search for: r_i

...rief, 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 doctors. Does it suffice to use the "nid" option in summary.rq? More specifically, if the model generating the number of patients, say, r_i, of doctor i is r_i = const + u_i, then I think I would obtain the median of the number of doctors' patients using rq(r~1, ...) and plugging this into summary.rq() using the option se="iid". Unfortunately, I don't observe r_i in the data but, instead, in the data I only have a...

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,

Attached is a small patch to allow users of the PBQP allocator to optionally insert a custom pass. I believe it can be usefull to other users of the pbqp. I used 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

...ew more proposals in the past. R <= 2.1.x has been plotting the Cook's distances vs. observation number, whereas quite a few people in the past have noted that all influence measures being more or less complicated functions of residuals and "hat values" aka "leverages", (R_i, h_{ii}), it would really make sense and fit more to the other plots to plot residuals vs. Leverages --- with the additional idea of adding *contours* of (equal) Cook's distances to that plot, in case one would really want to seem them. In the mean time, this has been *active* in R-devel for q...

...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]]] : no such index at level 2 How can I express: "select only those rows 'r_i' from crossRsorted where mean(q[[r_i[1]]]) > 0? Thanks, - Godmar

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, (2) i.e. has the weight in there. Apart from the division t...

...for a coxph object." This suggests that its usefulness is confined to the sort of data which arise in survival/proportional hazard applications. My question is: is 'clogit' capable of a general conditional logistic analysis? E.g. given a set of data on binomial experiments with Y=1 r_i times out of n_i, associated with levels A_i and B_i of factors A and B at N_A and N_B levels, would clogit(Y ~ A+B, method=c(Exact")) generate something sensible containing the results of a standard exact conditional logistic regression of Y on A and B? With thanks, Ted. --------------...

...,30,85); # range is (30,50) data_for_s = sort(1/(273+data_for_c)); data_for_time = sort(runif(1000,0,100000)) # range is (0,100000) data_for_R = exp((-exp(mle_beta0+mle_beta1*data_for_s))*data_for_time) data_all = cbind(data_for_s,data_for_time,data_for_R) # function: plot_R_i_3d = f(data_for_time,data_for_s) plot_R_i_3d = function(data_for_time,data_for_s) { R_i = exp((-exp(mle_beta0+mle_beta1*data_for_s))*data_for_time); return(R_i) } # tried 1) persp or 2) wireframe persp(data_for_time,data_for_s,plot_R_i_3d) ==> Error in min(x, na.rm = na.rm) : invali...

Hi Lang, > Hmm. Let me make sure I'm reading this right. The constraints are that: > a) All four operands have distinct registers. > b) The first two are in a consecutive pair (second > first) > c) The second two are in a consecutive pair (fourth > third) Constraints b & c are OK, but a is too strict : "mpra %R0, %R1, %R0, %R1" is OK. But I though, may be

...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 "competing risks". Observations are obtained from n independent individuals in the form (t_i,r_i;s_i) where t_i is the time of the event (failure), r_i is the response type (failure type), and s_i is the stimulus type (explanatory variable). I am wondering if it is possible to use survfit5 to fit parametric and nonparametric models to data like these, and if so how to do it. I read the docume...

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  [2,]  0.298  0.615  [3,]  0.194  0.187  [4,]  0.357  0.297  [5,]  0...

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 figure it out from the help files. thanks, rafael

...orm 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 set of values {N_i, R_i}, i=1,2,.....etc... can I use minpack as for the case of function of a single variable? Should I instead resort to another package? Any suggestion is welcome. Many thanks Lorenzo

...se.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 below]. They also state that all the components of that statistic (v_i, F_i, r_i, N_i) 'are computed with Kalman Filter Smoother applied to the null model'. Also, as I understand, that part has been implemented in one of the S packages , SsfPack, as the book on that states on p 531 'the standardized smoothed disturbances may be interpreted as t-statistics for impul...

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

...d (roughly) as: (r0, r1, ... rn) = gc.statepoint(p0, p1, ... pn) is equivalent to 1. for each object o in the heap: o_new = malloc(size of o) *o_new = *o free(o) rewrite all refs to o to point to o_new 2. for each object p_i in (p0, p1, ... pn): set r_i to the the location p_i was relocated to >> ### Changes to the optimizer >> >> We change the optimizer to >> >> - Not speculate loads of GCREF type >> - Not change uses of GCREF types to uses of non-GCREF types >> >> Locally we already have c...

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