similar to: dist equivalent

Dear all, I have come across a very confusing matter regarding dist() supplied by the amap package: --- m is just a test matrix > library(amap) Loading required package: mva Warning message: package 'mva' has been merged into 'stats' > m a b c aa 0.1 0.2 0.3 bb 2.0 3.0 4.0 cc 2.0 4.0 6.0 dd 0.3 0.2 0.1 > ds<-dist(m,method="pearson") >

Hello all, I wanted to announce a new R search engine I made that covers all the major R mailing lists, CRAN, r-project.org, and more. Results are tabbed, so you can refine the search. Current refinements include searching just the mailing lists, searching just introductions, and searching the web for source files ending in .R. Please send comments and suggestions. If you want to add sites to

Hi All, could someone shed some light on what the difference between the estimated dispersion parameter that is supplied with the GLM function and the one that the 'gamma.dispersion( )' function in the MASS library gives? And is there consensus for which estimated value to use? It seems that the dispersion parameter that comes with the summary command for a GLM with a Gamma dist. is

Newsgroup members, I appreciate the help on this topic. David Duffy provided a solution (below) that was quite helpful, and came close to what I needed. It did a great job creating two vectors of dichotomous variables with a known correlation (what I referred to as a phi-coefficient). My situation is a bit more complicated and I'm not sure it is easily solved. The problem is that I must

Hello all, I would like to compare the similarity of two cluster solutions using a validation criteria (such as Hubert's gamma coefficient, the Dunn index the corrected rand index and so on) I see (from here:http://www.statmethods.net/advstats/cluster.html) that the function cluster.stats() in the fpc package provides a mechanism for comparing 2 cluster solutions - *BUT* - it requires me to

Any reference to the appropriate documentation would be most appreciated. I am using the TSP module for clustering of HIV genetic sequences. The distances have already been computed and available as either upper-triangular or square, i.e.: a 1 2 3 b 4 5 c 6 d or a 0 1 2 3 b 1 0 4 5 c 2 4 0 6 d 3 5 6 0 The TSP modules takes in a "dist" object.

Dear R users, I am trying to use MCD and MVE methods in the analysis of functional imaging (fMRI) data. But, before doing that, I want to understand the sampling distribution of the correlation parameter given by MCD and MVE (cov.mcd$cor, cov.mve$cor). To this end, I conducted a simulation where in each of 100000 epochs, I a. construct a matrix from two vectors, each containing 40 numbers

Dear R users, I am trying to use MCD and MVE methods in the analysis of functional imaging (fMRI) data. But, before doing that, I want to understand the sampling distribution of the correlation parameter given by MCD and MVE (cov.mcd$cor, cov.mve$cor). To this end, I conducted a simulation where in each of 100000 epochs, I a. construct a matrix from two vectors, each containing 40 numbers

Hello list (happy new yeaR), Here's a copy of a message i just send to Christian Hennig (who wrote the fpc package). That may interrest some of you, and maybe someone could have a better solution than mine. Romain. ------------------------------------------------------------------------------------------ Mister Hennig, [[[ I'm writing in english because i don't know german

Hi list! I'm computing multivar. distances from a set of centroids to a (large) set of individuals. I'm now just using rbind to create a matrix (x) with the centroid and the individuals, then run as.matrix(dist(x)) and finally select the appropriate columns, as I'm not interested on the distances among individuals. Therefore, this procedure implies a waste of computing time. Is there

Muchas gracias Eva. Manuel 2013/7/5 Eva Prieto Castro <evapcastro@yahoo.es> > Hola, Manuel: > > Si descomprimes el tar.gz indicado por Marcuzzi, y accedes a la subcarpeta > R, en el archivo legends.R buscas la cadena p-value, y en el archivo > strucplot.R es donde creo que modificas el "Pearson residuals" (busca por > "Pearson\nresiduals"). > >

I am making heatmaps for a dataset (~ 300*600 matrix) with the following R script (I am not familiar with R and this is the first time I am using it). library("gplots") library("Cairo") mydata <- read.csv(file="data.csv", header=TRUE, sep=",") rownames(mydata)=mydata$Name mydata <- mydata[,2:297] mydatamatrix <- data.matrix(mydata) mydatascale

Lets say I have an * at my business, with 7960 SIP phones. All the sip phones are registered using their extension number (like 305), but I would also like to put my SIP URI on my business card and in a name format, not an extension number (like lee.goodman), so that the SIP URI would read lee.goodman@asterisk.company.com. How would I set this up in extensions.conf? I got

Hi Stefan, Thank you very much for pointing me to the wordspace package. It does the job a bit faster than my C code but is 100 times more convenient. By the way, since the tcrossprod function in the Matrix package is so fast, the Euclidean distance can be computed very fast: euc_dist <- function(m) {mtm <- Matrix::tcrossprod(m); sq <- rowSums(m*m);? sqrt(outer(sq,sq,"+") -

Hi all, I am a novice user of the heatmap.2 function in the Bioconductor gplots package. I am trying to create a heatmap diagram from a hierarchical clustering result based on the complete linkage and the pearson correlations as the distance metrics. After reading the help page for heatmap.2, it seems that it calls the distfun and hclustfun with the default settings. Is that the same as doing

> From: Siegfried.Macho > > Dear R-helpers, > > 3 questions: > 1. Is there a package that contains a routine for computing > Kendall's W > (coefficient of concordance), with and without ties ? Is that the same as Kendall's tau, as in cor(..., method="kendall")? > 2. Is there a package that contains a routine for computing > Goodman' s

Hello, everybody, I hope somebody could help me with a dist() function. I have a data frame of size 2*4087 (col*row), where col corresponds to the treatment and rows are species, values are Hellinger distances, I should reconstruct a distance matrix with a dist() function. I know that "euclidean" method should be used. When I type: dist(dframe,"euclidean") it gives me a

Dear colleagues in R, Has anybody implemented the 1) (Goodman & Kruskal) lambda 2) (Thiel's) uncertainty coefficient Tanks Rafael M Ramos [[alternative HTML version deleted]]

Hello and sorry that I still haven?t found a solution for my problem. I need to extract the lower and upper triangle from a square matrix including the diagonal. This diagonal is not zero in that special case. I tried with as.dist w<-as.dist(w, diag = TRUE) > w 1 2 3 4 5 1 0 2 2 0 3 3 8 0 4 4 9 14 0 5 5 10 15 20 0 but found no way to keep the diagonal that is in the

James Pearson wrote: > > I'm attempting to port an existing xinetd service from CentOS 6 to > something equivalent with systemd for CentOS 7 > > The existing xinetd config uses the attributes 'only_from' to limit > connections from a limited set of remote hosts and 'banner_fail' to > print a suitable error message when access is denied > > However,