similar to: cutree() and horizontal dendrograms

Any idea why clusters five and six fall off the scale in the following? library(cluster) library(maptree) data(flower) dfl2<-daisy(flower,type=list(asymm=c(1,3),ordratio=7)) hdfl2<-hclust(dfl2) prune.dfl2<-prune.clust(hdfl2,k=6) plot(prune.dfl2) Of course the following will display all six clusters, but without a scale: draw.tree(prune.dfl2) Best wishes, Mikkel Mikkel Grum

I'm trying to plot observations against observation dates and getting julian dates along the x-axis: library(date) Week<-as.date(c("05/02/03","05/09/03","05/16/03","05/23/03","05/30/03","06/0 7/03","06/14/03")) Leafminers<-c(0,2,5,10,4,6,5) Diglyphus<-c(0,0,4,5,7,3,1) LeafDig<-cbind(Week,Leafminers,Diglyphus)

Can anyone tell me what's wrong with this command?? xx<-subset(x,LOCAL.NAME==c("Chifumbata","Chikota"),select=c(13,16,19,23,26,2 9,30,33:48)) Warning message: longer object length is not a multiple of shorter object length in: LOCAL.NAME == c("Chifumbata", "Chikota") both of the following commands work fine

Is there a way of removing all rows with missing values from a data frame? I usually use subset(x, var1!="NA") and repeat for each variable. It would be nice to be able to do it in one fell swoop. Also, surprisingly, it doesn't always work. Sometimes I'm left with an empty set even though not all rows have missing values for the variable. Cheers, mikkel Mikkel Grum,

First question: I have an Excel file with both character and numeric variables that I want to import to a data.frame, so I've saved the data to a tab-delimited text file. read.table : * Won't identify the mode of my character variables, as some of them contain only numbers and none of them are surrounded by quotes, * Won't allow me to specify the mode of my variables, * Doesn't

Is there a way of extracting mean distance or dissimilarity for a given strata from a 'dist' or 'dissimilarity' object, e.g. extract mean distances for each species in Anderson's iris data? data(iris) iris.dist<-dist(iris[,1:4]) then what? Mikkel Grum, PhD Genetic Diversity Scientist International Plant Genetic Resources Institute (IPGRI) Sub-Saharan Africa Group ***

I'm trying to plot four different sorghum types on a plot using a different colour/symbol combination for each sorghum type (TYPBOTA in the script below). What am I doing wrong?: plot(xx$LATITUDE,xx$SFD1, points(xx$SFD1,xx$LATITUDE, col=1:4[codes(xx$TYPBOTA)],pch=c(4,3,1,2)[codes(xx$TYPBOTA)]) grid() I get the following error message Error in 1:4[codes(xx$TYPBOTA)] : NA/NaN argument

I have two problems where I've come up with some code that will do the analysis that I want, but it looks pretty clumsy. In the first case, I calculate the variance on five different columns for each of 14 clusters and get them into one matrix. I get the job done, but I would have thought that it could be done in one or two lines, not six, and be generalized so that it didn't matter how

This message is in MIME format. Since your mail reader does not understand this format, some or all of this message may not be legible. ------_=_NextPart_000_01C35A53.75780090 Content-Type: text/plain; charset="iso-8859-1" Anyone have a clue why hclust() and agnes() produce different results in the example below when both use method="average"?? I'm not able to reproduce

>>>>> "MM" == Martin Maechler <maechler@stat.math.ethz.ch> writes: >>>>> "MikG" == M GRUM <M.GRUM@CGIAR.ORG> writes: MikG> I'm resending this bug report with a new example. As MikG> seen below, cut.dendrogram gives an error message for MikG> some heights, but not for others and with some MikG> datasets

>>>>> "MikG" == M GRUM <M.GRUM@CGIAR.ORG> writes: MikG> I'm resending this bug report with a new example. As MikG> seen below, cut.dendrogram gives an error message for MikG> some heights, but not for others and with some MikG> datasets adn not others. I can't see why. MikG> Last time I unwittingly sent my message with

Hello, I would like to cut a hclust tree into several groups at a specific similarity. I assume this can be achieved by specifying the "h" argument with the specified similarity, e.g.: clust<-hclust(dist,"average") cut<-cutree(clust,h=0.65) Now, I would like to draw rectangles around the branches of the dendrogram highlighting the corresponding clusters, as is done by

I'm clustering rather large data sets and would like to cut the dendrograms to get a better view of specific components. I calculate the dissimilarity matrix using daisy() because I have a mixture of variable types: factors, ordered factors and numerical variables. If I want one dendrogram, I use agnes() for the agglomerative nesting and pltree() to draw the dendrogram. That way, I get the

Hi All: I am clustering 500 genes using hclust of R. Visualizing cluster membership becomes difficult with so many genes in each cluster...Is there a way of printing the dendrogram in multiple pages so that I can clearly see what is in each cluster? Thanks in advance. Karthi.

I'm resending this bug report with a new example. As seen below, cut.dendrogram gives an error message for some heights, but not for others and with some datasets adn not others. I can't see why. Last time I unwittingly sent my message with HTML formatting. This time I'm travelling and using an e-mail system that I am unfamiliar with. As far as I can see, I am not using HTML.

Hello, Can anyone offer any insight on graphing classification dendrograms with the nodes marked? plot(tree()) produces a nice tree structure but there's no acccompanying text. Thanks, D S. David White sdavidwhite at bigfoot.com Columbus, Ohio -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hi! I just discovered that cutree() and cut.dendrogram() do not assign the same cluster numberings when called on the same tree. More specifically, cutree() assigns cluster numbers by order of appearance in the data, while cut.dendrogram() sorts clusters by height (see example below). I guess this is for historical reasons? I'm hit by this difference when I want to get a vector of cluster

Hi! In the example: hc <- hclust(dist(USArrests), "ave") dend1 <- as.dendrogram(hc) dend2 <- cut(dend1, h=70) Do the branches "Branch 1", "Branch 2", "Branch 2"...in dend2$upper str(dend2$upper) --[dendrogram w/ 2 branches and 4 members at h = 152] |--[dendrogram w/ 2 branches and 2 members at h = 77.6] | |--leaf "Branch 1" (h=

Hi, this is rather a (presumed) bug report than a question because I can solve my personal statistical problem by working with hclust instead of agnes. I have done a complete linkage clustering on a dist object dm with 30 objects with agnes (R 1.8.0 on RedHat) and I want to obtain the partition that results from a cut at height=0.4. I run > cl1a <- agnes(dm, method="complete")

Hi, this is rather a (presumed) bug report than a question because I can solve my personal statistical problem by working with hclust instead of agnes. I have done a complete linkage clustering on a dist object dm with 30 objects with agnes (R 1.8.0 on RedHat) and I want to obtain the partition that results from a cut at height=0.4. I run > cl1a <- agnes(dm, method="complete")