similar to: AW: Paper on PAM and Clara

I need help on calling the svydesign function in the survey package (although this error appears not to be specific to svydesign). I am passing parameters incorrectly but am not sure how to correct the problem. ## Call the main function PS.sim (one of mine). The dots are parameters I omitted to simplify the question. ## y.col, str.col, clus.id, and PS.col are names of columns in the object pop.

The homals package http://www.jstatsoft.org/v31/i04 will get a major programming overhaul. This will take some time, but what's a few years on a 40-year project. Suggestions from the audience are welcome. homals() has a core loop over the m variables in which 1. tapply is used to compute category quantification (centroids) 2. category quantifications are then adjusted to satisfy the

The homals package http://www.jstatsoft.org/v31/i04 will get a major programming overhaul. This will take some time, but what's a few years on a 40-year project. Suggestions from the audience are welcome. homals() has a core loop over the m variables in which 1. tapply is used to compute category quantification (centroids) 2. category quantifications are then adjusted to satisfy the

> DATA [,1] [1,] -2.0314185 [2,] -0.3594015 [3,] -1.3225832 [4,] 1.5050892 [5,] -0.4535558 [6,] 3.6335036 [7,] 5.1965750 [8,] 4.2923129 [9,] 6.0913187 [10,] 4.9131070 > RESA [,1] [,2] [,3] [,4] [,5] [1,] -2.0314185 1.5050892 -1.3225832 -1.3225832 -2.0314185 [2,] -0.3594015 -0.3594015 4.2923129 -0.3594015

Hi, everyone, I am a newbie on R. Now I want to do image pixel classification by random forest. But I has not a clear understanding on random forest. Here is some question: As for an image, for example its size is 512x512 and has only one variable -- gray level. The histogram of the image looks like mixture Gaussian Model, say Gauss distribution (u1,sigma1), (u2,sigma2),(u3,sigma3). And a

Hi all, can anyone point me to a document that descirbes how to trace into an OS crash under Solaris 10? My hope is to get target-oriented description to figure out the system call and the process/thread that lead to the crash. Regards, Andreas

Hi, erveryone, I show much thanks to Andy and Matthew on former questions. I now sample only a small segment of a image can segment the image into several classes by RandomForest successfully. Now I have some confusion on it: 1. What is the internal component classifier in RandomForest? Are they the CART implemented in the rpart package? 2. I use training samples to predict new samples. But

Dear WizaRds, I am trying to compute the (adjusted) Rand Index in order to comprehend the variable selection heuristic (VS-KM) according to Brusco/ Cradit 2001 (Psychometrika 66 No.2 p.249-270, 2001). Unfortunately, I am unable to correctly use cl_ensemble and cl_agreement (package: clue). Here is what I am trying to do: library(clue) ## Let p1..p4 be four partitions of the kind

Dear people, A somewhat clueless question follows: I just discovered that the lme function in contrib package nlme for R, while similar to the lme function in Splus, does not use the cluster function option. This difference does not appear to be documented in the V&R `R Complements' file. I have data which is divided into 6 groups The lme model is of the form (simplified from the actual

R-helpers: I am using R 2.5 on Windows XP, packages all up to date. I have run into an issue with the MIcombine function of the mitools package that I hoped some of you might be able to help with. I will work through a reproducible example to demonstrate the issue. First, make a dataset from the pbc dataset in the survival package --------------- # Make a dataset library(survival) d <-

Dear R-listers, I do clustering on tens of individuals by thousands of traits. I have known the assignment of each individual. I want to classify the individuals by randomly resampling different subsets of the traits, for example, randomly resampling 100 traits for 100 times, then 200 traits for 100 times, then 300 traits for 100 times, ,,,,,,. By each subset of traits, I do clustering of the

Hi, I have been using the lqs function with method='lms'. However the results I get are a little different from the results noted by Rousseeuw & Leroy (Robust Regression and Outlier Detection) and I was wondering how to use these results for outlier detection. I'm using the stackloss dataset, for which the original Rousseeuw et al. program points out that observations 1,2,3,4

Hi. I'm checking into robust methods for principal components analysis. There seem to be several floating around. I'm currently focusing my attention on a method of Hubert, Rousseeuw, and Vanden Branden (http://wis.kuleuven.be/stat/Papers/robpca.pdf) mainly because I'm familiar with other work by Rousseeuw and Hubert in robust methodologies. Of course, I'd like to obtain

Dear r-helpers! I have trouble reading data from an Oracle data base using RODBC Version 1.0-3, R Version 1.7.1, Windows XP, Oracle8 ODBC Driver Version 8.1.6.4.0: > library(RODBC) > channel <- odbcConnect(dsn="PAV32", case="oracle", believeNRows=FALSE) > # ok, this was succesful > x <- sqlTables(channel) > x[37, ] TABLE_CAT TABLE_SCHEM TABLE_NAME

Hi: I have calculated the Silhouette score and Dunn score after hierarchical clustering for 3 clusters: #Distance measure d <- dist(USArrests, method = "euclidean") #Hierarchical clustering hc <- hclust(dist(USArrests), "ave") #calculating silhouette value for 3 clusters sil<- silhouette(cutree(hc, k=3), d) #calculating Dunn index for 3 clusters clus <- cutree(hc,

Hi! I have a question concerning the rcdk package: I generated a test sdf-file with 3 molecules in it and tried to perform clustering by fingerprints and plot the results in a dendogramm. This is what I did: mols <- load.molecules ("molecules.sdf") fp.list <- lapply (mols, get.fingerprint, 'maccs') fp.dist <- fp.sim.matrix (fp.list, method='tanimoto')

Dear R people, I need to do some work with clustering, but know next to nothing about it at present. R has (at least) three clustering packages, cluster, mclust, cclust. I was wondering if someone can direct me to some good books where I could find documentation and background on the functions in these packages. The html help in these packages lists the following as references. Can people

Dear R-users! I would like to search for "association rules" (and compute support and confidence, for example) in a data set, and wonder if this can be done with R (version 1.5.0 for Windows)? Particulary, I wonder if a) anyone has done something like that with R or b)maybe someone has written an R-interface to some existing algorithm/software (e.g. to something like the

Dear All, I am attempting to use the FANNY fuzzy clustering function in R (Kaufman & Rousseeuw, 1990), found in the "cluster" package. I have run into a variety of difficulties; the two most crucial difficulties are enumerated below. 1. Where is the 'm' parameter in FANNY? In _Finding Groups in Data: An Introduction to Cluster Analysis_ (1990) by Kaufman & Rousseeuw,

Hi Peter, You didn't give a very specific example, but it seems to me that what you wish to do is not really complicated. I suppose you have created a table of sequences vs. say hyprophobicity, charge, etc..., something like... seq hydroph arom b0001 0.104762 0.000000 b0002 0.035122 0.065854 b0003 0.024193 0.070968 b0004 -0.096729 0.084112 b0005 -0.973469 0.091837 b0006