similar to: Computing inverse cdf (quantile function) from a KDE

Hello, I'm having some issues getting a ggplot figure to show up in the knitr output, when placed in a loop. Specifically, I have a loop inside a knitr chunk : ```{r fitting, warning=FALSE, fig.width=10, fig.height=10, fig.keep='high'} for (t in 1:T) { # do a regression of tgt.vals ~ predictors and compute coeffs and fitted values (fit.vals / fit.adj.vals) plot(

Hello, I wanted to know if there was way to convert a histogram of a data-set to a kernel density estimate directly in R ? Specifically, I have a histogram [bins, counts] of samples {X1 ... XN} of a quantized variable X where there is one bin for each level of X, and I'ld like to directly get a kde estimate of the pdf of X from the histogram. Therefore, there is no additional quantization of

Is there a function in R for calculating empirical cumulative distribution functions, i.e. the inverse of the quantile function? Perhaps in some library? I''d hate to have to re-invent the wheel. David Edwards, Biostatistics, Novo Nordisk A/S, Bagsværd, Denmark. DEd@novonordisk.com <mailto:DEd@novonordisk.com> Tlf: +45 44 42 62 35. Fax: +45 44 42 14 80 [[alternate HTML version

Hi all, is there any package in R capable of smooth approximation of CDF basing on given sample? (Thus, I am not speaking about ecdf) In particular, I expect very much that the approximation should subject to the property: f(x0)<=f(x1) for x0<x1, where x0 and x1 belong to range of the sample given. Polynomial approximation could be OK for me as well. P.S.

Hi, I am trying to use R to mimic what I did in SAS. proc transreg data=x ; model identity(GSI)=monotone(group1); output out=d2 pprefix=M; run; Accroding to SAS documentation, the MONOTONE transfomation algorithm comes from (Kruskal 1964, secondary approach to ties). I have tried ace. it does provide some kind of monotone transformation, but it is not what I expected. Here is how

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

hi, this is more a statistical question than a R question. but I do want to know how to implement this in R. I have 10,000 data points. Is there any way to generate a empirical probablity distribution from it (the problem is that I do not know what exactly this distribution follows, normal, beta?). My ultimate goal is to generate addition 20,000 data point from this empirical distribution created

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

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

I need to calculate the probability that a sample quantile will exceed a threshold given the size of the iid sample and the parameters describing the distribution of each observation (normal, in my case). I can compute the probability with brute force simulation: simulate a size N sample, apply R's quantile() function on it, compare it to the threshold, replicate this MANY times, and count the

> > 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){ > >

(oops accidentally sent to -owners) Hi, i'm trying to start a LXC guest on a F14 computer .. followed the examples in http://libvirt.org/drvlxc.html, but got stuck when starting it .. 03:07:23.706: debug : virCgroupNew:542 : New group / 03:07:23.707: debug : lxcControllerRun:563 : Setting up private /dev/pts 03:07:23.711: debug : lxcControllerRun:589 : Mouting 'devpts' on

Hi, I am getting some weird results here and I think I am missing something. I am trying to program a function that for a set of random variables drawn from uniform distributions plots that distribution of the second order statistic of the ordered variables. (ie I have n uniform distributions on [0, w_i] for w_i different w_j and i=1..n. I want to plot the distribution of the second order

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

Given f, a pdf over a finite interval, is there any existing R function that can efficiently tabulate the cumulative distribution function for f, or produce all N+1 quantiles of the form i/N? "Efficiently" here means better than doing repeated integrations for each point.

> 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'

I'm trying to provide different parameters to the integrate function for various probability functions. I'm using dnorm as the simplest example here. For instance integrate(dnorm, -1.96, 1.96) produces the correct answer for a normal distribution with mean 0 and standard deviation 1. I've tried two ways to use mean=2.0 and standard deviation 1, but with no luck. The examples follow.

Hi, I'm trying to compute  the spatiotemporal correlation matrix by using Delta Kronecker products of spatial and temporal correlation matrix  in R, but didn't find any delta Kronecker's operator in R. The operators in matrix such as multiplication, addition, eigen values/vector and etc is easily to find and used. Could someone help me, please? Cheers. Firdaus

Hi Charles, On Wednesday, July 13, 2016 11:40:09 PM Charles Lepple wrote: > On Jul 12, 2016, at 5:27 PM, Ivan Adzhubey <iadzhubey at rics.bwh.harvard.edu> > wrote: > > I have recently installed NUT 2.7.3.1 on a freshly installed Ubuntu 16.04 > > LTS (64-bit) system, connected via USB cable to APC Smart-UPS 1500 (SMT > > model). I am trying to make this configuration

this is not a problem with R but a request for related advice. i am trying to run a lengthy batch job from my home. the OS is ... Linux jedi.bwh.harvard.edu 2.4.22-openmosix1smp #1 SMP Fri Sep 5 01:05:37 CEST 2003 i686 athlon i386 GNU/Linux i start the job and put it in the background. while i am connected, all is well. eventually my ISP shuts down the connection if i do not do any input.