similar to: distribution of second order statistic

Hello, I wanted to know if there is a simple way of getting the inverse cdf for a KDE estimate of a density (using the ks or KernSmooth packages) in R ? The method I'm using now is to perform a numerical integration of the pdf to get the cdf and then doing a search for the desired probablity value, which is highly inefficient and very slow. Thanks, -fj [[alternative HTML version deleted]]

Dear R developers, I am a little disappointed that my bug report only made it to the wishlist, with the argument: Well, it does not say it has. Only relevant to prediction intervals. predict.lm does calculate prediction intervals for linear models from weighted regression, so they should be correct, right? As far as I can see they are bound to be wrong in almost all cases, if no weights

Sorry for starting another topic, this is actually a reply to Segher's post on Sun Oct 22 on the 'mdct question' topic. I wasn't subscribed properly and so I didn't get email confirmation and thus can't add to that thread. So Segher, if the equation is indeed what you say it is, then replacing mdct_backward with this version should work, but it doesn't. Am I applying

hi, i have just begun using R, so please bear with me. i am trying to use cmdscale and display the result. i read the data using read.table(), calculate the proximity matrix using dist() and the display the result using the cmdscale(). this is very fine. in addition, i want the display to distinguish between two classes of records in my data. i have my data records marked as "1" or

Hello, I have a vector of samples x of length N. Associated with each sample x_i is a certain weight w_i. All the weights are in another vector w of the same length N. I have another vector of samples y of length n (small n). All these samples have equal weights 1/n. The ECDF of these samples is defined as for example at http://en.wikipedia.org/wiki/Empirical_distribution_function and I can

Peter, This is a highly structured text. Just for the discussion, I separate the building blocks, where (D) and (E) and (F) are new: BEGIN OF TEXT -------------------- (A) Non-?NULL? ?weights? can be used to indicate that different observations have different variances (with the values in ?weights? being inversely proportional to the variances); (B) or equivalently, when the elements of

Hi, i need a sample-code, for which the llvm alias-analysis finds a *must-aliases*. I have tried codes like followings. In all cases, i see just *may-aliases* when i use "opt -aa-eval -print-all-alias-modref-info foo.bc": Regards Raad 1 ========================================== void foo() { int i = 2; int& r = i; } 2 =========================================== void

Dear R users, I'm trying to fit a simple random intercept model with a fixed intercept. Suppose I want to assign a weight w_i to the i-th contribute to the log-likelihood, i.e. w_i * logLik_i where logLik_i is the log-likelihood for the i-th subject. I want to maximize the likelihood for N subjects Sum_i {w_i * logLik_i} Here is a simple example to reproduce

Hola a todos, Estoy intentando resolver un problema de optimización con R con restricciones lineales, pero no consigo incluir dichas restricciones. Es decir, f<-function(w){ sd(...) # desviación típica de ciertos datos } optim(rep(1/2,8),fn = f,lower=0,upper=1,method='L-BFGS-B') # no se como incluir aquí las restricciones Las restricciones son: la suma de los w_i es 1 y todos los

I have a list of vectors $A "AB" "BC" "CD" $B "GF" "HG" "FH" "FJ" and I want to convert it into a dataframe of form A AB A BC A CD B GF B HG B FH B FJ Just can't quite come up with a nice "R" solution for it. Thanks, Sean

> > On 9 Feb 2017, at 16:00, G?ran Brostr?m <goran.brostrom at umu.se> wrote: > > > > In my package 'glmmML' I'm using old C code and linpack in the optimizing procedure. Specifically, one part of the code looks like this: > > > > F77_CALL(dpoco)(*hessian, &bdim, &bdim, &rcond, work, info); > > if (*info == 0){ > >

> On 10 Feb 2017, at 14:53, G?ran Brostr?m <goran.brostrom at umu.se> wrote: > > Thanks to all who answered my third question. I learned something, but: > > On 2017-02-09 17:44, Martin Maechler wrote: >> >>>> On 9 Feb 2017, at 16:00, G?ran Brostr?m <goran.brostrom at umu.se> wrote: >>>> >>>> In my package 'glmmML'

Hello, I want to implement LLVM backend for a specific VLIW hardware. I am working on defining its instruction set, and assembly language. The hardware has two pipelines, int and float. Each pipeline can do 3 operations/cycle, 3 operations forms an instruction. One of the Integer Instruction looks like this: add Ri, Rj, Rk; add Rl, Rm, Rn; add Ro, Rp, Rq An int instruction and a float

I am more than 'a little disappointed' that you expect a detailed explanation of the problems with your 'bug' report, especially as you did not provide any explanation yourself as to your reasoning (nor did you provide any credentials nor references). Note that 1) Your report did not make clear that this was only relevant to prediction intervals, which are not commonly used.

Hello! I would like to use lmer() to fit data, which are some estimates and their standard errors i.e kind of a "meta" analysis. I wonder if weights argument is the right one to use to include uncertainty (standard errors) of "data" into the model. I would like to use lmer(), since I would like to have a "freedom" in modeling, if this is at all possible. For

> On Feb 13, 2018, at 4:02 PM, array chip via R-help <r-help at r-project.org> wrote: > > Hi all, > > The survdiff() from survival package has an argument "rho" that implements Fleming-Harrington weighted long rank test. > > But according to several sources including "survminer" package

Hi All, I am trying to code an R script which gives me the time varying parameters of the NIG and GH distributions. Further, becasue I think these these time varying parameters should be more responsive to more recent observations, I would like to include a weighted likelihood estimation proceedure where the observations have an exponentially decaying weighting rather than the equal weighting

Dear R gurus, I have a function "goftests" that receives the following arguments: * a vector "x" of data values; * a distribution name "dist"; * the dots list ("...") containing a list a parameters to pass to CDF function; and calls several goodness-of-fit tests on the given data values against the given distribution. That is: ##### BEGIN CODE SNIP #####

> On Feb 14, 2018, at 5:26 PM, David Winsemius <dwinsemius at comcast.net> wrote: > >> >> On Feb 13, 2018, at 4:02 PM, array chip via R-help <r-help at r-project.org> wrote: >> >> Hi all, >> >> The survdiff() from survival package has an argument "rho" that implements Fleming-Harrington weighted long rank test. >>

I tried lrm in library(Design) but there is always some error message. Is this function really doing the weighted logistic regression as maximizing the following likelihood: \sum w_i*(y_i*\beta*x_i-log(1+exp(\beta*x_i))) Does anybody know a better way to fit this kind of model in R? FYI: one example of getting error message is like: > x=runif(10,0,3) > y=c(rep(0,5),rep(1,5)) >