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I am analyzing from a very simple experiment. I have measured plants of two different colours (yellow and purple) in 9 different populations. So, I have two different factors : a fixed effect (Colour with two levels) and a random one (Population with 9 levels). I first analyzed the data with the aov function LargS is the variable aov(formula = LargS ~ Col + Error(Col/Pop)) Terms:

Is there a way to set up a regression in R that forces two coefficients to be equal but opposite in sign? I'm trying to setup a model where a subject appears in a pair of environments where a measurement X is made. There are a total of 5 environments, one of which is a baseline. But each observation is for a subject in only two of them, and not all subjects will appear in each

Is there some clever way of pasting results from R into Excel or Word, as tab limited format so they are easy to turn into a formatted table. Or is there some other way of doing this to avoid the time spent reformatting the output for presentation. If different, I am also interested in an answer to the same question but using S-Plus. Many thanks, Graham

Hi r-users,   I have 2 sets of data and I would like to superimpose this cumulative density in one graph.   I know how to put the 2 graphs in one same graph but my problem is the data are different.   > z[1:20]  [1]  2.02347388  3.19514379  0.05188875  1.41333812  3.50249892  4.34272676  6.65639581  5.01533819  4.18207505 [10]  2.87615551  2.28202152  0.49431541  0.06570579  5.68352438

A big compile time regression. Any ideas? Ciao, Duncan. On 22/07/11 19:13, llvm-testresults at cs.uiuc.edu wrote: > > bwilson__llvm-gcc_PROD__i386 nightly tester results > > URL http://llvm.org/perf/db_default/simple/nts/253/ > Nickname bwilson__llvm-gcc_PROD__i386:4 > Name curlew.apple.com > > Run ID Order Start Time End Time > Current 253 0 2011-07-22 16:22:04

On Jul 24, 2011, at 3:02 AM, Duncan Sands wrote: > A big compile time regression. Any ideas? > > Ciao, Duncan. False alarm. For some reason that I have not yet been able to figure out, these tests run significantly more slowly when I run them during the daytime, which I did for that run. I checked a few of the worst regressions reported here and they all recovered in subsequent

Thank you, Greg, and also to Scott Ellison, who replied privately. I am in the process of trying out both suggestions. After I sent my initial message, I came across the Systemfit package, which allows specification of constraints on parameters. In theory, this should solve my problem perfectly. However, I was not able to get it to work with my data, as every attempt yielded the following

I want to take the first row of each unique ID value from a data frame. For instance > ddTable <- data.frame(Id=c(1,1,2,2),name=c("Paul","Joe","Bob","Larry")) I want a dataset that is Id Name 1 Paul 2 Bob > unique(ddTable) Will give me all 4 rows, and > unique(ddTable$Id) Will give me c(1,2), but not accompanied by the name column.