search for: approximation

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...

Hi, I wonder if anyone could advise me with this: I've been trying to make a standard curve in R with lm() of some standards from a spectrophotometer, so as I can express the curve as a formula, and so obtain values from my treated samples by plugging in readings into the formula, instead of trying to judge things by eye, with a curve drawn by hand. It is a curve and so I used the

> Almost any method of fitting a density estimate would work on > integrating (numerically) the result. it is a nice idea concerning the monotony property, which will be obtained automatically, but I am going to use results of approximation analytically > In particular, look at package polspline, where > p(old)logspline does the integration for you. thank you, I am going to install it. > > is there any package in R capable of smooth approximation of CDF > > basing on given sample? (Thus, I am not speaking ab...

..., Ontario, Canada Web: socserv.mcmaster.ca/jfox > -----Original Message----- > From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Evans, > Richard K. (GRC-H000) > Sent: Monday, September 25, 2017 3:28 PM > To: r-help at r-project.org > Subject: [R] bowed linear approximations > > Hello, > > Please run the following code snippet and note the resulting plot: > > x <- c(10, 50) > y <- c(0.9444483, 0.7680123) > plot(x,y,type="b",log="x") > for(i in 1:50){ > xx <- exp(runif(1,log(min(x)),log(max(x)) )) yy <-...

Hi all, I recently did a run where I built clang executables on FreeBSD 12-CURRENT [1], from trunk r250000 (2015-10-11) all through r327700 (2018-03-16), with increments of 100 revisions. This is mainly meant as an archive, for easily doing bisections, but there are also some interesting statistics. From r250000 through r327700: * the total (stripped) executable size grew by approximately 43% *

In the R source, nmath/pnorm.c contains the code for a rational function approximation for the normal cdf. These constants are listed: const double a[5] = { 2.2352520354606839287, 161.02823106855587881, 1067.6894854603709582, 18154.981253343561249, 0.065682337918207449113 }; The source file cites a paper by Cody (1969) and states th...

...x > > > > > > -----Original Message----- > > From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Evans, > > Richard K. (GRC-H000) > > Sent: Monday, September 25, 2017 3:28 PM > > To: r-help at r-project.org > > Subject: [R] bowed linear approximations > > > > Hello, > > > > Please run the following code snippet and note the resulting plot: > > > > x <- c(10, 50) > > y <- c(0.9444483, 0.7680123) > > plot(x,y,type="b",log="x") > > for(i in 1:50){ > > xx <- e...

...444483, 0.7680123) plot(x,y,type="b",log="x") for(i in 1:50){ xx <- exp(runif(1,log(min(x)),log(max(x)) )) yy <- approx(x,y,xout=xx, method = "linear") points(xx,yy$y) } notice the "log=x" plot parameter and the resulting "bow" in the linear approximation. This makes sense when I realized that the plot command is first making the plot and then drawing straight lines between points on a log plot AFTER the plot is generated and that that's why the line is straight. I get that. .. and it also makes sense that the bowed points are a result of the l...

...things even more confusing, Prentice introduced an "exact marginal likelihood" which is not implemented in R, but which SAS calls the "exact" method. Data is usually not truly discrete, however. More often ties are the result of imprecise measurement or grouping. The Efron approximation assumes that the data are actually continuous but we see ties because of this; it also introduces an approximation at one point in the calculation which greatly speeds up the computation; numerically the approximation is very good. In spite of the irrational love that our profession has for any...

Dear guRus, is there a package that calculates the Approximate Entropy (ApEn) of a time series? RSiteSearch only gave me a similar question in 2004, which appears not to have been answered: http://finzi.psych.upenn.edu/R/Rhelp02a/archive/28830.html RSeek.org didn't yield any results at all. Happy holidays (where appropriate), Stephan

Dear R-users, I learned today that there exists an interesting topic in numerical analysis names "best polynomial approximation" (BSA). Given a function f the BSA of degree k, say pk, is the polynomial such that pk=arginf sup(|f-pk|) Although given some regularity condition of f, pk is unique, pk IS NOT calculated with least square. A quick google tour show a rich field of research and many algorithms proposed for c...

...f times that some of the problems solved by various passes in the optimizer are indeed NP-hard, even though the instances are small enough to be tractable (and very quickly). Is this true? If so, which are these problems? Register allocation? Instruction scheduling? Are they solved exactly or by approximations? Or not solved at all (the need of solving them is avoided in some way)? Greetings, Nicola

I am using pnorm() with the log.p=T argument to get approximations to ln \Phi(x) and qnorm with the log.p=T argument to get estimates of \Phi^{-1}(exp(x)). What approximations are used in these two functions (I noticed in the source pnorm.c it doesn't look like Abramowitz and Stegen) and where can I find the citation? Thanks, Richard Morey

Thanks for raising this. This is something we've recently been looking at too at Sony, as over the course of PS4's lifetime so far we've seen our clang executable on Windows approximately double in size, which isn't ideal for things like distributed build systems. A graph of clang.exe size on our internal staging branch matches yours closely with it being more of a death by a

I'm sure the x86 scheduler models are causing bloat. Every time a single instruction appears on a line by itself like this in a scheduler model: def: InstRW<[SBWriteResGroup2], (instregex "ANDNPDrr")>; It causes that instruction to be its own group in the generated output. And its replicated for each CPU. We should look into better using regular expressions or taking

Hello, Today I was investigating ks.test() with two numerical arguments (x and y) and was left a bit confused about the policy behind handling ties. I might be missing something, so sorry in advance, but here is what confuses me: The documentation states: "The presence of ties always generates a warning, since continuous distributions do not generate them" But when I run a test with

Hi, Having a set of values (non-time series data), what are the approximation functions that could determine the trend of the values? Cheers, Carol [[alternative HTML version deleted]]

Hi all, Issue: I merged two zoo objects (a regular and an irregular). After the merge I used the function 'na.approx' to have also values in the resolution of the regular time series. Problem: After approximation some rows at the beginning or at the end of the zoo objects disappear due to the 'na.approx' algorithm. Now I just want to have all the rows of the regular time series with the approximated values of the irregular time series back. Because some rows disappear I wasn't able to program a...

...lp [mailto:r-help-bounces at r-project.org] On Behalf Of Evans, > Richard K. (GRC-H000) > Sent: Tuesday, September 26, 2017 10:01 AM > To: Eric Berger <ericjberger at gmail.com>; Fox, John <jfox at mcmaster.ca> > Cc: r-help at r-project.org > Subject: Re: [R] bowed linear approximations > > My apologies for the typos in the code. > Here is a corrected version you can copy/paste in R to see the issue. > > freq <- c(2, 3, 5, 10, 50, 100, 200, 300, 500, 750, 1000, 1300, 1800, 2450, 2900, > 3000, 4000, 5000, 6000, 7000, 8200, 9300, 10000, 11000, 18000, 26500, 3...

...0.59 0.991 x2 16.78 1 0.0000 18.19 2 0.0001 14.19 0.882 procedure 26.12 1 0.0000 44.31 3 0.0000 38.31 0.711 ClinicalScore 25.75 1 0.0000 70.06 4 0.0000 62.06 0.544 x1 83.42 1 0.0000 153.49 5 0.0000 143.49 0.000 Then, fitted an approximation to the full model using most imprtant variable (R^2 for predictions from the reduced model against the original Y drops below 0.95), that is, dropping "stenosis". > full.ols.approx <- ols(plogit ~ x1+x2+ClinicalScore+procedure) > full.ols.approx$stats n Model L.R....