similar to: unexpected results with higher-order functions and lapply

I could not find any documentation of how dot-dot-dot works when used as an argument in a function call (rather than as a formal argument in a definition). I would appreciate some references to the rules governing situations like: f1<-function(x,y,...){ ? print(x) } f2<-function(...){ ? f1(...) } f2(1,2,3) In the call above how are the three numbers bound to the individual formal

Hello, Apologies in advance if this is a stupid question. I am running R on Ubuntu 9. R version 2.9.2 (2009-08-24) I am trying to work with plm. I think the library is installed, as I can do > library(plm) Loading required package: kinship Loading required package: survival Loading required package: splines Loading required package: nlme Loading required package: lattice [1] "kinship

Hi, Does Icecast has RTP support for streaming OGG/Vorbis and OGG/theora media files? If so, then please give me some pointers on how to configure my Icecast server to listen to RTP requests. If it's not available, then is the inclusion of this feature, in plans for future release of the product? Thanks -- Subhabrata Bhattacharya

The page is either too busy, or there is something seriously wrong with access to this page. Most of the time, trying to reach www.bioconductor.org results in failure. Only once in a blue moon, do I get through. In fact, thus far, I have not been able to install bioconductor, since the first source(...) command from the R command window -- following instruction on www.bioconductor.org page,

Dear Sir/Madam, I'm getting a problem with a R-code which converts a data frame to a matrix. It first generate a (m^(n-m) * m) matrix A and then regenerate another matrix B having less dimension than A which satisfy some condition. Now I wish to assign each row of B to a vector as individual. My problem is when I set any choice of (n,m) except m=1 it works fine but setting m=1 I got the

Dear R-listers, I am giving part of my R code : ########################################################### n=15 m=1 library("partitions") library("gregmisc") library("combinat") x = t(restrictedparts(n-m,m)) l = length(x[,1]) for(u in 1:l){ A= unique(matrix( unlist(permn(x[u,])), ncol=m, byrow=TRUE )) }

Hi, What is the actual functionality of ezstream when I use it with Icecast?What does it exactly do? Why isn't Icecast written to read a file directly and stream it? -- ~$ubh

Dear R Helpers! Is the Higher Order Correlation function already programmed in R I couldn't find it? Much appreciated, Sergei Petrov

Would it be appropriate to add a Which higher-order function? Which <- function(f, x) { ind <- vapply(x, f, logical(1)) !is.na(ind) & ind } Then Filter would be: Filter <- function(f, x) x[Which(f, x)] I think the use of vapply is slightly better than the current as.logical(sapply(x, f)) because it will return a more informative error message if the predicate doesn't

Is there an easy way to turn a vector of length n into an n by n matrix, in which the diagonal equals the vector, the first off diagonal equals the first order differences, the second... etc. I.e. to do this more efficiently: diffmatrix <- function(x){ n <- length(x); M <- diag(x); for(i in 1:(n-1)){ differences <- diff(x, dif=i); for(j in 1:length(differences)){ M[j,i+j]

Dear all, Consider the model below: > x <- lm(mpg ~ cyl * disp * hp * drat, mtcars) > summary(x) Call: lm(formula = mpg ~ cyl * disp * hp * drat, data = mtcars) Residuals: Min 1Q Median 3Q Max -3.5725 -0.6603 0.0108 1.1017 2.6956 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.070e+03 3.856e+02 2.776 0.01350 * cyl

Hi, I have a naive question with the xen BVT scheduler. Supposing I have only one guest domain runnning on domain 0 and with the xm bvt command I have allocated cpu units in the ratio 9:1 to domain 0 and domain 1(say) respectively. Now I create another guest domain. How much CPU share it (or subsequent domains created after it) will get? ~$ubh _______________________________________________

I think it is not a bug. It is a general property of interactions. This property is best observed if all variables are factors (qualitative). For example, you have three variables (factors). You ask for as many interactions as possible, except an interaction term between two particular variables. When this interaction is not a constant, it is different for different values of the remaining

Dear All, I am facing a problem in calling an user defined R function from Java through JRI. The user defined R function does a loess normalization on micro array data ( find in the limma package of BioConductor) and the last 2 lines of the R Code is : MA <- normalizeWithinArrays(RG, method="loess") return(MA$A) # where MA$A is the column A of MA. Now while calling it in Java, I

Hi, I am facing the following problems while installing xen from its source.Ihave compiled linux-2.6.11.10 with the option ARCH=xen.I had been fiddling with the various configuration to boot up the kernel but its not working anyway. When I compiled the kernel with the following option as built-in [*] Root file system on NFS I get the following error: Root-NFS: No NFS server available, giving

On 6/7/05, Subhabrata Bhattacharya <subh.arya@gmail.com> wrote: > Hi, > > Does Icecast has RTP support for streaming OGG/Vorbis and OGG/theora > media files? Not in the current version. It may be added in the relatively distant future, but there's nobody workingon it at the moment. Mike

Dear All, (this msg is a statistics/computing question to the list) I'm trying to implement a modern-version of a (classic) "several-step protocol" in Fishery Biology (due to Bhattacharya, 1967): analysis of length-frequency distribution of fish larvae to id cohorts and later estimate growh rates! I've trouble with the 1st step: using kernel density estimation (KDE) function

Dear R users, The first version of the package ?horseshoe' is now available on CRAN. It contains various functions for implementing the horseshoe prior for sparse linear regression, and faster versions for the special case of the normal means problem. The functions output, among other things, the horseshoe estimator (posterior mean), credible intervals, and there is a function to perform

Dear R users, The first version of the package ?horseshoe' is now available on CRAN. It contains various functions for implementing the horseshoe prior for sparse linear regression, and faster versions for the special case of the normal means problem. The functions output, among other things, the horseshoe estimator (posterior mean), credible intervals, and there is a function to perform

Dear Sir/madam, I'm getting a problem with a R-code which calculate Fisher Information Matrix for Hybrid Censored Weibull Distribution. My problem is that: when I take weibull(scale=1,shape=2) { i.e shape>1} I got my desired result but when I take weibull(scale=1,shape=0.5) { i.e shape<1} it gives error : Error in integrate(int2, lower = 0, upper = t) : the integral is probably