search for: approximately

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

Dear Rich, Assuming that I understand what you want to do, try adding the following to your script (which, by the way, is more complicated that it needs to be): xx <- 10:50 m <- lm(y ~ x) yy <- predict(m, data.frame(x=xx)) lines(spline(xx, yy), col="blue") m <- lm(y ~ log(x)) yy <- predict(m, data.frame(x=xx)) points(xx, yy, col="magenta") The first set of

...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 approximat...

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

Hi Rich, If I understand your comment about "uniformly distributed along the log=x axis" then I think John's (second) set of commands needs a change to the definition of xx, as follows: xx <- exp(seq(from=log(min(x)),to=log(max(x)),length=50)) m <- lm(y ~ log(x)) yy <- predict(m, data.frame(x=xx)) points(xx, yy, col="red") HTH, Eric On Mon, Sep 25, 2017 at

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 <- approx(x,y,xout=xx, method = "linear") points(xx,yy$y) } notice the "log=x" plot parameter and the resulting "bow" in the

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 rang...

...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....

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

Dear Rich, I think that it's generally a bad idea to give statistical (as opposed to simply technical) advice by email without knowing the context of the research. I think that you'd do well to seek help from a statistician, and not just do what I suggest below. Interpolating the data only makes sense if there's no random component to the response (mag in your data). Otherwise, it

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