search for: nstarting

After a year my R programming style is still very "C like". I am still writing a lot of "for loops" and finding it difficult to recognize where, in place of loops, I could just do the same with one line of code, using "sapply", "lapply", or the like. On-line examples for such high level function do not help me. Even if, sooner or later, I am getting my R

Hello, the k-means {stats} help and the Hartigan&Won paper say nothing about the way random sets works (parameter nstart). I would expect to get the different results for each random initial set but I always obtain only one result: how is it selected? Charles Raux

Hello, here is a working reproducible example which crashes R using kmeans or gives empty clusters using the nstart option with R 15.2. library(cluster) kmeans(ruspini,4) kmeans(ruspini,4,nstart=2) kmeans(ruspini,4,nstart=4) kmeans(ruspini,4,nstart=10) ?kmeans either we got empty always clusters and or, after some further commands an segfault. regards, Detlef Groth ------------ [R] Empty

I''ve created a simple script (below) which calls the effect.appear script in order to make a group of items appear at the page load. I would like to have the images randomly appear at different times; e.g. the 3rd image might start appearing 2 seconds after load, the 6th image immediately after load, the first image 1 second after load, etc... How can I achieve this affect? ---

Hi. I am trying to modify kmeans function. It seems that is failing something obvious with the workspace. I am a newbie and here is my code: myk = function (x, centers, iter.max = 10, nstart = 1, algorithm = c("Hartigan-Wong", + "Lloyd", "Forgy", "MacQueen")) + { + do_one <- function(nmeth) { + Z <- switch(nmeth, { + Z

Hello, I am an R newbie, trying to use lsoda to solve standard Lotka-Volterra competition equations. My question is: how do I pass a parameter that varies with time, like say, phix <- 0.7 + runif(tmax) in the example below. # defining function lotvol <- function(t,n,p){ x <- n[1]; y <- n[2] rx <- p["rx"]; ry <- p["ry"] Kx <-

aggregate.ts does not behave in the same way as the equivalent method aggregate.rts in S-PLUS. In particular it - changes the start of the time series - tends to have a length which is 1 shorter For example: R> x <- ts(1:10) R> aggregate(x, nfreq=0.5, FUN=min) Time Series: Start = 2 End = 8 Frequency = 0.5 [1] 2 4 6 8 S> x <- rts(1:10) S> aggregate(x, nf=0.5, fun = min) [1]

Hi. It seems that nearly every time I try to use factanal I get the following response: >faa2db1<-factanal(mretdb1,factors=2,method="mle",control=list(nstart=25)) Error in factanal(mretdb1, factors = 2, method = "mle", control = list(nstart = 25)) : unable to optimize from these starting value(s) > In the case cited above, mretdb1 is synthetic data created

From: Maros Zatko <mzatko@redhat.com> --- fish/rl.c | 158 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ fish/rl.h | 32 +++++++++++++ 2 files changed, 190 insertions(+) create mode 100644 fish/rl.c create mode 100644 fish/rl.h diff --git a/fish/rl.c b/fish/rl.c new file mode 100644 index 0000000..bb8fd62 --- /dev/null +++ b/fish/rl.c @@ -0,0 +1,158 @@ +/* guestfish -

From: Maros Zatko <hacxman@gmail.com> Auxiliary functions for readline to support space character escaping in filenames in future. Escaping function is taken from fish.c (used to be parse_quoted_string) plus its un-escaping counterpart. There are a few tests for both. Maros Zatko (3): fish: rl.{c,h} - escaping functions for readline fish: basic tests for readline escaping autotools:

Dear List, I'm trying to get diff/removed data and it's offset out. So I write a functions in receive_data. When I run backup, I found there is a weird behavior which I don't understand. i = recv_token(f_in, &data) will receive (i = -1, offset2 = 0) some where in the middle of the transfer procedure. That's to say, it's going to transfer the first data block from sender,

Dear experts, I am encountering a version-dependent issue. My laptop runs Ubuntu 12.04 LTS 64-bit, R 2.14.1; the issue explained below never occurred with this version of R My desktop runs Ubuntu 11.10 64-bit, R 2.13.2; what follows applies to this setup. The data I'm clustering is constituted by the rows of a 320 x 6 matrix containing integers ranging from 1 to 7, no missing data. I applied

On 08/25/2013 05:02 PM, Olly Betts wrote: > So the simple fix is > probably just to install the perl-Search-Xapian RPM instead. Thanks, the Centos 6 repos don't have that rpm and the http://xapian.org/download page seems to only cover the XS bindings, if I am reading this correctly: But I was able to remove the rpm packages and compile and install the core and swig from source.

Dear members. I am having problems to understand the kmeans- results in R. I am applying kmeans-algorithms to my big data file, and it is producing the results of the clusters. Q1) Does anybody knows how to find out in which cluster (I have fixed numberofclusters = 5 ) which data have been used? COMMAND (kmeans.results <- kmeans(mydata,centers =5, iter.max= 1000, nstart =10000)) Q2) When I

Using R 2.1.0 on Windows 2 questions: 1. Is there a way to parse the output from kmeans within R? 2. If the answer to 1. is convoluted or impossible, how do you save the output from kmeans in a plain text file for further processing outside R? Example: > ktx<-kmeans(x,12, nstart = 200) I would like to parse ktx within R to extract cluster sizes, sum-of-squares values, etc., OR save ktx in

I'm using bigkmeans in 'biganalytics' to cluster my 60,000 by 600,000 matrix. I'm using a 8 core Linux VM. I have register parallel backend with >registerDoMC() And I checked how many cores registered with >getDoParWorkers() It returns 8, which is the number of cores I have on my machine. And I run the test below, whose results shows improved speed due to parallel. check

Hi, all, I have a really big matrix that I want to run k-means on. I tried: >data <- read.big.memory('mydata.csv',type='double',backingfile='mydata.bin',descriptorfile='mydata.desc') I'm using doMC to register multicore. >library(doMC) >registerDoMC(cores=8) >ans<-bigkmeans(data,k) In system monitor, it seems only one thread running R. Is

> tab <- array( sample(3^5), rep(3,5) ) > loglin( tab, list(1,2,3,4,5) )[[1]] # AOK 2 iterations: deviation 5.456968e-12 [1] 10909.89 > loglin( tab, list(1,2,3,4,5), c(1,2,3) )[[1]] # OUCH! Process R bus error at Wed Jul 21 17:03:55 2004 this is inconsistent - sometimes issuing this msg several times before barfing: Error in switch(z$ifault, stop("This should not

Hi, I am running some rather complex mixtures of beta regressions using the betamix() command from the betareg package (V. 3.0-4). If I am doing exploratory regressions with only one random starting value (nstart=1) I obtain results which converge after about 100 iterations. However, if I run regressions with nstart=100 random starting values I obtain solutions amended with a warning that no

At least it isn't working the way it used to. Code: $db = Search::Xapian::Database->new( $dx ); my $qp = Search::Xapian::QueryParser->new(); my $dbSize=$db->get_doccount(); # $qp->set_stemmer(new Search::Xapian::Stem("english")); # $qp->set_stemming_strategy(STEM_SOME); # $qp->set_default_op($defaultop); my $par =