similar to: Help on mapping memory

Gene names being misinterpreted by spreadsheet software (read.csv is no different) is a classic issue in bioinformatics. It seems like every practitioner ends up encountering this issue in due time. E.g. https://pubmed.ncbi.nlm.nih.gov/15214961/ https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1044-7 https://www.nature.com/articles/d41586-021-02211-4

Hi Peter, Rui, Chrstophe and Gabriel, Thanks for your inputs -- the use of qnorm(., log=TRUE) is a good point in line with pnorm with which we devised log(p) as log(2) + pnorm(-abs(z), lower.tail = TRUE, log.p = TRUE) that could do really really well for large z compared to Rmpfr. Maybe I am asking too much since z <-20000 >

No, that will make it even worse since you'll be declaring a lot more memory that you actually have. The real problem is that you're ignoring the truncation, so you probably want to use something like if (snprintf(tempname, sizeof(tempname), "%s.%d", of1name, j) >= sizeof(tempname)) Rf_error("file name is too long"); BTW: most OSes systems have a path limits that

Hi again, I realised it is useful to replicate the warnings locally without relying on CRAN automatic check; instead of R(-devel) CMD check --as-cran package_version.tar.gz one can use R CMD check --configure-args="" and in my case the WARNINGS were initially given with https://www.stats.ox.ac.uk/pub/bdr/gcc9/README.txt and those specification might as well used in --configure-args

On Thu, May 30, 2019 at 7:21 PM Simon Urbanek <simon.urbanek at r-project.org> wrote: > > No, that will make it even worse since you'll be declaring a lot more memory that you actually have. > > The real problem is that you're ignoring the truncation, so you probably want to use something like > > if (snprintf(tempname, sizeof(tempname), "%s.%d", of1name,

include/Rmath.h declares a set of 'logspace' functions for use at the C level. I don't think there are core R functions that call them. /* Compute the log of a sum or difference from logs of terms, i.e., * * log (exp (logx) + exp (logy)) * or log (exp (logx) - exp (logy)) * * without causing overflows or throwing away too much accuracy: */ double Rf_logspace_add(double

Hum... This boils down to > as.numeric("1.23e") [1] 1.23 > as.numeric("1.23e-") [1] 1.23 > as.numeric("1.23e+") [1] 1.23 which in turn comes from this code in src/main/util.c (function R_strtod) if (*p == 'e' || *p == 'E') { int expsign = 1; switch(*++p) { case '-': expsign = -1; case

Hi All, Thanks for all your comments which allows me to appreciate more of these in Python and R. I just came across the matrixStats package, ## EXAMPLE #1 lx <- c(1000.01, 1000.02) y0 <- log(sum(exp(lx))) print(y0) ## Inf y1 <- logSumExp(lx) print(y1) ## 1000.708 and > ly <- lx*100000 > ly [1] 100001000 100002000 > y1 <- logSumExp(ly) > print(y1) [1] 100002000

I agree with many the sentiments about the wisdom of computing very small p-values (although the example below may win some kind of a prize: I've seen people talking about p-values of the order of 10^(-2000), but never 10^(-(10^8)) !). That said, there are a several tricks for getting more reasonable sums of very small probabilities. The first is to scale the p-values by dividing the

You may want to look into using the log option to qnorm e.g., in round figures: > log(1e-300) [1] -690.7755 > qnorm(-691, log=TRUE) [1] -37.05315 > exp(37^2/2) [1] 1.881797e+297 > exp(-37^2/2) [1] 5.314068e-298 Notice that floating point representation cuts out at 1e+/-308 or so. If you want to go outside that range, you may need explicit manipulation of the log values. qnorm()

>>>>> William Dunlap via R-devel >>>>> on Sun, 23 Jun 2019 10:34:47 -0700 writes: >>>>> William Dunlap via R-devel >>>>> on Sun, 23 Jun 2019 10:34:47 -0700 writes: > include/Rmath.h declares a set of 'logspace' functions for use at the C > level. I don't think there are core R functions that call

I'm looking for a port of Schafer's PAN module for multiple imputation of nested data. It is written in S-Plus, and I would like to use it in R. Any pointers most appreciated. Best wishes, Paul von Hippel

In article <E02FA554-9D6D-4E7D-8A78-5FBDE1DE939D at kicp.uchicago.edu>, Valeri Galtsev <galtsev at kicp.uchicago.edu> wrote: > > > > On Sep 26, 2020, at 8:05 AM, Jerry Geis <jerry.geis at gmail.com> wrote: > > > > I have a disk that is flagging errors, attempting to rescue the data. > > > > I tried dd first - if gets about 117G of 320G disk

OK, I am definitely new to porting from Windows to Linux. You may have already seen my previous post about Winelib, ignore it for the time being. We have an app that dates back several developers, which of course no longer are available, and several years. We have had quite a few requests for a version that runs on Linux and hence the work to get the app ported. It is not a large app by most

> On Sep 26, 2020, at 8:05 AM, Jerry Geis <jerry.geis at gmail.com> wrote: > > I have a disk that is flagging errors, attempting to rescue the data. > > I tried dd first - if gets about 117G of 320G disk and stops incrementing > the save image any more. did you try dd conv=noerror ? this flag makes dd not stop on input error. Whatever is irrecoverable is irrecoverable,

@tonymountifield Does this still hold true? https://superuser.com/a/1075837 On Sun, Sep 27, 2020 at 7:21 AM Tony Mountifield <tony at softins.co.uk> wrote: > In article <E02FA554-9D6D-4E7D-8A78-5FBDE1DE939D at kicp.uchicago.edu>, > Valeri Galtsev <galtsev at kicp.uchicago.edu> wrote: > > > > > > > On Sep 26, 2020, at 8:05 AM, Jerry Geis

Hello, Well, try it: p <- .Machine$double.eps^seq(0.5, 1, by = 0.05) z <- qnorm(p/2) pnorm(z) # [1] 7.450581e-09 1.228888e-09 2.026908e-10 3.343152e-11 5.514145e-12 # [6] 9.094947e-13 1.500107e-13 2.474254e-14 4.080996e-15 6.731134e-16 #[11] 1.110223e-16 p/2 # [1] 7.450581e-09 1.228888e-09 2.026908e-10 3.343152e-11 5.514145e-12 # [6] 9.094947e-13 1.500107e-13 2.474254e-14 4.080996e-15

Hi, I have a big dataset which has many missing values and want to implement Multiple imputation via Monte carlo markov chain by following J Schafer's "Analysis of incomplete multivariate data". I don't know where to begin and is looking for a sample R code that implements multiple imputation with EM, MCMC, etc.... Any help / suggestion will be greatly appreciated. David

Hello, I'm seeking the solution to live storage migration using blockcopy. Previously, the "virsh undefine" is required before blockcopy. https://www.redhat.com/archives/libvirt-users/2015-October/msg00027.html QEMU has "block-dirty-bitmap-*" operations now, are there steps for the live storage migration using blockcopy without undefine? By the way, why's the purpose

Dear R-developers, I came to a somewhat unexpected behaviour of read.csv() which is trivial but worthwhile to note -- my data involves a protein named "1433E" but to save space I drop the quote so it becomes, Gene,SNP,prot,log10p YWHAE,13:62129097_C_T,1433E,7.35 YWHAE,4:72617557_T_TA,1433E,7.73 Both read.cv() and readr::read_csv() consider prot(ein) name as (possibly confused by