similar to: Recursive Computation in R

I am working on a project that requires me to do very large factorial evaluations. On R the built in factorial function and the one I created both are not able to do factorials over 170. The first gives an error and mine return Inf. Is there a way to have R do these larger calculations (the calculator in accessories can do 10000 factorial and Maple can do even larger) -- View this message in

Hi, I tried to calculate the formula for the birthday problem (the probability that at least two people out of a group of n people share the same birthday) But the factorial-function allows me only to calculate factorials up to 170. So is there a way to push that limit? to solve this formula: (factorial(365) / factorial((365-23))) / (365^23) (n=23)

Hi all, Does anybody know if it is possible to build a fractional factorial design in R? That is, suppose that we want do design an experiment with 3 factors with 2, 3 and 3 levels, respectivly. However we want to consider, let's say, only 6 from all possible level combinations. Does R design such experiment? Thanks in advance, Caio [[alternative HTML version deleted]]

I'm a total newbie at using R, and so there probably is a better way to do this. However, I couldn't find one, and so maybe this will help someone. I was calculating log-likelihoods using a multinomial model, and found that for large n, prod(n:1) wouldn't work to calculate factorials (e.g., prod(200:1) = Inf). The below function calculates the natural log of a factorial (e.g.

I've looked through the documentation with R-0.64.1 and have been unable to find a high-level function for evaluation of factorials (i.e., n!, not factorial designs). Is there such a function? It is trivial to code, so everyone could write their own, but it also would be worthwhile as a standard tool. I'm guessing I am just overlooking it.

PLEASE HELP I actually want to do the following: a[j] = (1/(j!))*Π (i-1-d), j = 500, Π means product i = 1 to j   Yet, j! will stop at 170 and Π (i-1-d) at 172; so, a[j] will not exceed 170. I would like to have at least 200 a[j].   WHAT SHOULD I DO?   PLEASE SEE MY CODE FOR DETAIL!! #################################################### R CODE:

Dear List, I am looking for a function `Pseudo standard error' (PSE), which is available in S+ DOX (design of experiemnt) module - Is there a similar function available in R? Reference for PSE function is in the paper: 'Quick and easy analysis of unreplicated factorials' by Russell V. Lenth, Technometrics, 1989, 31, 4, 469-473. Thanks. -Nitin

I posted this last night but I think I figured out the problem. I checked on the underlying equation for the Fisher Exact Test for tables greater than 2X2. It looks like I have an insane amount of factorials to be multiplied. Is that problem? Is it an overflow issue? Jeff Last night’s post: I just a ran a fisher.test on a 9x5 table and received the following message

Hello all, I'd like to announce the availability of a mailing list for a newly-formed SIG (Special Interest Group) dedicated to using R for Discrete Choice Modelling. This list is intended for discussion of issues revolving around the design and analysis of Discrete Choice (aka Stated Choice, Stated Preference or Choice-Based Conjoint) experiments. While R has good infrastructure for

Boxplot and bxp seem to have changed behaviour a bit of late (R 2.4.1). Or maybe I am mis-remembering. An annoying feature is that while at=3:6 will work, there is no way of overriding the default xlim of 0.5 to n+0.5. That prevents plotting boxes on, for example, interval scales - a useful thing to do at times. I really can see no good reason for bxp to hard-core the xlim=c(0.5, n+0.5) in the

Here's a question for ecologists on the r-help list-- I'm addressing this to ecologists in particular because they're most likely to be familiar with the equation in question but I'll be happy to discuss the problem with anyone who's willing to take a whack at it. I'm trying to write a function to calculate the large sample variance of species richness estimates by

Dear R friends, I was wondering if there were factorial and modulus functions out there that I've somehow overlooked? -P. -- Peter L. Hurd, Ph.D. phurd at uts.cc.utexas.edu http://www.zo.utexas.edu/research/phurd fax 512.471-3878 Section of Integrative Biology, University of Texas, Austin TX 78712 USA

Dear list, perhpas this question is more suitable for R-dev but since I am not really a developer I post it here first. Apparently the following lines do not create any problem in R: library(combinat) r <- 20; b <- 2; sum( sapply(0:r,function(x) nCm(r,x)^(2*b)) ) > 2^64 while in C I obtain an overflow of data even using unsigned long long and with long double I incurr in precision

L.S. We would like to generate fractional design for a conjoint analysis. For example we would like to reduce 2x2x2x2x3 --> 12-16 profiles. We tried the R-package "conjoint" and we found an article which describes how to make fractional design for these kinds of studies with Algdesign: http://tolstoy.newcastle.edu.au/R/e10/help/att-8876/DCE_with_R.pdf However, the generated

Dear R community, I am new to R, a reforming SAS user :) I am running R 2.10.1 on a Windows XP machine. I would like to write linear functions of my coefficient parameter estimates from a glm, but am having a difficult time understanding the parameterization R uses. In the toy example below I am running a glm on binomial data, with clones and lines within clones as fixed effects, each with 6

Dear R useRs, i have the following code to compute values needed for a contour plot ############################################################ "myContour" <- function(a, b, plist, veca, vecb, dim) { tmpb <- seq(0.5 * b, 1.5 * b, length=dim) tmpa <- seq(0.5 * a, 1.5 * a, length=dim) z <- matrix(0, nrow=dim, ncol=dim) for(i in 1:dim) { for(j in 1:dim)

2.6.25-stable review patch. If anyone has any objections, please let us know. ------------------ From: Mark McLoughlin <markmc at redhat.com> commit 23cde76d801246a702e7a84c3fe3d655b35c89a1 upstream. hdr->csum_start is the offset from the start of the ethernet header to the transport layer checksum field. skb->csum_start is the offset from skb->head. skb_partial_csum_set()

On Tue, May 27, 2008 at 12:20:47PM +0100, Mark McLoughlin wrote: > hdr->csum_start is the offset from the start of the ethernet > header to the transport layer checksum field. skb->csum_start > is the offset from skb->head. > > skb_partial_csum_set() assumes that skb->data points to the > ethernet header - i.e. it computes skb->csum_start by adding > the

On Tue, May 27, 2008 at 12:20:47PM +0100, Mark McLoughlin wrote: > hdr->csum_start is the offset from the start of the ethernet > header to the transport layer checksum field. skb->csum_start > is the offset from skb->head. > > skb_partial_csum_set() assumes that skb->data points to the > ethernet header - i.e. it computes skb->csum_start by adding > the

Hello, Thanks everyone who replied to my earlier Q. I ran into a problem. I am analysng multicenter clinical trials. The model is Y=treatment + center + treatment*center. I have the effect sizes and the patient distributions for each scenario in the simulation. Can someone tell me how to compute the power in this case? May be by using a F-test or something like that. Thank you very much.