similar to: Force group syntax for multiple groups

Hello All, I have posted to this list before regarding the same issue so I apologize for the multiple e-mails. I am still struggling with this issue so I thought I'd give it another try. This time I have included reproducible code and a subset of the data I am analyzing. I am running an ANOVA with three factors: GROUP (5 levels), FEATURE (2 levels), and PATIENT (2 levels), where

Dear list, I would like to do some calculation using different grouping variables. My 'df' looks like this: # Some data set.seed(345) id <- seq(200,400, by=10) ids <- sample(substr(id,1,1)) group1 <- rep(1:3, each=7) group2 <- rep(1:2, c(10,11)) group3 <- rep(1:4, c(5,5,5,6)) df <- data.frame(id, ids, group1, group2, group3) df <- rbind(df, df, df) df$time <-

Hi, For further calculations I need to extract indivdual Variances of different random effects from a fitted model. I found out how to extract the correlations (VarCorr(m1)@reSumry$group1) but I was not able to find a way to extract the other components individually. To extract the Residuals I tried: (ranef(m1)@ stdErr) which unfortunately did not work. Thank you very much for your help!

Hi, I just get a question (sorry if it is a dumb one) and I "phase" my question in the following R codes: group1<-rnorm(n=50, mean=0, sd=1) group2<-rnorm(n=20, mean=1, sd=1.5) group3<-c(group1,group2) Now, if I am given a dataset from group3, what method (discriminant analysis, clustering, maybe) is the best to cluster them by using R. The known info includes: 2 clusters,

Is there a way of positioning the color key in levelplot when the axes are on a categorical (rather than numerical) scale? I've put some sample code below. I need to add a secondary y axis to the right side of my plot but then the labels interfere with the color key (which is currently on the right side). Is there a way to shift the color key over a bit more to the right? I've tried

Dear all, First, let's create some data to play around: set.seed(1) (df <- data.frame(Group=rep(c("Group1","Group2","Group3"), each=10), Value=c(rexp(10, 1), rexp(10, 4), rexp(10, 10)))[sample(1:30,30),]) ## Now we need the empirical distribution function: edf <- function(x) ecdf(x)(x) # empirical distribution function evaluated at x ##

Dear list, I am confused on how to explain the interaction term in the context of "treatment contrast". for example, I have an data frame as below: sub group val 1 a group1 3.685625 2 a group1 3.407445 3 a group1 4.040920 4 a group1 2.890875 5 b group1 3.853280 6 b group1 4.113585 7 b group1 3.043250 8 b group1 3.800920 9 c group1 5.394305 10 c

Dear friends, My R*C table is as follow: better good bad Goup1 16 71 37 Group2 0 4 61 Group3 1 6 57 Can I test if there are statistical significant between Group1 and Group2, Group2 and Group3, Group1 and Group2, taking into the multiple comparisons? The table can be set up using the following program: a<-matrix(data=c(16,71,37,0,4,61,1,6,57),nrow=3,byrow=TRUE) Thanks

Hello, unfortunately, I don't know a better subject. I would like to be very flexible in how to process my data. Assume the following dataset: par1 <- seq(0,1,length.out = 100) par2 <- seq(1,100) fac1 <- factor(rep(c("group1", "group2"), each = 50)) fac2 <- factor(rep(c("group3", "group4", "group5", "group6"), each =

Hi all, I'd appreciate any pointers or advise regarding the following issue with files written by M$ Office on Samba 3.0.32 on snv_98 (OpenSolaris) on a ZFS filesystem: samba share: [sharename] read only = No browseable = yes writeable = yes directory mask = 0770 create mask = 0770 delete readonly = Yes acl check permissions =

If I have 3 groups,and for each group,I get the ratio(e.g. incidence rate). Now I wanna compare 3 ratio pairwise,and get the corresponding p values,i.e: group1 vs group2 ,p value=? group1 vs group3 ,p value=? group2 vs group3 ,p value=? Which statistical test should be used? Thanks a lot for your help. [[alternative HTML version deleted]]

I maintain a directory structure containing dirs and files that I regularly push to ~50 hosts, which are divided into 3 groups that have slightly different needs (minor mods in a couple of files). So ideally I would have 4 directories: /path/to/sync/common/ <- common files /path/to/sync/group1/ <- group1 specific only /path/to/sync/group2/ <- group2 specific only

Dear List, I am looking to perform exploratory analyses of two (relatively) large datasets of categorical data. The first one is a binary 80x100 matrix, in the form: matrix(sample(c(0,1),25,replace=TRUE), nrow = 5, ncol=5, dimnames = list(c( "group1", "group2","group3", "group4","group5"), c("V.1", "V.2", "V.3",

Hello While searching the archives and googling for : -login script to map drives according to group membership I saw lots of complicated solutions (on-the-fly scripts, group directories) etc. but the following works very well for me: I downloade dthe ifmember.exe from the microsoft website, and stuck it in the netlogon directory (not the scripts directory)

Hi, my dependent variable is a proportion ("prob.bind"), and the independent variables are factors for group membership ("group") and a covariate ("capacity"). I am interested in the effects of group, capacity, and their interaction. Each subject is observed on all (4) levels of capacity (I use capacity as a covariate because the effect of this variable is normatively

Hi all, I'm working on a seemingly trivial problem that I can't figure out how to implement in R. I'd be most grateful for any help here. I want to do the following: first, randomly assign each of n units to one of g groups of size n/g. Then, randomly re-assign each of the n units to a different group (i.e., same as the first step, but the unit can't be assigned to a group to

Hello All, I'm deploying NUT in a data center and I'm curious to know how others have gone about staging the shutdown of various systems. The systems are broken down into groups of importance, group1 being the most important group4 being the least. In the event of an outage take down group4 after 5 minutes, group3 after 10 minutes, group2 after 15, and finally group4 after 20. I've

#I am trying to understand how R fits models for contrasts in a #simple one-way anova. This is an example, I am not stupid enough to want #to simultaneously apply all of these contrasts to real data. With a few #exceptions, the tests that I would compute by hand (or by other software) #will give the same t or F statistics. It is the contrast estimates that R produces #that I can't seem to

Dear R users, I have a data frame in the form below, on which I would like to make normality tests on the values in the ExpressionLevel column. > head(df) ID Plant Tissue Gene ExpressionLevel 1 1 p1 t1 g1 366.53 2 2 p1 t1 g2 0.57 3 3 p1 t1 g3 11.81 4 4 p1 t2 g1 498.43 5 5 p1 t2 g2 2.14 6 6 p1 t2 g3 7.85 I

Hello, I have a question about mapping users home directory's to samba. The homedir layout is based on an old one used on NIS+ system. The structure is something like: /users /group1 /user1 /user2 ... /group2 /user3 ...