similar to: setting distance matrix and clustering methods in heatmap.2

Hi, I''m using heatmap.2 to cluster my data, using the centroid method for clustering and the maximum method for calculating the distance matrix: library("gplots") library("RColorBrewer") test <- matrix(c(0.96, 0.07, 0.97, 0.98, 0.50, 0.28, 0.29, 0.77, 0.08, 0.96, 0.51, 0.51, 0.14, 0.19, 0.41, 0.51), ncol=4, byrow=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

I'm using this code to create a heatmap of expression data and indicate a specific column of data, post clustering: #Generate heatmap: heatmapChipDx <- heatmap.2(L3data_med2, dendrogram="column",trace="none", col=redgreen(64), colsep=seps, sepcolor="yellow", sepwidth = 0.1, margin=c(5,5), scale="row", key=FALSE) I have two problems that I'm

Hello, I'm trying to learn how to use lattice and levelplot in particular. There are three elements of customizing the plots I'm stuck with: a) Is there a way to put borders around each "cell" within a level-plot. I'm trying to do something like the colsep/rowsep/sepcolor/sepwidth parameters of heatmap.2 in gplots b) Can I alter the line-width of dendrogram added

Hello. I am not certain even how to search the archives for this particular question, so if there is an obvious answer, please smack me with a large halibut and send me to the URLs. I have been experimenting with fitting curves by using both maximum likelihood and maximum spacing estimation techniques. Originally, I have been writing distribution-specific functions in 'R' which work

Good afternoon, I ran heatmap and hclust on the same matrix x (strictly, I ran heatmap(x), and hclust(dist(t(x))), and realized that the two dendrograms were slightly different, in that the left-right arrangement of one pair of subclusters (columns) was reversed in the two functions (but all individual columns were grouped correctly). Looking through the code for heatmap as a most definite

H ello R-experts, I want to do ordination plots using vegan metaMDS. I have a where many cells have zero values. Data structure: X[1:10,1:14] Height.1 Height.2 Height.3 Height.4 Height.5 Height.6 Height.7 Height.8 Height.9 Height.10 Height.11 Height.12 Height.13 D30I1A 46 0 0 0 0 0 0 0 0 0 39 0 98 D30I1B

I would like you consider that the function ecdf could be extended in the following way to handle weights when computing Empirical distribution Functions. There exist particular cases that supports this kind of extension, see for example: Rao, C. R., 1997. Statistic and True. Putting chance to work. World Scientific Publishing. Cox, D. R., 1969. Some Sampling Problems in Technology. New

Dear R wizards, Hopeful someone can help me with what I believe is a pretty simple task. I pretty new to R so some (much) of the obvious escapes me. How do I get a distance matrix into heatmap? What do I tell distfun if what I'm trying to map is already an ordered distance matrix? I tried >heatmap(x, distfun=as.dist(x)) where x is the distance matrix but R gave me an error. Thanks in

Hello, I have come across some unexpected behaviour of the function heatmap in the stats package. This looks like a bug to me, but I might have misunderstood something. When calling the function in symmetric mode, the ColSideColors are plotted correctly, but RowSideColors appear in reverse order. This code (modified from the example on the help page) demonstrates the problem: cU <-

Dear R-users I've got the next problem: I've got this *function*: fitcond=function(x,densfun,pcorte,start,...){ myfn <- function(parm,x,pcorte,...) -sum(log(dens(parm,x,pcorte,...))) Call <- match.call(expand.dots = TRUE) if (missing(start)) start <- NULL dots <- names(list(...)) dots <- dots[!is.element(dots, c("upper",

Hi, Can anyone tell me how to set Z-score according to my own requirement as the below code is taking as per the file entries. Any help would be appreciable. library(gplots) x=read.table("final.txt", header=TRUE) mat=data.matrix(x) heatmap.2(mat, col=colorRampPalette(c("green","white","red"))(256), #col=greenred(75), Rowv=TRUE, Colv=FALSE, distfun = dist,

Hi, I am having troubles with heatmap(). I have a matrix containing pairwise distance values and I want to plot this matrix with heatmap. I wonder now what distfun is for. Is a distance matrix computed from my initial matrix again and then plotted? cheers, andreas

Hi netters, I have a matrix X of the size (1000,100). The values are from -3 to +3. When I tried heatmap(X, distfun=function(c),dist(c,method="bin"),hclustfun=function(m),hclust(m,method="average")) I got the error message: Error: evaluation nested too deeply: infinite recursion / options(expressions=)? However, if I used default parameters for distfunction:

Hi, Could you please help? Heatmap doesn't work with: > heatmap(as.matrix(SPIV2),na.rm = T) Error in hclustfun(distfun(x)) : NA/NaN/Inf in foreign function call (arg 11) There are no 0 data rows or column Thanks a lot Regards --------------------------------------- David

Hi, I run into following error when using heatmap() for data matrix "xx". Any help is appreciated? "xx" contains many "NA"s. > hv <- heatmap(data.matrix(xx)) Error in hclustfun(distfun(if (symm) x else t(x))) : NA/NaN/Inf in foreign function call (arg 11) Thanks a lot. Yuhong

I have a data which contain some NA value in their elements. What I want to do is to **perform clustering without removing rows** where the NA is present. I understand that `gower` distance measure in `daisy` allow such situation. But why my code below doesn't work? __BEGIN__ # plot heat map with dendogram together. library("gplots") library("cluster")

Hola Ruben, Sí precisamente es lo que comentas, en matemáticas no se suele llamar bucketización (este término se emplea más en informática) sino datos agrupados. Pero la idea es la que tu mismo dices. Respecto a las gráficas que has puesto, me han aclarado mucho sobre el tema, gracias. Si realizo lo mismo, por ejemplo con nbucket=1000 sigo obteniendo un p-valor de 1. Es decir, que casi le

Dear List, This is Elaine, a postgraduate studying in bird distributions in East Asia. I want to calculate Simpson dissimilarity index, based on a presence/absence matrix of bird species in islands in East Asia. (matrix row: 36 islands/matrix column: species ID) (R package vegan to make NMDS and R package betapart) In most papers using vegan for NMDS and betapart for dissimilarity

Hi, I am using heatmap.2 of gplots to make heatmaps of my the attached file. I am giving my code for the same .. library(gplots) x=read.table("1.txt", header=TRUE) mat=data.matrix(x) heatmap.2(mat, col=greenred(75), Rowv=TRUE, Colv=TRUE, distfun = dist, hclustfun = hclust, dendrogram = c("both"), scale = c("row"), na.rm=TRUE, trace="none",