similar to: help with mclust

> No HTML this time. Sorry Dear all, I am attempting to model some one-dimensional data using Gaussian mixture model with mclust.? Generally, the data that I have have 3 overlapping populations (with one of them being the majority, and the other two combining to less than 15%) and for some reason, mclust consistently ignores the smaller peaks, giving me strange values for the means

Quite possibly, and I?ll look into that. Aside from the work I was doing, however, I wonder if there is a way such that sapply could avoid the overhead of having to call the identical function to determine the conditional path. From: William Dunlap [mailto:wdunlap at tibco.com] Sent: Tuesday, March 13, 2018 12:14 PM To: Doran, Harold <HDoran at air.org> Cc: Martin Morgan <martin.morgan

FYI, in R devel (to become 3.5.0), there's isFALSE() which will cut some corners compared to identical(): > microbenchmark::microbenchmark(identical(FALSE, FALSE), isFALSE(FALSE)) Unit: nanoseconds expr min lq mean median uq max neval identical(FALSE, FALSE) 984 1138 1694.13 1218.0 1337.5 13584 100 isFALSE(FALSE) 713 761 1133.53 809.5 871.5

Thanks, Martin, that (inherit from a virtual class) worked great. I already had a base class created with setUnion, and so this was an easy switch. I had assumed that since inheriting from a class without slots would produce a class without slots the result would still be virtual. Fortunately that's not the case. Ross ________________________________________ From: Martin Morgan

Using the correct mode absolutely solves it. Apologies for not trying the obvious. Cheers Joris On Thu, May 3, 2018 at 2:10 PM, Martin Morgan <martin.morgan at roswellpark.org > wrote: > > > On 05/02/2018 03:21 PM, Joris Meys wrote: > >> Dear all, >> >> I've noticed by trying to download gz files from here : >>

>>>>> Qin Zhu <qinzhu at outlook.com> >>>>> on Fri, 6 May 2016 11:33:37 -0400 writes: > Thanks for all your great answers. > The app I?m working on is indeed an exploratory data analysis tool for gene expression, which requires a bunch of bioconductor packages. > I guess for now, my best solution is to divide my app into modules and

Thanks for looking into it. Is there a way to avoid the HEAD request in R 3.3.0? I'm asking because if there isn't, then I'll add a workaround in a package I'm working on. -Winston On Tue, Jun 21, 2016 at 9:45 PM, Martin Morgan <martin.morgan at roswellpark.org> wrote: > On 06/21/2016 09:35 PM, Winston Chang wrote: >> >> In R 3.2.4, if you ran

> On 6/21/19 10:56 AM, Tierney, Luke wrote: > [...] > Something that would be useful and is being considered is having a > mechanism for registering default condition handlers. This would allow > the condition to be re-signaled with a custom class and then having > a custom conditionMessage method is less likely to cause conflicts. Is it correct that you are proposing something

>>>>> Henrik Bengtsson <henrik.bengtsson at gmail.com> >>>>> on Tue, 13 Mar 2018 10:12:55 -0700 writes: > FYI, in R devel (to become 3.5.0), there's isFALSE() which will cut > some corners compared to identical(): > > microbenchmark::microbenchmark(identical(FALSE, FALSE), isFALSE(FALSE)) > Unit: nanoseconds > expr

Could your code use vapply instead of sapply? vapply forces you to declare the type and dimensions of FUN's output and stops if any call to FUN does not match the declaration. It can use much less memory and time than sapply because it fills in the output array as it goes instead of calling lapply() and seeing how it could be simplified. Bill Dunlap TIBCO Software wdunlap tibco.com On Tue,

Martin In terms of context of the actual problem, sapply is called millions of times because the work involves scoring individual students who took a test. A score for student A is generated and then student B and such and there are millions of students. The psychometric process of scoring students is complex and our code makes use of sapply many times for each student. The toy example used

Thanks for all your great answers. The app I?m working on is indeed an exploratory data analysis tool for gene expression, which requires a bunch of bioconductor packages. I guess for now, my best solution is to divide my app into modules and load/unload packages as the user switch from one module to another. This brought me another question: it seems that unload package with the

Hi, A benchmarking study with an additional (data.table-based) solution. Enjoy! ;) Cheers, Denes -------------------------- ## packages ########################## library(dplyr) library(data.table) library(IRanges) library(microbenchmark) ## prepare example dataset ########### ## use Bert's example, with 2000 stations instead of 2 d_df <- data.frame( station =

Hi Martin, On 11/29/2017 10:46 PM, Martin Morgan wrote: > On 11/29/2017 04:15 PM, T?th D?nes wrote: >> Hi, >> >> A benchmarking study with an additional (data.table-based) solution. > > I don't think speed is the right benchmark (I do agree that correctness > is!). Well, agree, and sorry for the wording. It was really just an exercise and not a full

Or with the Bioconductor IRanges package: df <- with(input, DataFrame(station, year=IRanges(from, to), record)) expand(df, "year") DataFrame with 24 rows and 3 columns station year record <character> <integer> <character> 1 07EA001 1960 QMS 2 07EA001 1961 QMC 3 07EA001 1962 QMC 4

On 11/29/2017 04:15 PM, T?th D?nes wrote: > Hi, > > A benchmarking study with an additional (data.table-based) solution. I don't think speed is the right benchmark (I do agree that correctness is!). For the R-help list, maybe something about least specialized R knowledge required would be appropriate? I'd say there were some 'hard' solutions -- Michael (deep