similar to: Error: unexpected '<' in "<" when modifying existing functions

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

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

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

> 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 Team, I am trying to run kmeans in R, and I need to save the different clusters into different folders. How can I achieve this? # this is how my data looks. $ *cat 1.tsv | head* userid bookid rating bookTotalRatings bookAvgRating userTotalRatings userAvgRating 1 100 0 24 2.7916666666666665 291 2.6735395189003435 2 200 7 24 2.9583333333333335 6 7.0

Hello All, I'm having some trouble figuring out what the clearest way to plot my k-means clustering result on an my existing MDS. First I performed MDS on my distance matrix (note: I performed k-means on the MDS coordinates because applying a euclidean distance measure to my raw data would have been inappropriate) canto.MDS<-cmdscale(canto) I then figured out what would be my optimum

hi r-help, i am doing kmeans clustering in stats. i tried for five clusters clustering using: kcl1 <- kmeans(as1[,c("contlife","somlife","agglife","sexlife",                         "rellife","hordlife","doutlife","symtlife","washlife",                       

Hello all, I've been comparing results from kmeans() in R to PROC FASTCLUS in SAS and I'm getting drastically different results with a real life data set. Even with a simulated data set starting with the same seeds with very well seperated clusters the resulting cluster means are still different. I was hoping to look at the source code of kmeans(), but it's in C and FORTRAN and

Dear sir; may you drop me some idea how can i get rid of following error message: Error in switch(nmeth, { : NA/NaN/Inf in foreign function call (arg 1) i dont know what does nmeth and ther rest of error message mean? i have a file which contains 460 rows and 174 columns including missing value as NA. Regards, Mostafa

Greetings! I want to have the coefficients that R uses in shapiro.test() for the Shapiro-Wilk test for a prticular sample size, i.e. the a[i] in W = Sum(a[i]*x[i])/(Sum(x[i] - mean(x))^2) (where the x[i] are sorted). Two questions: Q1: Is there a readymade R function from which I can extract these? Q2: I was wondering if I might be able to modify the code for the function shapiro.test() so

I am running a k-means clustering code in R : mydata_kmeans5 <- kmeans(mydata, centers=5).. But the problem is that the data is having some "NA" in it. So R is showing me a message :Error in switch(nmeth, { : NA/NaN/Inf in foreign function call (arg 1) In addition: Warning messages: 1: In switch(nmeth, { : NAs introduced by coercion 2: In switch(nmeth, { : NAs introduced by

# Why does expressing one function require(ctest) t.test # return only function (x, ...) UseMethod("t.test") <environment: namespace:ctest> # but expressing another function shapiro.test # returns more complete code? function (x) { DNAME <- deparse(substitute(x)) x <- sort(x[complete.cases(x)]) n <- length(x) if (n < 3 || n > 5000)

Hello, I am hoping you can help me with a question concerning kmeans clustering in R. I am working with the following data-set (abbreviated): BMW Ford Infiniti Jeep Lexus Chrysler Mercedes Saab Porsche Volvo [1,] 6 8 2 8 4 5 4 4 7 7 [2,] 8 7 4 6 4 1 6 7 8 5 [3,] 8 2 4

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, I am trying to call a FORTRAN subroutine from R. The Fortran code is @: http://lib.stat.cmu.edu/apstat/206 It performs a bivariate isotonic regression on a rectangular grid (m X n) matrix. I used the g77 compiler and successfully created a dll file and it also loads successfully from R. But somehow the programs fails to run properly. (I do get the correct result when I compile the

Hi, This email reports bugs in acinclude.m4, src/library/tcltk/src/Makefile, and share/perl/Rd2contents.pl. It is based on R-patched.tgz from 07/27/2001 (R 1.3.0) and comes from trying to compile R on AIX 4.3. 1) acinclude.m4: A string on line 3096 starts with a single quote: ' but is terminated with a double quote: ". (This leads to the weird error message that configure can't

> library(Kendall) > Kendall(1:3,1:3) WARNING: Error exit, tauk2. IFAULT = 12 <<<<<< tau = 1, 2-sided pvalue =1 I believe Kendall tau is well-defined for this case and the reported value is correct; isn't it a bug to give a warning? (And if, e.g., the pvalue is not well-defined in this case, wouldn't it be better to return NA or NaN or something?) Also,

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]

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 -

Hello, I am running cluster analysis, and am attempting to create a graph of my clusters. I keep on getting an error that says that my figure margins are too large. d <- file.choose() d <- read.csv(d,header=TRUE) mydataS <- scale(d, center = TRUE, scale=TRUE) #Converts mydataS from a matrix to a data frame mydataS2 <- as.data.frame(mydataS) #removes "coden"