] 60 65 76 79

Dear all, How can I run a constrained correspondence analysis with the following data: 15 animals were measured repeatedly month-wise (over to 2 years) according to ther diet composition (8 food categories). our data.frame looks like this: food 1 2 ... 8 sex season year animal freq 12 8 ... 1 0 summer 2011 1 freq 0 7 ... 0 1 winter 2011 1 ... freq 0 7 ... 0 1 spring 2011 15 We

Hello, I am trying to help someone who has carried out an experiment and I'm finding it quite difficult to understand the appropriate model to use & code it. The response is a measurement - the amount of DNA extracted during the experiment. There were 2 factors to be tested - one is the condition under which the experiment took place and the other is the type of DNA to be

Sorry if this is the wrong ml for this question, I am new to R. I am trying to use R to analyze the data from my thesis experiment and I am having troubles accounting for the pseudoreplication properly from having each participant repeat each treatment combination (combination of fixed factors) 5 times. The design of the experiment is as follows... Responses: CompletionTIme VisitedTargets

Hi all, I'm new to R and have the following problem: I have a 2 factor design (a has 2 levels, b has 3 levels). I have an object kidney.aov which is an aov(y ~ a*b), and when I ask for model.tables(kidney.avo, se=T) I get the following message along with the table of effects: Design is unbalanced - use se.contrast() for se's but the design is NOT unbalanced... each fator level

Hi All, I'm generating 10 different data sets with 1 and 0 in a matrix form and writing the output in separate files. Now I need to stack all these data sets in one vector and I know that stack only operates on list or data frame however I got these data sets by converting list to a matrix so can't go backwards now. Is there a way i can still use Stack? Please see the program:

Hello, R experts, I have a list called dataHP which has 30 elements (m1, m2, ..., m30). Each element is a 7x6 matrix holding yield data from two factors experimental design, with treatment in column, position in row. For instance, the element 20 is: dataHP[[20]] col1 col2 col3 trt1 trt2 trt3 [1,] 22.0 20.3 29.7 63.3 78.5 76.4 [2,]

I'm trying to understand better the differences between fixed and random effects by running very simple examples in the nlme package. My first attempt was to try doing a t-test in lme. This is very similar to the Rail example that comes with nlme, but it has two groups instead of five. So I try a1 <- 1:10 a2 <- 7:16 t.test(a2,a1) getting t(18)=4.43, p=.0003224. Then I try to do it

Need some help please I am trying to use this model because I have temporal replication in my data results<-read.table(file=file.choose(),header=T) attach(results) names(results) results<-na.omit(results) library(nlme) library(lattice) results<-groupedData(weight~date|group,outer=~diet,results) plot(results) plot(results,outer=T) model<-lme(weight~diet,random=~date|group,results)

Dear R users, I'm pretty new on using lmer package. My response is binary and I have fixed treatment effect (2 treatments) and random center effect (7 centers). I want to test the effect of treatment by fitting 2 models: Model 1: center effect (random) only Model 2: trt (fixed) + center (random) + trt*center interaction. Then, I want to compare these 2 models with Likelihood Ratio Test.