search for: approximated

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 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

...linear approximations, but > maybe "log" approximations? > However, the approximation methods are only "linear" and "constant" .. there > isn't a "log" method to approximate with. > > So can anyone tell me how to fix the code such that he approximated points > DO lie on the line as plotted with the "log=x" plot parameter? > Oh, and they have to be uniformly distributed along the Log=x axis.. I think > that's the tricky part. > > Any help and/or insight would be greatly appreciated. > > Thank you! > -Rich &...

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 that these

...gt; > maybe "log" approximations? > > However, the approximation methods are only "linear" and "constant" .. > there > > isn't a "log" method to approximate with. > > > > So can anyone tell me how to fix the code such that he approximated > points > > DO lie on the line as plotted with the "log=x" plot parameter? > > Oh, and they have to be uniformly distributed along the Log=x axis.. I > think > > that's the tricky part. > > > > Any help and/or insight would be greatly appreciated....

...what I want to do is NOT linear approximations, but maybe "log" approximations? However, the approximation methods are only "linear" and "constant" .. there isn't a "log" method to approximate with. So can anyone tell me how to fix the code such that he approximated points DO lie on the line as plotted with the "log=x" plot parameter? Oh, and they have to be uniformly distributed along the Log=x axis.. I think that's the tricky part. Any help and/or insight would be greatly appreciated. Thank you! -Rich [[alternative HTML version deleted]]

For time scale that are truly discrete Cox proposed the "exact partial likelihood". I call that the "exact" method and SAS calls it the "discrete" method. What we compute is precisely the same, however they use a clever algorithm which is faster. To make things even more confusing, Prentice introduced an "exact marginal likelihood" which is not

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

Hi all. I’ve heard a couple of 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

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]]

...#39;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 code which works properly for 'all' circumstances. I also tried to work with the 'index' function. Didn't work out so far. Thank you for your help. Regards, Alex [[alternativ...

...gt; plot(freq,mag,type="b",log="x"); for(i in 1:200){ xx <- > exp(runif(1,log(min(freq)),log(max(freq)) )); yy <- approx(freq,mag,xout=xx, > method = "linear"); points(xx,yy$y,col=rgb(1,0,0)); } > > For completeness, I have been puzzling over why the approximated points > don't lie linearly over the original data set (especially prominent in the bow > between freq=10 and 50). Once I realized (and concurred with) why this bow > exists, I have been struggling with how to make these approximations as > expected.. In my original post, I think I...

Hi, I am trying to construct a logistic regression model from my data (104 patients and 25 events). I build a full model consisting of five predictors with the use of penalization by rms package (lrm, pentrace etc) because of events per variable issue. Then, I tried to approximate the full model by step-down technique predicting L from all of the componet variables using ordinary least squares