similar to: Interaction term in lmer

i did some searching through the archives and didn''t really find an answer to my question, so i''m going to just ask it. i have a situation where there are 3 sortable lists. list1, list2, and list3 i need list2 to accept divs from all 3 lists, but list1 and list3 to only accept divs from the list1 and list3 i''ve added two classes to the divs in my sortables: .rail

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"

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

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

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

Hello, I have a problem with creating an identity matrix for glmnet by using the contrasts function. I have a factor with 4 levels. When I create dummy variables I think there should be n-1 variables (in this case 3) - so that the contrasts would be against the baseline level. This is also what is written in the help file for 'contrasts'. The problem is that the function

#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

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

Hey there I am estimating a multilevel model using lmer. I have 5 imputed datasets so I am using mitools to pool the estimates from the 5 > > datasets. Everything seems to work until I try to use > MIcombine to produced pooled estimates. Does anyone have any suggestions? The betas and the standard errors were extracted with no problem so everything seems to work smoothly up until

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

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

Dear All I am trying to do a repeated measures analysis using lmer and have a number of issues. I have non-orthogonal, unbalanced data. Count data was obtained over 10 months for three treatments, which were arranged into 6 blocks. Treatment is not nested in Block but crossed, as I originally designed an orthogonal, balanced experiment but subsequently lost a treatment from 2 blocks. My

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, 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

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,]

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:

It was recently pointed out on the lists that the S-PLUS Trellis suite has a function called reorder.factor that's useful in getting useful ordering of factors for graphs. I happily went ahead and implemented it, but it turns out that R (not S-PLUS) has a generic called reorder (with a method for "dendrogram"). Naturally, this causes R to think I'm defining a method for

R-helpers: I am using R 2.5 on Windows XP, packages all up to date. I have run into an issue with the MIcombine function of the mitools package that I hoped some of you might be able to help with. I will work through a reproducible example to demonstrate the issue. First, make a dataset from the pbc dataset in the survival package --------------- # Make a dataset library(survival) d <-

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

Dear R developers and users: I have the following data, x is the response vaiable, nsample(individual) nested within trt, and subsample nested within nsample, I want to fit trt as fixed effect, and block, nsample(trt) as random effects using lme, is the following coding correct? dat$vgrp <- getGroups(dat, form = ~ 1|trt/nsample, level = 2) ge.lme1 <- lme(fixed=x~trt, data=dat,