similar to: creating subsets and calculating weights

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 ##

All, I am building a glusterfs environment for file storage and need to use ACL's. The CentOS system is joined to AD. We have ingested data into the Gluster environment at /toplevel. OS: CentOS 7.3 Glusterfs: 3.10.5 Samba: 4.4.4 smb.conf: [global] workgroup = GROUP security = ADS realm = GROUP.DOMAIN.COM template homedir = /home/%U template shell

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

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

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, a while ago I posted a question regarding the use of alternative models, including a quasibinomial mixed-effects model (see Results 1). I rerun the exact same model yesterday using R 2.7.2 and lme4_0.999375-26 (see Results 2) and today using R 2.7.2 and lme4_0.999375-27 (see Results 3). While the coefficient estimates are basically the same in all three regressions, the estimated standard

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 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

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 =

Dear All, I have two factors: GROUP and PATIENT, where PATIENT is nested within GROUP. >levels(example$GROUP) [1] "0" "1" "2" "3" "4" > levels(example$PATIENT) [1] "1" "2" "3" There are three observations at each combination of these factors. However, there are no observations for PATIENT = 3 and GROUP

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)

Yes: # getent group GROUP group:x:17573: # getent group group2 group2:x:11010: # getent group GROUP3 group3:x:21178: # wbinfo --group-info GROUP group:x:17573: # wbinfo -n GROUP S-1-5-21-948789634-15155995-928725530-7573 SID_DOM_GROUP (2)

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,

Hi list, Could you explain the error I see here? Thanks! ## I'm using R 2.8.0 on WinXP, Hmisc_3.4-3 > table1 <- matrix(10, 180,7) > cell.format <- matrix("", ncol=7, nrow=180) > cell.format[c(seq(3,180,6),seq(4,180,6)),] <- "color{red}" > cell.format[c(seq(5,180,6),seq(6,180,6)),] <- "color{green}" > > latex(table1,

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]]

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",

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

#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

On 03/31/2016 07:40 AM, tomr wrote: > 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