similar to: Calculate missing value using a correlation metric

Hi guys, I've been using the kmeans and hclust functions for some time now and was wondering if I could specify a custom metric when passing my data frame into hclust as a distance matrix. Actually, kmeans doesn't even take a distance matrix; it takes the data frame directly. I was wondering if there's a way or if there's a package that lets you create distance matrices from

Hi, There are two packages providing knn classification: class and knnTree. However, it seems both uses Eucleadian distances only. How can I uses a custom distance function with either package? Thanks, Xiao-Jun

Hi, I'm plotting 4 graphs on one page (2x2 matrix) but I cant seem to get the title for the whole page right. I'm doing: op <- par(mfrow = c(2,2), pty="s") hist(var$V2, breaks="FD",main="Euclidean Metric", xlab="Sum of 3NN ... hist(var$V2, breaks="FD",main="Manhattan Metric", xlab="Sum of 3NN ... hist(var$V2,

Hello, pardon my ingorance, but what distance metric is used in this function in the nnclust package? The manual only says: "Find the nearest neighbours of points in one data set from another data set. Useful for Mallows-type distance metrics." BR, Jay

Hello. I have the following distance matrix between 8 points: [1,] 0.000000 3.162278 7.280110 8.544004 7.071068 9.899495 6.403124 8.062258 [2,] 3.162278 0.000000 5.000000 6.403124 4.472136 8.944272 6.082763 8.062258 [3,] 7.280110 5.000000 0.000000 1.414214 1.000000 5.000000 4.242641 5.830952 [4,] 8.544004 6.403124 1.414214 0.000000 2.236068 4.123106 4.472136 5.656854 [5,] 7.071068 4.472136

Vince Carey found this (thank you!). Since the fix to the problem is not entirely obvious, I post this to R-devel as RFC: help(dist) says: >> Missing values are allowed, and are excluded from all computations >> involving the rows within which they occur. If some columns are >> excluded in calculating a Euclidean, Manhattan or Canberra >> distance, the sum is

I have spatial data on a sphere (the Earth) for which I would like to run an gls model assuming that the errors are autcorrelated, i.e. including a corSpatial correlation in the model specification. In this case the distance metric should be calculated on the sphere, therefore metric = "euclidean" in (for example) corSpher would be incorrect. I would be grateful for help on how to

Hello R group Do you know if an EM algorithm exits for R to estimate missing data in a sample? I just found knn algorithm in to the package emv but it doesn't look to be the usual EM algorithm. Thanks Xavier -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

Hi, I'm mighty new to R. I'm using it on Windows. I'm trying to cluster using a distance matrix I created from the data on my own and called it D10.dist. I loaded the cluster package. Then tried the following command... > agnes("E:D10.dist", diss = TRUE, metric = "euclidean", stand = FALSE, > method = "average", par.method, keep.diss = n < 1000,

Hi, For example: I built a half matrix "w" using a daisy(x, metric = c("euclidean")) http://www.nabble.com/file/p25211016/1.jpg And next I transformed this matrix "w" using isoMDS function, for example isoMDS(w, k=2) and as result I got: http://www.nabble.com/file/p25211016/2.jpg And now I have two questions: 1. If number in matrix w[2, 1] (= 0.41538462) match

Hello all, I'd like to calculate the mean correlation within a cluster and understand if it's significantly >0. I'm using packages 'geomorph' and 'paleomorph'. #Simulate an array A <- array ( rep ( 1 : 36 , by = 4 ), dim = c ( 12 , 3 , 4 )) #Load 'geomorph' package and superimpose coordinates test.gpa <- gpagen ( A , print.progress = FALSE ) #Load

I am using the PAM algorithm in the CLUSTER library. When I allow PAM to seed the medoids using the default __build__ algorithm things work well: > pam(stats.table, metric="euclidean", stand=TRUE, k=5) But I have some clusters from a Hierarchical analysis that I would like to use as seeds for the PAM algorithm. I can't figure what the mediod argument wants. When I put in the

Using the 'CARET' package, is it possible to specify weights for features used in model prediction? And for the 'knn' implementation, is there a way to choose a distance metric (i.e. Mahalanobis distance)? Thanks, ~Kendric [[alternative HTML version deleted]]

Dear R users, I need to come up with an efficient method to compute the correlation (or at least, the euclidean distance if that's easier) between specific rows in a data frame (46,232 rows, 29 columns). The pairs of rows between which I want to find the correlation share a common value in one of the columns. So for example, in the following

***********reading in data********** data<-read.table("microarray.txt",header=T, sep="\t") head(data) dim(data) attach(data) ***********creating matrix and calculating variance across probesets******** x<-1:20000 y<-2:141 data.matrix<-data.matrix(data[,y]) variableprobe<-apply(data.matrix[x,],1,var) hist(variableprobe) **************filter out low

Hello, I am working with a matrix of multilocus genotypes for ~180 individual snail samples, with substantial missing data. I am trying to calculate the pairwise genetic distance between individuals using the stats package 'dist' function, using euclidean distance. I took a subset of this dataset (3 samples x 3 loci) to test how euclidean distance is calculated: 3x3 subset used

Hi! I have a dissimilarity matrix X and try to compare it with X' = dist(cmdscsale(X,k)). If I increase k, I should expect that the error (or fit) should monotonically decrease, right. Here is a sample code; library(mva) set.seed(12345) x <- as.matrix(dist(matrix(rnorm(100),ncol=10,byrow=T))) # x[1,2]<-x[2,1]<-1000 ## <<--** 1 # x[5,6]<-x[6,5]<-1000 ##

I need to use the Bhattacharyya distance metric to determine population separation. Has anyone written a Bhattacharyya distance metric function in R? -- View this message in context: http://old.nabble.com/Bhattacharyya-distance-metric-tp26221259p26221259.html Sent from the R help mailing list archive at Nabble.com.

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")

I have a binary classification problem where the fraction of positives is very low, e.g. 20 positives in 10,000 examples (0.2%) What is an appropriate cross validation scheme for training a classifier with very few positives? I currently have the following setup: ======================================== library(caret) tmp <- createDataPartition(Y, p = 9/10, times = 3, list = TRUE)