similar to: clustering algorithm detail

Hi All, i've problem to understand how to work with R to generate a hierarchical clustering my data are in a csv and looks like : idcode,count,temp,sal,depth_m,subs 16001,136,4.308,32.828,63.46,47 16001,109,4.31,32.829,63.09,49 16001,107,4.302,32.822,62.54,47 16001,87,4.318,32.834,62.54,48 16002,82,4.312,32.832,63.28,49 16002,77,4.325,32.828,65.65,46 16002,77,4.302,32.821,62.36,47

Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,] 

Dear R-list members, Just wondering if there is any way to compute the duplication matrix in R. I tried to search for it but only found functions "xpnd" and "vech". Basically for a symmetric n by n matrix A, the duplication matrix D_n is a matrix of dimension n^2 by n(n+1)/2 such that D_n vech(A)= c(A), where c(A) just vectorizes A. The duplication matrix is defined on page

Let say i have a square matrix and applied the 'vech' operator to stack the lower triangular elements into a vector: > Mat <- matrix(1:25, 5) > Mat ? ? ?[,1] [,2] [,3] [,4] [,5] [1,] ? ?1 ? ?6 ? 11 ? 16 ? 21 [2,] ? ?2 ? ?7 ? 12 ? 17 ? 22 [3,] ? ?3 ? ?8 ? 13 ? 18 ? 23 [4,] ? ?4 ? ?9 ? 14 ? 19 ? 24 [5,] ? ?5 ? 10 ? 15 ? 20 ? 25 > Mat[lower.tri(Mat)] ?[1] ?2 ?3 ?4 ?5 ?8 ?9 10 14

I'm writing R code to calculate Hierarchical Social Entropy, a diversity index that Tucker Balch proposed. One article on this was published in Autonomous Robots in 2000. You can find that and others through his web page at Georgia Tech. http://www.cc.gatech.edu/~tucker/index2.html While I work on this, I realize (again) that I'm a C programmer masquerading in R, and its really

Dear R Users,i am very new to R. I want your help on an issue regarding distance matrix and cluster analysis i had discharge data of 4 rivers(a,b,c,d) in 4 vectors each having 364 values > dput(qmu)structure(list(a = c(0.26, 0.25, 0.25, 0.25, 0.24, 0.23, 0.22, 0.21, 0.21, 0.21, 0.2, 0.19, 0.19, 0.19, 0.19, 0.18, 0.18, 0.18, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17, 0.17,

Dear All, I have a dissimilarity matrix which I happily convert to a distance object by running: X <- as.dist(Y) and I can happily now run either hclust(X) or agnes(X). So that the various bits of output are labelled correctly I would dearly like to be able to give names to the columns and rows of X, as would happen if I ran: mydata<-read.table("clipboard",header=T)

Can anyone please tell whether there is any R function to act as "VEC" and "VECH" operator on Matrix? Yes of course, I can write a user-defined-function for that or else, I can put dim(mat) <- NULL. However I am looking for some R function. Your help will be highly appreciated. Regards, -- View this message in context:

Hi All, I need to parse data from a file, example shown below. The first two lines can be skipped, the third line contains the column names. The next 13 lines can be skipped. The next line "1991" is a year value, with the following 13 values data for that year. The file then repeats this format with (year, 13 lines of data for that year). I would ideally like to end up with an

Dear list, First my information: platform i386-pc-linux-gnu arch i386 os linux-gnu system i386, linux-gnu status major 2 minor 3.1 year 2006 month 06 day 01 svn rev 38247 language R version.string Version 2.3.1 (2006-06-01) Now my question: How is it possible that a command in an R script is not

I'm trying to perform a mantel test that ignores specific pairs in my distance matrices. The reasoning is that some geographic distances below a certain threshold suffer from spatial autocorrelation, or perhaps ecological relationships become less relevant that stochastic processes above a certain threshold. The problem is that I can't find a way to do it. If I replace values in either or

I have a problem with R. I try to compute the confidence interval for my df. When I want to create the plot I have this problem: Error in xy.coords(x, y, xlabel, ylabel, log) : 'x' and 'y' lengths differ. I try this code: library(dplR) df.rwi <- detrend(rwl = df, method = "Spline",nyrs=NULL) write.table(df.rwi,file="rwi.txt",quote=FALSE,row.names=TRUE)

I have written a wrapper for heatmap.2 called heatmap.w.row.and.col.clust which auto-generates breaks using breaks<-round((c(seq(from=(-20 * stddev), to=(20 * stddev))))/20, digits = 2) #(stddev in this case = 2.5) This has always worked well in the past but now I am getting an error that non-finite breaks are being generated. Drilling down, it seems that my wrapper is generating finite

Hello, Using the following lines of code, I created the following graph:

Hello, Using the following lines of code, I created the following graph:

Hello there, I am not quite sure how to interpret the output of Rprof (in the following the output I was staring at). I was poking around the web a little bit for documentation but without much success. I guess if I want to figure out what takes so long in my code the 2nd table $by.total and the total.pct column (pct = percent) is the most helpful. What does it mean that [ or [.data.frame is

Hi everyone - I know that R is capable of clustering using the k-means algorithm, but can R do k-means++ clustering as well? Thanks, -- Dr. Ferebee Tunno Assistant Professor Department of Mathematics and Statistics Arkansas State University P.O. Box 70 State University, AR. 72467 ftunno@astate.edu (870) 329-7710 [[alternative HTML version deleted]]

Dear help community, I'm a R-beginner and use it for my master thesis. I've got a mixed model and want to analyse it with lme. There are a lot Cofactors that coult be relevant. To extract the important ones I want to do the stepAIC, but always get an error warning. Structure of my data: data.frame': 72 obs. of 54 variables: $ Block : Factor w/ 3 levels

I'm working with capscale and permutest for the first time, and having trouble getting statistical analyses for more than one constraining variable. I've read the documentation, but setting first=FALSE or using by="axis" doesn't seem to be helping. capscale seems to be fine, I receive output for more than one constrained axis. What am I doing wrong?

Hi all, I have a dataframe of rownames that I would like to extract from a larger matrix to form a new matrix. I have tried to use subset, in this manner x<-subset(largematrix, rownames$names=largematrix$rownames) where largematrix is the larger matrix and rownames$names is the dataframe with the row names of the rows I want to extract from the larger matrix. Of course, I get error