similar to: New package for Biased Urn Models

Dear R Users, I employed the phyper() function to estimate the likelihood that the number of genes overlapping between 2 different lists of genes is due to chance. This appears to work appropriately. Now i want to try this with 3 lists of genes which phyper() does not appear to support. Some googling suggests i can utilize the Multivariate hypergeometric distribution to achieve this. eg.:

I am building a package for various noncentral hypergeometric distributions. I want to include some heavy mathematical formulas. It appears that the build and INSTALL commands produce only .chm files and not .pdf files from my .Rd files under Windows. This means that it cannot show complicated mathematical formulas. The solution might be to include a .pdf file as a vignette. I tried that,

I am building a package for various noncentral hypergeometric distributions. I want to include some heavy mathematical formulas. It appears that the build and INSTALL commands produce only .chm files and not .pdf files from my .Rd files under Windows. This means that it cannot show complicated mathematical formulas. The solution might be to include a .pdf file as a vignette. I tried that,

I'm going to use dhyper(x, m, n, k) to get a 95% coverage. Let me use an example to explain my problem: Suppose I have a urn containing 90 red and 10 black balls. Now I wanna remove 3 from the urn. By the following codes: m<-90;n<-10;k<-3; x<-0:3 dhyper(x,m,n,k) I can obtain the probability that 0,1,2,3 red balls will be removed. 0.000742115 0.025046382 0.247680891

For those who are interested, I have made available a R function for noncentral hypergeometric distribution at http://www.geocities.com/jg_liao/software/Hypergeometric/hypergeometric_in_R.txt The paper that describes the algorithm will appear in The American Statistician. The function does not run on S-plus as the R's scoping rule is used. Here is how the function can be used: > n1

Dear R Users, There exists a non-central hypergeometric distribution function in the (MCMCpack) package, and a hypergeometric distribution function in the (stats) package. Is there a function for sampling from an extended hypergeometric distribution? Thanks, Narcyz This message is intended for the addressee named and may con...{{dropped}}

Hello, I use a software that outputs the data in a form/dialect(?) of xml (I think its called "SpreadsheetML", see below for an excerpt). It allows (admittedly) easy import of such files into Excel. I would like, however, to import it directly into R. I am familiar how to do that with standard xml-files (mostly thanks to Duncan Temple Lang's help). But I haven't found the right

The problem occurs when the sample odds ratio is Inf, such as in the following example. Given the fact that both upper bounds of the two 95% confidence intervals are Inf, I would have expected that the two lower bounds be equal, but they aren't. x <- matrix(c(9,4,0,2),2,2) x # [,1] [,2] #[1,] 9 0 #[2,] 4 2 rbind("two.sided.95CI"=fisher.test(x)$conf.int,

On Fri, Jul 27, 2012 at 2:37 PM, Michael Gottesman <mgottesman at apple.com> wrote: ... > I have actually timed said instructions in the past and reproduced Agner > Fog's results. I just prefer to speak by referring to facts that can not be > misconstrued as hearsay = ). That would be great. Also, can you point me to the Agner Fog table that you are referring to? Thanks.

>>>>> "PD" == Peter Dalgaard <p.dalgaard@biostat.ku.dk> >>>>> on 24 Nov 2004 18:32:15 +0100 writes: PD> tlumley@u.washington.edu writes: >> > > dhyper() does not allow non-integer values for input >> parameters m and n. >> > >> > this is in contrast to the other functions in the

"help" We want to estimate the number of caribou in Jasper. We recently conducted an aerial survey and saw 70 uncollared caribou and 8 of 11 collared caribou. We want to estimate the number of caribou in this population with 95% confidence limits. Gary White uses the hypergeometric distribution and determines the population estimates using maximum likelihood and 95%CL as

> It appears that the stats you listed are for movaps [SSE], not vmovaps [AVX]. I would *assume* that vmovaps(m128) is closer to vmovaps(m256), since they are both AVX instructions. Although, yes, I agree that this is not clear from Agner's report. Please correct me if I am misunderstanding. You are misunderstanding [no worries, happens to everyone = )]. The timings I listed were for

Can someone help me out with this problem? Write an R program to draw 10,000 random samples using the Polya Urn Scheme where the initial contents of the urn is one white ball and one black ball. Accumulate the proportion of white balls after each draw into one vector. Upload your R script file. HINT: You can simulate sampling one ball from an urn with N balls of which w are white and N - w are

On 03/15/2018 10:04 AM, Guillaume Chatelet via llvm-dev wrote: > [You can find an easier to read and more complete version of this RFC > here > <https://docs.google.com/document/d/1QidaJMJUyQdRrFKD66vE1_N55whe0coQ3h1GpFzz27M/edit?ts=5aaa84ee#>.] > > Knowing instruction scheduling properties (latency, uops) is the basis > for all scheduling work done by LLVM. > > >

Sounds like a very useful tool.  Thank you for contributing. Taking a step back and looking at the big picture, combining this with the recently contributed llvm-mca dramatically improves our scheduling and performance analysis story.  Being able to take a snippet of code on a particular machine, measure latency/throughput/ports for each instruction (this tool), and then analyze the entire

On Thu, Mar 15, 2018 at 4:41 PM, Hal Finkel via llvm-dev < llvm-dev at lists.llvm.org> wrote: > > On 03/15/2018 10:04 AM, Guillaume Chatelet via llvm-dev wrote: > > [You can find an easier to read and more complete version of this RFC here > <https://docs.google.com/document/d/1QidaJMJUyQdRrFKD66vE1_N55whe0coQ3h1GpFzz27M/edit?ts=5aaa84ee#> > .] > > Knowing

[You can find an easier to read and more complete version of this RFC here <https://docs.google.com/document/d/1QidaJMJUyQdRrFKD66vE1_N55whe0coQ3h1GpFzz27M/edit?ts=5aaa84ee#> .] Knowing instruction scheduling properties (latency, uops) is the basis for all scheduling work done by LLVM. Unfortunately, vendors usually release only partial (and sometimes incorrect) information. Updating the

According to Agner Fog, "...you must make sure that all calls are matched with returns. Never jump out of a subroutine without a return and never use a return as an indirect jump." (see paragraph 3.15 in microarchitecture.pdf and examples 3.5a and 3.5b in optimizing_assembly.pdf) Basically this patch replaces call .get_eip0 .get_eip0: pop eax with call .mov_eip_to_eax

1) Two comments ";ASSERT(lp_quantization <= 31)" in the new functions ..._wide_asm_ia32() -- just to mention this constraint. (max. possible value of lp_quantization is 15, so it's not a problem) 2) "mov cl, ..." was replaced with "mov ecx, ..." (again Agner Fog, optimizing_assembly.pdf) summary: write to a partial register may result in false dependencies

I sent a message a couple days ago about doing calculations for power of the ANOVA. Several people got back to me very quickly which I really appreciated. I'm working now on a similar problem, but instead of a balanced ANOVA, I have an unbalanced one. The first part of the question was: You assume that the within-population standard deviations all equal 9. You set the Type 1 error rate at á