similar to: Coda: On the efficiency of unsplit() for Rolf Turner's recent post

Dear R-bug-people I have encountered a problem with "unsplit", which I believe may be caused by a bug in the function. However, unexpericend with bug-reports I apologise if this is barely a user problem rather than a problem within R. The problem occurs if an object is split by several grouping factors with levels not occuring in the data, and using drop = TRUE. This may appear as

> On Nov 21, 2020, at 10:55 AM, Mario Annau <mario.annau at gmail.com> wrote: > > Hello, > > using the `unsplit()` function with tibbles currently leads to the > following error: > >> mtcars_tb <- as_tibble(mtcars, rownames = NULL) >> s <- split(mtcars_tb, mtcars_tb$gear) >> unsplit(s, mtcars_tb$gear) > Error: Must subset rows with a valid

Cool - thank you Peter! @Marc: This is really not a tidyverse vs base-R debate and I personally think that they should both work together for most parts. The common environment is still R. But just to give you the full picture I also filed a bug for tibbles (https://github.com/tidyverse/tibble/issues/829). With these two fixes I think that split/unsplit would work for tibbles and users (like me)

Hello everyone, I use the split function splitting with the f function on a 3 columns and more than 100 000 rows data frame. Once it's split I have a list of data frames still with 3 columns and n rows. I manipulate those list elements and get a list of data frames still with 3 columns but less rows. So when I unsplit it, I get an error as I use the same factor function I used to split ( f in

Hedderik van Rijn <hedderik@cmu.edu> writes: > If the second argument to unsplit is not a simple vector (but a "list > containing multiple lists"), the function seems to have some problems. > > Given a slight modification of the examples in help(split): > > > xg <- split(x,list(g1=g,g2=g)) > > unsplit(xg,list(g1=g,g2=g)) > [1] -0.7877109

Hedderik van Rijn <hedderik@cmu.edu> writes: > > Thanks for pointing this out. I think your code should work, although > > I'm slightly uneasy about actually modifying f, so how about > > Just curious, why are you uneasy about that? Does it have side effects? It is largely due superstition, but if you change f before calling split<-() you have split() and

R-devel, Below is a simple example calling split and unsplit on a numeric vector of length 2 where 'f' is c(1,NA). > unsplit(split(c(1,2), c(1,NA)), c(1,NA)) [1] 1 0 I noticed that the call to vector in unsplit gives us 0 as the 2nd element of the result. Is this the intended result, as opposed to NA? Thanks for your help, Jeff -- Jeff Enos Kane Capital Management jeff at

Perhaps this is the intended behavior, but I discovered that unsplit throws an error when it tries to set rownames of a variable that has no dimension. This occurs when unsplit is passed a list of data.frames that have only a single column. An example: df <- data.frame(letters[seq(25)]) fac <- rep(seq(5), 5) unsplit(split(df, fac), fac) For reference, I'm using R version 2.9.0

Hi everyone, I have already used split() and unsplit() in data frames without problems, but now I’m applying these functions to other data and when using unsplit() I have received the following message: Error in `row.names<-.data.frame`(`*tmp*`, value = c("1", "2", "3", "4", : duplicate ''row.names'' are not allowed In

In data OME in MASS I would like to extract the first 5 observations per subject (=ID). So I do library(MASS) OMEsub <- split(OME, OME$ID) OMEsub <- lapply(OMEsub,function(x)x[1:5,]) unsplit(OMEsub, OME$ID) - which results in [[1]] [1] 1 1 1 1 1 [[2]] [1] 30 30 30 30 30 [[3]] [1] low low low low low Levels: N/A high low [[4]] [1] 35 35 40 40 45 [[5]] [1] coherent incoherent coherent

Hello, using the `unsplit()` function with tibbles currently leads to the following error: > mtcars_tb <- as_tibble(mtcars, rownames = NULL) > s <- split(mtcars_tb, mtcars_tb$gear) > unsplit(s, mtcars_tb$gear) Error: Must subset rows with a valid subscript vector. ? Logical subscripts must match the size of the indexed input. x Input has size 15 but subscript `rep(NA, len)` has

I get the sentiment, but this is really just bad coding (on my own part, I suspect), so we might as well just fix it... -pd > On 21 Nov 2020, at 17:42 , Marc Schwartz via R-devel <r-devel at r-project.org> wrote: > > >> On Nov 21, 2020, at 10:55 AM, Mario Annau <mario.annau at gmail.com> wrote: >> >> Hello, >> >> using the `unsplit()`

Took me a while but I figured out how to put in common values of group means/counts, etc. to do the same thing as egen. lapply with split and then unsplit. Thomas Davidoff Assistant Professor Haas School of Business UC Berkeley Berkeley, CA 94720 phone: (510) 643-1425 fax: (510) 643-7357 davidoff@haas.berkeley.edu http://faculty.haas.berkeley.edu/davidoff [[alternative HTML

I will be out of the office starting 04/06/2007 and will not return until 04/24/2007. I will respond to your message when I return.

Hi Friends.. Well, I have setting OpenLDAP on CentOS 4.4 on a Blade Server.. I am trying of setting a cluster.. But I don't know that use: NFS or CODA.. What is you opinion? -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.centos.org/pipermail/centos/attachments/20070206/62c12ffa/attachment.html>

Hi all: The CODA package provides Geweke Diagnostic method for convergence checking. The geweke.diag in CODA returns Z-score value but not give a conclustion that it is convergence or not. So I'd like to know how small/big the magnitude of Z-score is corresponding to the convergence of a chain. That is, Doese Z-score smaller or more than *threshold *determine the convergence? If so, how big

Hi R users, Does anybody knows for the following erro after running geweke.diag(MCMC.sampled, frac1=0.1, frac2=0.5) Erro em glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, : la??o interno 1; n??o ?? poss??vel corrigir o tamanho do passo Al??m disso: Warning messages: 1: algoritmo n??o convergiu in: glm.fit(x = X, y = Y, weights = weights, start = start,

I have just uploaded the R port of the CODA (Convergence Diagnostics and Output Analysis) package to CRAN. This is a suite of functions for analyzing the output of Markov Chain Monte Carlo simulations and diagnosing lack of convergence of the simulated output. It is designed to work with (but is not necessarily limited to) the BUGS program: http://www.mrc-bsu.cam.ac.uk/bugs/ I have made

I have just uploaded the R port of the CODA (Convergence Diagnostics and Output Analysis) package to CRAN. This is a suite of functions for analyzing the output of Markov Chain Monte Carlo simulations and diagnosing lack of convergence of the simulated output. It is designed to work with (but is not necessarily limited to) the BUGS program: http://www.mrc-bsu.cam.ac.uk/bugs/ I have made

Dear all, I just wrote a script to have each of my three mcmc chins running on a different computer core to improve computation speed. To do it I use the function sfLapply from the package snowfall. Before using parallell computation, I did my diagnostic analyses just after the model updating, but now I have to save the coda before. Using the CODA package, it is possible to load the outputs in a