similar to: What is wrong with this contrast matrix?

Dear R users, I am analyzing several response variables (all scaled to [0;1]) using a multivariate linear model. After fitting the model, I set up a hypothesis matrix to test specific contrasts for these response variables; for example: "a always increases significantly more than b when regressed against x". What I am stuck with now is how to calculate the correct F-values (and

Hey, R-listers, I have a question about determining the orthogonal basis vectors. In the d-dimensinonal space, if I already know the first r orthogonal basis vectors, should I be able to determine the remaining d-r orthognal basis vectors automatically? Or the answer is not unique? Thanks for your attention. Fred

On Fri, Feb 26, 2016 at 8:43 PM, Chandler Carruth <chandlerc at google.com> wrote: > On Fri, Feb 26, 2016 at 8:40 PM Xinliang David Li <xinliangli at gmail.com> > wrote: > >> On Fri, Feb 26, 2016 at 8:30 PM, Chandler Carruth <chandlerc at google.com> >> wrote: >> >>> On Fri, Feb 26, 2016 at 8:15 PM Hal Finkel <hfinkel at anl.gov> wrote:

On Fri, Feb 26, 2016 at 8:57 PM, Hal Finkel <hfinkel at anl.gov> wrote: > > > ------------------------------ > > *From: *"Xinliang David Li" <xinliangli at gmail.com> > *To: *"Chandler Carruth" <chandlerc at google.com> > *Cc: *"Hal Finkel" <hfinkel at anl.gov>, "llvm-dev" <llvm-dev at lists.llvm.org>, >

----- Original Message ----- > From: "Xinliang David Li" <xinliangli at gmail.com> > To: "Chandler Carruth" <chandlerc at google.com> > Cc: "Hal Finkel" <hfinkel at anl.gov>, "llvm-dev" > <llvm-dev at lists.llvm.org>, "Philip Reames" > <listmail at philipreames.com>, "Duncan P. N. Exon Smith"

----- Original Message ----- > From: "Xinliang David Li" <xinliangli at gmail.com> > To: "Hal Finkel" <hfinkel at anl.gov> > Cc: "llvm-dev" <llvm-dev at lists.llvm.org>, "Philip Reames" > <listmail at philipreames.com>, "Duncan P. N. Exon Smith" > <dexonsmith at apple.com>, "Sanjoy Das" >

Dear Help-list, How can I do a multiple comparison test (mct) on selected contrasts from a linear model while using packages lme4 and multcomp? I am running R 2.13.0 under Windows 7. The following linear model and mct produces a global mct of 15 paired contrasts of the combined (Site, Position) factor SitePos of which only 9 are of interest. Model.G = lmer(log10(SrCa) ~ SitePos + (1 | Eel),

----- Original Message ----- > From: "Hal Finkel via llvm-dev" <llvm-dev at lists.llvm.org> > To: "Xinliang David Li" <xinliangli at gmail.com> > Cc: "llvm-dev" <llvm-dev at lists.llvm.org> > Sent: Friday, February 26, 2016 11:07:35 PM > Subject: Re: [llvm-dev] Possible soundness issue with > available_externally (split from

On Fri, Feb 26, 2016 at 8:40 PM Xinliang David Li <xinliangli at gmail.com> wrote: > On Fri, Feb 26, 2016 at 8:30 PM, Chandler Carruth <chandlerc at google.com> > wrote: > >> On Fri, Feb 26, 2016 at 8:15 PM Hal Finkel <hfinkel at anl.gov> wrote: >> >>> Can you explain your concerns about reliability? My idea is that we have >>> a kind of

Dear R-experts Execuse me for an easy question, but I need help, sorry for that. >From days I have been working with a large dataset, where operations are needed within a component of dataset. Here is my question: I have big dataset where x1:.....x1000 or so. What I need to do is to work on 4 consequite variables to calculate a statistics and output. So far so good. There are more vector

Dear R users, suppose I have a matrix of observations for which I calculate all pair-wise correlations: m=matrix(sample(1:100,replace=T),10,10) w=cor(m,use="pairwise.complete.obs") How do I extract only those correlations that are >0.6? w[w>0.6] #obviously doesn?t work, and I can?t find a way around it. I would very much appreciate any help! Best wishes Christoph (using R

On Fri, Feb 26, 2016 at 8:30 PM, Chandler Carruth <chandlerc at google.com> wrote: > On Fri, Feb 26, 2016 at 8:15 PM Hal Finkel <hfinkel at anl.gov> wrote: > >> Can you explain your concerns about reliability? My idea is that we have >> a kind of delayed-name comdat section attribute that says that the name of >> the comdat section into which the function will

I wrote something to check for duplicate rows in a data frame, but it is too inefficient. Is there a way to do this without the nested loops? This code correctly indicates rows 1-7, 1-8, 2-9 and 7-8 are duplicates. > m <- matrix(c(1,1,1,1,1, 2,2,2,2,2, 6,6,6,6,6, 3,3,3,3,3, 4,4,4,4,4, 5,5,5,5,5, 1,1,1,1,1, 1,1,1,1,1, 2,2,2,2,2, 7,7,7,7,7), ncol=5, byrow=TRUE) > df <- data.frame(m)

Dear all, I have a data frame that looks like this: c1 c2 c3 A B C B C A A A B and so on; I?d like to produce one single vector consisting of the columns c1,c2, c3, such that vector=("A","B","A","B","C","A","C","A","B") I guess it?s easy to do but I don?t know how...Can anyone

Dear all, How can I replace text in objects that are of class "formula"? y="a * x + b" class(y)="formula" grep("x",y) y[1] Suppose I would like to replace the "x" by "w" in the formula object "y". How can this be done? Somehow, the methods that can be used in character objects do not work 1:1 in formula objects... Many

Dear R users, Let?s say I have 10 models, each named m1,m2,m3..., and I would like to summarize them automatically and simultaneously - e.g., to extract parameter estimates later on from all models; how can I do that? I have tried: x=1:10 #this creates some example data y=rnorm(10) m1=lm(x~y) m2=lm(x~1) sum.lms=function(x)summary(paste("m",x,sep="")) sum.lms(1:2) but

Dear all, For an introductory course on glm?s I would like to create an example to show the difference between glm and transformation of the response. For this, I tried to create a dataset where the variance increases with the mean (as is the case in many ecological datasets): poissondata=data.frame( response=rpois(40,1:40), explanatory=1:40) attach(poissondata) However, I have run into

Dear R users, I??ve got a simple question but somehow I can??t find the solution: I have a data frame with columns 1-5 containing one set of integer values, and columns 6-10 containing another set of integer values. Columns 6-10 contain NA??s at some places. I now want to calculate (1) the number of values in each row of columns 6-10 that were NA??s (2) the sum of all values on columns 1-5

Hi All, is there any general advice about speeding up recursive functions (not mentioning 'don't use them')? Regards, Federico Calboli -- Federico C. F. Calboli Department of Epidemiology and Public Health Imperial College, St. Mary's Campus Norfolk Place, London W2 1PG Tel +44 (0)20 75941602 Fax +44 (0)20 75943193 f.calboli [.a.t] imperial.ac.uk f.calboli [.a.t]

1. If your code actually runs, you should upgrade R, and quit using `_' for assignment... 8-) 2. You seem to have an extraneous `]' after the na.exclude. Could that be the problem? Andy > From: Christoph Scherber > > actually, the situation is much more complicated. I am producing > multiple graphs within a "for" loop. For some strange reason, the >