similar to: Adding weights to ecdf

Hi list: This is my first time to post my question on the list. Thanks for your help. I am solving a quadratic programming using R. Here is my question: w = arg min 0.5*w'Mw - w'N s. t. sum(w) = 1; w>0 note: w is weight vector, each w_i must >=0, and the sum of w =1. Here is my R code: A <-matrix(c(2.26,1.26,1.12,1.12,2.27,1.13,1.12,1.13,2.2),3,3); B <-

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

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 dears, I expect that Mr Thomas Lumley will read this message. I have data from a complexe stratified survey. The population is divide in 12 regions and a region consist to and urban area and rural one. there to region just with urbain area. stratification variable is a combinaison of region and area type (urban/rural) In rural area, subdivision are sample with probabilties proporionnal to

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)

heatmap.2 defaults to dist for calculating the distance matrix and hclust for clustering. Does anyone now how I can set dist to use the euclidean method and hclust to use the centroid method? I provided a compilable sample code bellow. I tried: distfun = dist(method = "euclidean"), but that doesn't work. Any ideas? library("gplots") library("RColorBrewer") test

Hi All. I have created two, 3-D, color graphs using persp(). Z values range from 1 to 100 on the first plot, and 0.5 to 50 on the second plot. I would like to keep the color range constant between the two graphs, rather than each graph showing the full range of colors. So the first graph should go from yellow to red and the second graph should go from yellow to orange. Below I present a

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

I am trying to model counts data from three different sites where the number of zeros differs significantly from one sight to another and also the counts gets bigger in site with few number of zeros considerably, meaning that the k's are different. In my model I am including about 10 covariates to start with, and I have beed attempting to fit a NEGATIVE BINOMIAL MODEL. The problem is the

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",