search for: jayemerson

Hi, I have used the heckit function in micEcon. Now I would like to evaluate the fit of the probit part of the model but when I enter AIC(sk$probit) I get this error Error in logLik(object) : no applicable method for "logLik" How can I then get the AIC for this model? Side question: If you know - from the top of your head - some link to readings dealing with evaluating the

...is case? Also the Upper Limit of Class 6 is specified as 128 bytes(Node Classes) Will there be any side effect in increasing this to 512 bytes or so (depending on the average of the Application data) Advance Thanks & Regards R.Subramanian On Tue, Jun 24, 2008 at 11:22 PM, Jay Emerson <jayemerson@gmail.com> wrote: > I saw your post to r-project... multiple MB of data isn't really a > problem. Multiple GB of data certainly are. I'm wondering if you can > clarify the problems you are having? It isn't clear to me that simply > replacing malloc would do much. R h...

We are building a package, and want to create S4 methods for both head and mean for our own BigMatrix class. Following the recommendation in "Writing R Extensions" we use exportMethods instead of export in NAMESPACE (this is described as being "clearer"). This works for head, but not for mean. Obviously we importFrom(utils, head), but don't need to do this for mean,

I was surprised to observe the following difference between 2.4.1 and 2.6.0 after a long overdue upgrade a few months ago of our departmental server. It wasn't a bug fix, but a subtle improvement. Here's the simplest example I could create. The size is excessive, on the order of the Netflix Competition data. The integer matrix is about 1.12 GB, and if coerced to numeric it is 2.24 GB.

I am using the library(bigmemory) to handle large datasets, say 1 GB, and facing following problems. Any hints from anybody can be helpful. _Problem-1: _ I am using "read.big.matrix" function to create a filebacked big matrix of my data and get the following warning: > x = read.big.matrix("/home/utkarsh.s/data.csv",header=T,type="double",shared=T,backingfile

...> -- > Ista Zahn > Graduate student > University of Rochester > Department of Clinical and Social Psychology > http://yourpsyche.org > > > > ------------------------------ > > Message: 3 > Date: Tue, 23 Mar 2010 08:10:21 -0400 > From: Jay Emerson <jayemerson at gmail.com> > To: r-help at r-project.org, > sunitap22 at gmail.com > Subject: Re: [R] Mosaic plots > Message-ID: > ??? <d4588dec1003230510i5f6b3b3cnd4f30af34b9bd9f1 at mail.gmail.com> > Content-Type: text/plain > > As pointed out by others, vcd supports mosaic p...

Package "bigmemory" is now available on CRAN. A brief abstract follows: Multi-gigabyte data sets challenge and frustrate R users even on well-equipped hardware. C/C++ and Fortran programming can be helpful, but is cumbersome for interactive data analysis and lacks the flexibility and power of R's rich statistical programming environment. The new package bigmemory bridges this gap,

The re-engineered bigmemory package is now available (Version 3.5 and above) on CRAN. We strongly recommend you cease using the older versions at this point. bigmemory now offers completely platform-independent support for the big.matrix class in shared memory and, optionally, as filebacked matrices for larger-than-RAM applications. We're working on updating the package vignette, and a

Version 2.2.0 of package bcp is now available. It replaces the suggests of NetWorkSpaces (previously used for optional parallel MCMC) with the dependency on package foreach, giving greater flexibility and supporting a wider range of parallel backends (see doSNOW, doMC, etc...). For those unfamiliar with foreach (thanks to Steve Weston for this contribution), it's a beautiful and highly

Version 2.2.0 of package bcp is now available.? It replaces the suggests of NetWorkSpaces (previously used for optional parallel MCMC) with the dependency on package foreach, giving greater flexibility and supporting a wider range of parallel backends (see doSNOW, doMC, etc...). For those unfamiliar with foreach (thanks to Steve Weston for this contribution), it's a beautiful and highly

The long-promised revision to bigmemory has arrived, with package 4.2.3 now on CRAN. The mutexes (locks) have been extracted and will be available through package synchronicity (on R-Forge, soon to appear on CRAN). Initial versions of packages biganalytics and bigtabulate are on CRAN, and new versions which resolve the warnings and have streamlined CRAN-friendly configurations will appear

Package "bigmemory" is now available on CRAN. A brief abstract follows: Multi-gigabyte data sets challenge and frustrate R users even on well-equipped hardware. C/C++ and Fortran programming can be helpful, but is cumbersome for interactive data analysis and lacks the flexibility and power of R's rich statistical programming environment. The new package bigmemory bridges this gap,

The re-engineered bigmemory package is now available (Version 3.5 and above) on CRAN. We strongly recommend you cease using the older versions at this point. bigmemory now offers completely platform-independent support for the big.matrix class in shared memory and, optionally, as filebacked matrices for larger-than-RAM applications. We're working on updating the package vignette, and a

Version 2.2.0 of package bcp is now available. It replaces the suggests of NetWorkSpaces (previously used for optional parallel MCMC) with the dependency on package foreach, giving greater flexibility and supporting a wider range of parallel backends (see doSNOW, doMC, etc...). For those unfamiliar with foreach (thanks to Steve Weston for this contribution), it's a beautiful and highly

Version 2.2.0 of package bcp is now available.? It replaces the suggests of NetWorkSpaces (previously used for optional parallel MCMC) with the dependency on package foreach, giving greater flexibility and supporting a wider range of parallel backends (see doSNOW, doMC, etc...). For those unfamiliar with foreach (thanks to Steve Weston for this contribution), it's a beautiful and highly

The long-promised revision to bigmemory has arrived, with package 4.2.3 now on CRAN. The mutexes (locks) have been extracted and will be available through package synchronicity (on R-Forge, soon to appear on CRAN). Initial versions of packages biganalytics and bigtabulate are on CRAN, and new versions which resolve the warnings and have streamlined CRAN-friendly configurations will appear

I can't speak for ff and filehash, but bigmemory's data structure doesn't allow "clever" merges (for actually good reasons). However, it is still probably less painful (and faster) than other options, though we don't implement it: we leave it to the user because details may vary depending on the example and the code is trivial. - Allocate an empty new filebacked

At one point we might have gotten something working (older version?) on Solaris x86, but were never successful on Solaris sparc that I remember -- it isn't a platform we can test and support. We believe there are problems with BOOST library compatibilities. We'll try (again) to clear up the other warnings in the logs, though. !-) We should also revisit the possibility of a CRAN BOOST

John, thanks for starting (or restarting) this thread.? I'd like to add to the discussion with another concrete example, about as simple as it gets, which may help focus at least part of this discussion. I have worked with Taylor Arnold to implement a method developed in Conover (1972) for Kolmogorov-Smirnov goodness-of-fit tests for discrete distributions (one-sample only).? We needed this

I apologize in advance for probably missing something obvious, but if someone could point me in the right direction I'd be grateful. This NOTE is not unique to our package (I list a few others, below). Package bcp has several Suggests (strucchange, for example). Then in an Rd file, we have if (require(strucchange)) { # Doing some examples making use of strucchange } The CRAN check