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Dear all, My question might be more of a statistics question than a question on R, although it's on how to apply the 'quantreg' package. Please accept my apologies if you believe I am strongly misusing this list. To be very brief, the problem is that I have data on only a random draw, not all of doctors' patients. I am interested in the, say, median number of patients of

Hi Arnaud, The patch looks good. I've committed it in r133249. > > > I noticed an unexpected --- to me at least --- behaviour of the allocator. > > I have some instructions using 2 pairs of registers, say “mpra R_x, R_x+1, > R_y, R_y+1”, and setting the pairing constraints R_x -> R_x+1 and R_y -> > R_y+1 could silently produce wrong code like “mpra %R0, %R2, %R1, %R3”. I > add to explicitly add another constraint to describe that y must differ > from x, x-1 and x+1 to make the allocator build valid pairs, i...

...d it to undo some of the coalescer work, and make sure that I have different virtual registers, inserting a copy if necessary, to build a pair. I noticed an unexpected --- to me at least --- behaviour of the allocator. I have some instructions using 2 pairs of registers, say "mpra R_x, R_x+1, R_y, R_y+1", and setting the pairing constraints R_x -> R_x+1 and R_y -> R_y+1 could silently produce wrong code like "mpra %R0, %R2, %R1, %R3". I add to explicitly add another constraint to describe that y must differ from x, x-1 and x+1 to make the allocator build valid pairs, i...

As some of you R-devel readers may know, the plot() method for "lm" objects is based in large parts on contributions by John Maindonald, subsequently "massaged" by me and other R-core members. In the statistics litterature on applied regression, people have had diverse oppinions on what (and how many!) plots should be used for goodness-of-fit / residual diagnostics, and to my

Hi, I am confused about how to select elements from a list. I'm trying to select all rows of a table 'crossRsorted' such that the mean of a related vector is > 0. The related vector is accessible as a list element l[[i]] where i is the row index. I thought this would work: > crossRsorted[mean(q[[ crossRsorted[,1] ]], na.rm = TRUE) > 0, ] Error in q[[crossRsorted[, 1]]] :

I have a puzzle as to how R is computing Cook's distance in weighted linear regression. In this case cook's distance should be given not as in OLS case by h_ii*r_i^2/(1-hii)^2 divided by k*s^2 (1) (where r is plain unadjusted residual, k is number of parameters in model, etc. ) but rather by w_ii*h_ii*r_i^2/(1-hii)^2 divided by k*s^2,

Can someone clarify what I cannot make out from the documentation? The function 'clogit' in the 'survival' package is described as performing a "conditional logistic regression". Its return value is stated to be "an object of class clogit which is a wrapper for a coxph object." This suggests that its usefulness is confined to the sort of data which arise in

Hello Dear, I have a function, like z=f(x,y), and try a surface plot with this function. But, on the reference of "wireframe" requires data option, so I generated x and y, and computed z with them. But, still I have a problem to draw a surface plot. The code and errors are ################################################## mle_beta0=64.43707; mle_beta1=-24365.16; # generating for

...llvmdev at cs.uiuc.edu Subject: Re: [LLVMdev] PBQP & register pairing Hi Arnaud, The patch looks good. I've committed it in r133249. I noticed an unexpected --- to me at least --- behaviour of the allocator. I have some instructions using 2 pairs of registers, say "mpra R_x, R_x+1, R_y, R_y+1", and setting the pairing constraints R_x -> R_x+1 and R_y -> R_y+1 could silently produce wrong code like "mpra %R0, %R2, %R1, %R3". I add to explicitly add another constraint to describe that y must differ from x, x-1 and x+1 to make the allocator build valid pairs, i...

I will have data in the following form: Time resp type stim type 300 a A 200 b A 155 a B 250 b B 80 c A 1000 d B ... c is left censored observation; d is right censored This sort of problem is discussed in Chap 9 of Cox & Oakes Analysis of Survival Data under the name

Hi all, I have this data set: ## Empirical copula ## dt1 = ranking ## dt2 = observed uniform data associated with the ranking   Sample data, > dt1         S_i   R_i  [1,]   7.0  10.0  [2,] 232.5 440.5  [3,] 143.0 141.5  [4,] 272.5 222.0  [5,]  46.0  34.0  [6,] 527.0 483.0  [7,] 420.5 563.5  [8,]  23.5  16.5  [9,]  56.5  68.5 [10,] 341.5 382.5   > dt2       unisk1 unisk2  [1,]  0.008  0.010

Hi Arnaud, That sounds great. I look forward to seeing a patch. You may also look forward to big performance improvements in the PBQP allocator: I'm working on updates which will improve compile speeds and massively reduce memory use. Regards, Lang. On Tue, Jun 7, 2011 at 7:02 PM, Arnaud Allard de Grandmaison < Arnaud.AllardDeGrandMaison at dibcom.com> wrote: > > I also

I also considered this approach, but did not want to dive in the constraint handling for now. The PBQP path seemed easier at first sight --- and was easy to setup. And I always wanted to give a try to the pbqp :) I will add the hook to the pbqp and propose a patch if this looks clean enough. Thanks, -- Arnaud de Grandmaison -----Original Message----- From: Jakob Stoklund Olesen

hi! i want to change the "robustness weights" used by loess. these are described on page 316 of chambers and hastie's "statistical models in S" book as r_i = B(e_i,6m) where B is tukey's biweight function, e_i are the residulas, and m is the median average distance from 0 of the residuals. i want to change 6m to, say, 3m. is there a way to do this? i cant

Dear All, In the past I have often used minpack (http://bit.ly/zXVls3) relying on the Levenberg-Marquardt algorithm to perform non-linear fittings. However, I have always dealt with a function of a single variable. Is there any difference if the function depends on two variables? To fix the ideas, please consider the function f(R,N)=(a/(log(2*N))+b)*R+c*N^d, where a,b,c,d are fit parameters. For

A question about: Kalman in R, time series and deJong-Penzer statistic - how to compute it using available artefacts of KalmanXXXXX? Background. in the paper http://www.lse.ac.uk/collections/statistics/documents/researchreport34.pdf 'Diagnosing Shocks in TIme Series', de Jong and Penzer construct a statistic (tau) which can be used to locate potential shocks. [p15, Theorem 6.1 and

This bug is not quite fixed - the example from my original report now = works using R-2.2.1, but plot(Uniform, 6) does not. The bug is due to if (show[6]) { ymx <- max(cook, na.rm =3D TRUE) * 1.025 g <- hatval/(1 - hatval) # Potential division by zero here # plot(g, cook, xlim =3D c(0, max(g)), ylim =3D c(0, ymx),=20 main =3D main, xlab =3D

Thanks, I think that's a useful way to look at it (though if I wanted to bikeshed I'd suggest the name "DoubleIndirect" as a bit more precise than "VeryIndirect"). An aspect of it that I'm still puzzling over is that my target runtime (at least in its current form) doesn't have a way to represent/process a "VeryIndirect" pointer. So I'd like to

Hi Andy, Andrew Trick wrote: > Sanjoy, > > This looks very close to my understanding of the statepoint design trajectory when you first introduced it. It’s great that you followed through and took the time to formalize the IR semantics. It’s been a couple years since I’ve thought about it so I may ask some obtuse questions. > > I think he subject line is wrong though! Did

This is a proposal to add strong GC reference types to LLVM. We have some local (downstream) patches that are needed to prevent LLVM's optimizer from making transforms that are problematic in the presence of a precise relocating GC. Adding a notion of a strong GC reference to LLVM will let us upstream these patches in a principled manner, and will act as a measure to avoid new problematic