similar to: R equivalent to `estimate' in SAS proc mixed

Hi al, I have a dataset (see attached), which basically involves 4 treatments for a chemotherapy drug. Samples were taken from 2 biopsy locations, and biopsy were taken at 2 time points. So each subject has 4 data points (from 2 biopsy locations and 2 time points). The objective is to study treatment difference.? I used lme to fit a mixed model that uses "biopsy.site nested within pid"

Hello, I'm trying to install R on a linux machine running Red Hat 8. I ran ./configure make and get the following error. I've installed several versions of R (2.2.1 most recently) on this machine and haven't had any problems until now. I wondered if the outdated compiler (gcc version 3.2) was the problem and attempted to install my own, more recent version. I tried gcc versions 4.1.0

Hello Everyone,   Below is some SAS code that uses a "line hold specifier" to read multiple observations from each of several input lines of data. There are 3 patients per line in the in-stream data.   Is there a way in R to read this kind of data? I've looked in my books and online but haven't found anything   Thanks,   Paul     DATA EXAMP.TRIAL; INPUT TRT $ CENTER PAT SEX $ AGE

Dear R-users, I am looking for a R function that would be the equivalent of the SAS proc freq ( http://support.sas.com/documentation/cdl/en/procstat/63104/HTML/default/viewer.htm#/documentation/cdl/en/procstat/63104/HTML/default/procstat_freq_sect006.htm). The table, ftable, xtabs functions are close but do not quite offer the same capabilities (e.g. they just return counts and no %ages as far as

Hi- Since the tru64 patch was designed for -current, I thought I would try to build it with a recent snapshot before backporting to 3.4p1. So I downloaded openssh-SNAP-20020826.tar.gz frpm the portable snapshots, but it does not contain the 'configure' script. I tried copying the 'configure' from 3.4p1, but that does not create a Makefile from the Makefile.in. Where are the

I have no problem setting up my mixed model, or performing anova or lsmeans on my model?s outputs. However, performing lsd mean separation is giving me fits. So I do not have a problem when using two-way anova model. When using the code: fit.yield.add <- lm(data = ryzup, Yield ~ Rep + Nitrogen + Treatment) LSD.test(fit.yield.add, trt = "Nitrogen", alpha = 0.1, console = TRUE)

Dear WizaRds, After consulting different sources I am still unable to understand the correct use of return() in nested functions. To illustrate the problem: f <- function(x,y,type){ est1<-function(x,y){ z=x+y out(x,y,z)} est2<-function(x,y){ z=x*y out(x,y,z)} out<-function(x,y,z) return(x,y,z) if (type=="est1") est1(x,y) if (type=="est2") est2(x,y) }

Hi- Under privsep, I experimented with moving the session_setup_sia() out of do_child() and into do_setusercontext(), which is where the uids/gids are set to the final execution user. The call is made with a NULL tty, and this is functional provided that any later pty allocation uses grantpty() to set the device permissions. Logging in with this method shows that a utmp entry does get made for

Hi, I'm a newbie.Sorry for my question. I have RH9 & downloaded rpm of dovecot for RH9. I installed it: rpm -ivh nameOfthatrpm & when i ran #service dovecot start ,I recieved following lines: Starting IMAP daemon (dovecot): Fatal: Can't use SSL certificate /usr/share/ssl/certs/dovecot.pem: No such file or directory

Dear R-mates, # Here's what I am trying to do. I have a dataset like this: id = c(rep(1,8), rep(2,8)) dur1 <- c( 17,18,19,18,24,19,24,24 ) est1 <- c( rep(1,5), rep(2,3) ) dur2 <- c(1,1,3,4,8,12,13,14) est2 <- rep(1,8) mydata = data.frame(id, estat=c(est1, est2), durat=c(dur1, dur2)) # I want to one have this: id = c(rep(1,8), rep(2,8))

Hi , I recently installed Fedora Core 1. Installed postfix on it and that is running fine. I am trying to get dovecot to run and I have changed only the following line from the default dovecot.conf : imap_listen = 127.0.0.1 since I'd like to install squirrelmail later on. Now when I try to start dovecot I get "Starting Dovecot Imap: Fatal: Can't resolve address

Dear R users, I'm hoping that more experienced users will be able to assist me in examining the model fit of a mixed generalised linear model. The example using the data 'bacteria' within the MASS package will hopefully illustrate what I would like to acheive; library(MASS) library(nlme) attach(bacteria) # y being output and the trt - treatment group being an explanatory variable.

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,

My question concerns the logic behind hypothesis tests for fixed-effect terms in models fitted with lme. Suppose the levels of Subj indicate a grouping structure (k subjects) and Trt is a two-level factor (two treatments) for which there are several (n) responses y from each treatment and subject combination. If one suspects a subject by treatment interaction, either of the following models seem

I have three columns of data, Xc, Trt and fish. This was a repeated measures design with 6 measurements taken from each of 5 fish. Xc is the actual measurement, Trt is the treatment, and fish is the fish number. Data can be seen below (hopefully it is in the column format). I would like to look for differences between treatments in a repeated measures format. I used the following code

What do we loose by not having post-auth privsep? What code is executed between authorization and actual setting of the effective uid? On Tue, 3 Sep 2002, Chris Adams wrote: > Once upon a time, Toni L. Harbaugh-Blackford <harbaugh at nciaxp.ncifcrf.gov> said: > > It appears that the integration of the sia session setup will either > > have to be rethought or abandoned

Rusers: I am trying to apply a quadratic discriminant function to find the best classification outcomes. 1 is assigned to the values greater than a threshold value; and 0 otherwise. I would like to see how the apparent error rates and the optimal error rate change with increasing threshold values. I have a 1000*10 data matrix: n=1000 and p=10. Here is what I wrote so far, but seems to be

Hello there, We want to move from UW-IMAP to Dovecot (mbox format), works like a charm, but UW-IMAP has that bloody habbit to make a stupid message in all his mailbox files : From MAILER-DAEMON Thu Apr 19 10:30:01 2001 Date: 19 Apr 2001 10:30:01 +0200 From: Mail System Internal Data <MAILER-DAEMON at mailhub2-vil.isdnet.net> Subject: DON'T DELETE THIS MESSAGE -- FOLDER INTERNAL DATA

To anyone that may be able to help, First I applogize if this message ends up in your inbox twice. The first one seemed to get stuck in a pending status so I deleted and started over. I am relatively new to R, however I know the basics through some classes I have taken. Unfortunately the classes left off the more complicated analysis that I need to learn and use. I am attempting to replicate

I am trying to use lme to fit a mixed effects model to get the same results as when using the following SAS code: proc mixed; class refseqid probeid probeno end; model expression=end logpgc / ddfm=satterth; random probeno probeid / subject=refseqid type=cs; lsmeans end / diff cl; run; There are 3 genes (refseqid) which is the large grouping factor, with 2 probeids nested within each refseqid,