similar to: statistical contrasts on 3-way interaction

#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, 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,]

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

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

There has been some recent discussion on this list about the value of using intervals with lme() to check for whether a model is ill-defined. My question is, what else can drive very large confidence intervals for the variance components (or cause the error message "Error in intervals.lme(Object) : Cannot get confidence intervals on var-cov components: Non-positive definite approximate

I can not understand why this fails > > faicoutput2 <- list(stuff21 = as.numeric(faicout$coefficients[2]), + stuff31=as.numeric(faicout$coefficients[3]), + stuff41=as.numeric(faicout$coefficients[4]), + stuff32=(stuff21-stuff31), + stuff42=(stuff21-stuff41), +

Thanks for the help so far, I have already solved the main problem, but sadly it seems I can't find out the last step. I hope you can help me there... Here ist the important part: It is trying to load some nvidia module, although i have already done apt-get purge nvidia* [ 4.636] (II) LoadModule: "glx" [ 4.638] (II) Loading /usr/lib/xorg/modules/extensions/libglx.so [

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

Thanks for the help so far, I have already solved the main problem, but sadly it seems I can't find out the last step. I hope you can help me there... Here ist the important part: It is trying to load some nvidia module, although i have already done apt-get purge nvidia* [ 4.636] (II) LoadModule: "glx" [ 4.638] (II) Loading /usr/lib/xorg/modules/extensions/libglx.so [

Hello, A nice guy call Jun Shen was helping me out with this, but I require a bit more help. Below is my data set or list called 'test'. I'm trying to calculate the %RSD for each pair of index and keep it in cronological order if you can imagine a 3rd column with 'date' beside index. Result Index 1 0.2901 17 2 0.2928 17 3 0.2913 18 4 0.2893 18 5

Hello, R experts, If I understand Ted's anwser correctly, then I can not contrast the mean yields between sections 1-8 and 9-11 under "Trt" but I can contrast mean yields for sections 1-3 and 6-11 because there exists significant interaction between two factors (Trt:section4, Trt:section5). Could I use the commands below to test the difference between sections 1-3 and 6-11 ?

Dear Thomas Lumley, and R-help list members, I have read your article "Network meta-analysis for indirect treatment comparisons" (Statist Med, 2002) with great interest. I found it very helpful that you included the R code to replicate your analysis; however, I have had a problem replicating your example and wondered if you are able to give me a hint. When I use the code from the

Hello, R users, I have two factors (treat, section) anova design experiment where there are 3 replicates. The objective of the experiment is to test if there is significant difference of yield between top (section 9 to 11) and bottom (section 9 to 11) of the fruit tree under treatment. I found that there are interaction between two factors. I wonder if I can contrast means from levels of

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

Hi, I have an experiment with 2 independant factors which I have been trying to analyse in R. The problem is that there are several data points recorded on the same animal. However, no combination of treatments is repeated on the same animal. All possible combinations of treatments are done in a random order with as many points as possible being done on 1 animal before moving onto the next. The

Based on what follows, the most favourable construction is that the documentation, by failing to say that the function should be used only for completely balanced designs, is deficient. Even for such designs, there is a bug that needs correction. The discussion is lengthy because I think it important to document, at least wrt effects and means, exactly what model.tables() does. My preference is

Example: I have the following model > model <- lmer(response ~ time * trt * bio + (time|id), data = dat) where time = time of observation trt = treatment group (0-no treatment / 1-treated) bio = biological factor (0-absent / 1-present) and I would like to obtain an estimate (with standard error) of the change in response over time for individuals in the

Hello listmembers, I've been thinking of using gls in the nlme package to test for serial correlation in my data set. I've simulated a sample data set and have found a large discrepancy in the results I get when using the default method REML vs. ML. The data set involves a response that is measured twice a day (once for each level of a treatment factor). In my simulated data set, I