similar to: Apply weights to the Efron Approximation

Hello: Could you possibly help me. In Efron's 2004 paper "Selection and Estimation ..." it was mentioned that so-called effect density estimate, denoted by g1(mu) was included in the locfdr package. However, I can't find it in the description of the package. Any suggestions? Sincerely, Nik -- View this message in context:

Hello, My name is Lisa and I'm a statistician at Princess Margare Hospital. I wonder if there is any function in R that calculate the Normal rankits based on Blom's approximation? Thank you very much Lisa Wang Msc. Princess Margaret hospital Toronto, Ca

Does anyone have any R code that shows how to do a Laplace Approximation? I know there are a variety of these numerical approximation algorithms and I'm pretty open at this point, I'm just curious how it's approximated in R code. I have seen some functions in packages, but I think they all call C code, and am looking for an example in R only. Thanks. -- View this message in

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

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]]

> 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

Hello, I received the following warning when running chi-square; n Is there a way to catch the 'error' code of 'warning' after run chisq.test(x)? n What does this error mean? Thank you for your help. [[alternative HTML version deleted]]

I'm trying to find an R package that will smooth a line with the "polynomial approximation with exponential kernel" or "PAEK" algorithm that is used in ESRI's ArcGIS software. I have a copy of the original paper that provides the algorithm, but I'm not confident in how to code it. The algorithm will smooth a polyline's vertices at a user specified distance

I have some big combinations like: 4444444444444444444444444444 choose 784645433 Can R compute these? Is there any package that does stirlings approximation in R? -- Thanks, Jim. [[alternative HTML version deleted]]

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.

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

I am getting "Chi-squared approximation may be incorrect in: chisq.test(x)" with the data bleow. Frequency distribution of number of male offspring in families of size 5. Number of Male Offspring N 0 518 1 2245 2 4621 3 4753 4 2476 5

Dear list, I have a series of data points which I want to approximate with exactly two linear functions. I would like to choose the intervals so that the total deviation from my fitted lines is minimal. How do I best do this? Thanks! Kamila The information transmitted in this electronic communication...{{dropped}}

Dear R experts, I am trying to analyze data from an article, the data looks like this Patient Age Sex Aura preCSM preFreq preIntensity postFreq postIntensity postOutcome 1 47 F A 4 6 9 2 8 SD 2 40 F A/N 5 8 9 0 0 E 3 49 M N 5 8 9 2 6 SD 4 40 F A 5 3 10 0 0 E 5 42 F N 5 4 9 0 0 E 6 35 F N 5 8 9 12 7 NR 7 38 F A 5 NA 10 2 9 SD 8 44 M A 4 4 10 0 0 E 9 47 M A 4 5 8 2 7 SD 10 53 F A 5 3 10 0 0 E 11

The floating-point expectations allow for an error tolerance. Is there any similar facility for dates? For example, say I have a custom class that handles date/time spans and I want to spec it: context "A DateRange span" do specify "should know when a week ago is :)" do d = DateRange.new d.last_week.should_be_close_to(1.week.ago, 24*60*60) end end The idea