search for: approximate

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.

...s 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 following formula: model <- lm(Approximate.Counts~X..Light.Transmission + I(Approximate.Counts^2), data=Standards) It gives me a pretty decent graph: xyplot(Approximate.Counts + fitted(model) ~ X..Light.Transmission, data=Standards) I'm pretty happy with it, and looking at the model summary, to my inexperienced eyes it seems pretty...

...ine, 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 about ecdf) > > I think it _is_ an ECDF you want to approximate, since you mention > `sample' below. no, it is not. I do not need the closeness to a ECDF but to a CDF. classic ECDF (like that implemented in stepfun) is yet another approximation of CDF. In particular, if I try to pursue the best approximation of any ECDF function in polynomial basis, i...

...line produced on the plot as > shown, so I realize that 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...

...NT [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% * the size of the text segment grew by approximately 41% * the size of the data segment grew by approximately 61% * the size of the bss segment grew by approximately 185% * real build time (on a 32 core system) grew by approximately 60% * user build time (on a 32 core system) grew by approxim...

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

...> > shown, so I realize that 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'...

...ions that lie on the line produced on the plot as shown, so I realize that 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 great...

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 thousand cuts rather than being down to a small number of sudden big-bang changes. I did spot one ra...

...Sat, Mar 17, 2018 at 6:26 AM, Greg Bedwell via cfe-dev < cfe-dev at lists.llvm.org> wrote: > 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 thousand cuts rather than being down to a small number of sudden > big-bang changes...

...;The presence of ties always generates a warning, since continuous distributions do not generate them" But when I run a test with ties there is no warning: ks.test(1:4, 4:7) However, when I specify that I do not want an exact test, there appears a warning saying that the computation will be approximate: ks.test(1:4, 4:7, exact=FALSE) # Warning: p-value will be approximate in the presence of ties But isn?t specifying exact=FALSE already makes the test approximate? I tried inspecting the source code for guidance but also was left a bit puzzled. In ks.test.R under if(is.numeric(y)) clause there i...

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 [[alternati...

...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...

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 (ols in rms package) as the followings. I would like to know whether I am doing right or not. > library(rms) > plogit <- predict(full.model) > full.ols <- ols(plogit ~...