similar to: hierarhical cluster analysis of groups of vectors

Hi Rafael, What about multivariate logistic regression? ----- Forwarded Message ---- From: Rafael Duarte <rduarte@ipimar.pt> To: Anders Malmendal <anders@chem.au.dk> Cc: r-help@stat.math.ethz.ch Sent: Tuesday, May 29, 2007 3:21:11 PM Subject: Re: [R] hierarhical cluster analysis of groups of vectors It seems that you have already groups defined. Discriminant analysis would probably

I would like to create an xyplot with varying y-axis limits and horizontal labels at the y-axis tickmarks. The following does not seem to work, although I think it should, going by the documentation for par. R version 2.5.1, Windows XP Prof. Thanks for a clue. Andreas Krause library(lattice) # axis labels for y-axis are horizontal xyplot(Sepal.Length ~ Sepal.Width | Species, data=iris) #

Dear list, I am new to lattice plots. I want to make a barchart with 10 and more levels. I need to use a grey scale for printing purposes. The problem is that with 10 or more levels in factors it is very difficult to distinguish each level in the plot and legend, since the greys are very similar (some levels have value of zero and don't appear). Here is an example of my problem: df <-

I am using R to make two-way ANOVA on a number of variables using g <- aov(var ~ fact1*fact2) where var is a matrix containing the variables. However the outcome seem to be dependent on the order of fact1 and fact2 (i.e. fact2*fact1) gives a slightly (factor of 1.5) different result. Any ideas why this is? Thanks for any help Anders

Dear all I have tried to estimate the confidence intervals for predicted values of a nonlinear model fitted with nls. The function predict gives the predicted values and the lower and upper limits of the prediction, when the class of the object is lm or glm. When the object is derived from nls, the function predict (or predict.nls) gives only the predicted values. The se.fit and interval aguments

Hi, I'm trying to cross compile R260 in a ubuntu 6.06 linux. I downloaded the Makefile for 251 and simply replaced the R version by 260. However I'm getting an error about mingw. ernesto at gandalf:~/ipimar/devel/R/ccompile260$ make R export

If you look at the structure, you'll see: > x$V4 <- 0 > str(x) `data.frame': 4 obs. of 4 variables: $ V1: int 1 2 3 4 $ V2: int 5 6 7 8 $ V3: int 9 10 11 12 $ V4: num 0 Don't know if this is the intended result. In any case, you're probably better off using data.matrix, as > data.matrix(x) V1 V2 V3 V4 1 1 5 9 0 2 2 6 10 0 3 3 7 11 0 4 4 8 12

Hi,   I need to generate a heatmap on a square matrix and wouldn't want to reorder the columns and the rows on the heatmap display.    I have used the options Rowv=NULL and Colv=NULL but doesn't seem to work. Following is a snippet of the heatmap function i am using. args <- commandArgs(); inputfile <- args[2] imgfile   <- args[3] bitmap(imgfile, height=15, width=15, res=100,

I have used heatmap to visualize my microarray data. I have a matrix of M-values. I do the following. #The distance between the columns. sampdist <- dist(t(matrix[,]), method="euclidean") sclus <- hclust(sampdist, method="average") #The distance between the rows. genedist <- dist(matrix[,], method="euclidean") gclus <- hclust(genedist,

Dear all, I need to make dendogram from read count in a csv file across 34 samples including biological replicate. Please share R code or package to do this. Do I also need to normalized read count before using read data? Thanks [[alternative HTML version deleted]]

Dear all, I am still a R apprentice... Apologies for the basic question. I am trying to reshape a dataframe based on the order of two variables (a character variable and a numerical variable). To simplify it, consider the following dataframe > df<-data.frame(id=c("b","b","a","a","a"),ord=c(2,1,1,3,2)) id ord 1 b 2 2 b 1 3 a 1 4

I am using R for hierarchical clustering of a number of documents. I have a distance matrix on which I have applied hclust method. When I plot the result of hclust method, I can see the dendogram plotted. What I need now is the dendogram stored as a tree in a data structure. My goal is to automatically label all internal nodes. For that, I need to know, which leaf nodes make a first level

Dear R I recently performed a cluster analysis. It produced the dendogram no problem but unfortunately the font size of the row.names were all cluttered due to their large size So I tried to change the font size using plclust(cluster.results, labels=iris$specie, cex=0.8) and R came back to me saying Error in plclust(cluster.results, labels = iris$specie, cex = 0.8) : unused argument(s)

Greetings, \Sexpr{} has worked on MikTex with earlier versions of R, I can not seem to get it to work with R-2.4.0. I run Sweave with MikTex using the following statement in Run under Accessories. latex -include-directory="C:\Program Files\R\R-2.4.0\share\texmf" "C:\Documents and Settings\Suzette\Desktop\MyFile\MyProgram.tex" Any advice would be much appreciated, Suzette

Hi everyone, I am using the fastgreedy.community function to get the $merges matrix and the $modularity vector. This serves my purpose of testing modularity of my graph. But I am "greedy" to plot the heat map and dendrrogram based on the $merges dendogram matrix. I know that heatplot does the graphics part but I am not sure if the dendogram generated by the heatplot will match the one

Hello everyone! I have two questions regarding cluster analysis with the package 'cluster' : 1. I have a dissimilarity matrix in csv format, and I would like to use it to generate a dendogram using 'hclust'. How do I make that matrix compatible with the function? I think it has to be similar to the objects generated by the 'dist' function. Any ideas? 2. Does

Greetings, I implemented BRugs to run the EWOC model with a cohort size N=1. I output the simulation data using bugsdata(data), where data is the following list. > data $Dose [1] 140 $Y [1] 0 bugsdata(data) puts out the file data.txt as follows. list(Dose=1.40000E+02, Y=0.00000E+00) after I type the following line modelData("data.txt")

Hello all, I am trying to do a Latin Hypercube Sampling (LHS) to a 5-parameter design matrix. I start as follows: library(lhs) p1<-randomLHS(1000, 5) If I check the distribution of each parameter (column), they are perfectly uniformly distributed (as expected).For example, hist(p1[,1]) Now the hard (maybe strange) question. I want the combination of the first three parameters to sum up to

Hello, I am a new member, and I need your help. For my work (thesis), I use the package rpart to construct trees. But, to continu my studies, I need to calcule the 'variable importance'. But, I don't find a program to do this in the implementation of R. Is there someone who know if there exist a program which calculate the 'variable importance', notion defined by Breiman and

Dear all Why isn'it possible to calculate Mahalanobis distances with R for a matrix with 1 row (observations) more than the number of columns (variables)? > mydata <- matrix(runif(12,-5,5), 4, 3) > mahalanobis(x=mydata, center=apply(mydata,2,mean), cov=var(mydata)) [1] 2.25 2.25 2.25 2.25 > mydata <- matrix(runif(420,-5,5), 21, 20) > mahalanobis(x=mydata,