similar to: (no subject)

Hai From which CRAN mirror can get the package ‘LPP2005REC’ Ram [[alternative HTML version deleted]]

Can any one help me where i can find small standalone R programs that do things like: scatterplot, mean/median, kernel density estimation etc. Ram [[alternative HTML version deleted]]

Hi, Anybody know what are the common Standard statistical methods used for the analysis of a dataset,and anybody know which of these methods give similar results Ram [[alternative HTML version deleted]]

Hi, I would like to plot a map of US counties using different colors. map() seems to be the function to use, e.g. library(maps); map('usa'); map('county', 'colorado', add=T,fill = T, col=c(1:5)) plots Colorado counties using colours 1 to 5. However, I want each color to represent a certain value - a value to be picked from a data frame. This code should show a

The "maps" package has a function called "match.map", which is for map coloring . Its example is followed: # filled map showing Republican vote in 1900 # (figure 6 in the reference) data(state, package = "datasets") data(votes.repub) state.to.map <- match.map("state", state.name) x <- votes.repub[state.to.map, "1900"] gray.colors <-

Hi, I need help with the randomForest prediction. i run the folowing code: > iris.rf <- randomForest(Species ~ ., data=iris, > importance=TRUE,keep.forest=TRUE, proximity=TRUE) > pr<-predict(iris.rf,iris,predict.all=T) > iris.rf$votes[53,] setosa versicolor virginica 0.0000000 0.8074866 0.1925134 > table(pr$individual[53,])/500 versicolor virginica 0.928

Hi everybody, I want to use the cluster package (Cluster Analysis Extended Rousseeuw et al.). I downloaded it from the CRAN and installed it on my linux system (fedora core 4). All seemed to be allright. But when trying to launch examples, I obtained the following message : > library(cluster) > data(votes.repub) > agn1 <- agnes(votes.repub, metric = "manhattan",

Dear mailinglist members, I was wondering if there was a way to re-evaluate the instances of a tree (in the forest) again after I have manually changed a splitpoint (or split variable) of a decision node. Here's an illustration: library("randomForest") forest.rf <- randomForest(formula = Species ~ ., data = iris, do.trace = TRUE, ntree = 3, mtry = 2, norm.votes = FALSE) # I am

Dear list, is there a way how to add information to internal nodes (branching points) in dendrogram created via plot.agnes function (package cluster)? I wish to place colored circles on the nodes, but I don't know how to proceed... I'll be grateful for any suggestion Ond?ej -- Ond?ej Mikula Institute of Animal Physiology and Genetics Academy of Sciences of the Czech Republic Veveri 97,

Dear R-devel, Inspired by Michael Li's talk at JSM, I decided to try rpvm and snow on our two linux boxes. It only took me a couple of hours of screwing around to get it working (sooner if I had RTFM). Our setup is: 2 dual PIII-866 Xeons, one with 2GB RDRAM, the other with 1.28GB RDRAM. The first machine is acting as the NIS/NFS server. both /usr and /home are exported to the second

> My question then is, how to compute a similar procedure using the 'world' > database. Specifically, how can I access the country names in the 'world' > database to accomplish the translation to the country names in my dataset? > Is there any way to unpack the 'world' database to do the matching in an > external program? And does anybody now of other (more

I have a dataset and I wish to use two different models to predict. Both models are SVM. The reason for two different models is based on the sex of the observation. I wish to be able to make predictions and have the results be in the same order as my original dataset. To illustrate I will use iris: # Take Iris and create a dataframe of just two Species, setosa and versicolor, shuffle them

Hello R-helpers, I'm writing a long function in which I manipulate a certain number of datasets. I want the arguments of said function to allow me to adapt the way I do this. Among other things, I want my function to have an argument which I will pass on to subset() somewhere inside my function. Here is a quick and simplified example with the iris dataset. myfunction<-function(table,

Dear All, I was trying to follow some of the examples in Venables and Ripley "Modern applied ... with S-plus" I have downloaded a copy of the iris data set and loaded into R. : however I cannot use the apply command (from p47): > apply (iris, 2 ,mean) Error in sum(..., na.rm = na.rm) : invalid "mode" of argument > apply (iris, c(2) ,mean) Error in sum(..., na.rm =

Hopefully I will not be flamed for this on the list, but I am starting out with R and having some trouble with combining plots. I am playing with the famous iris dataset (checking out example dataset in R while reading through Introduction to datamining) What I would like to do is create three graphs (combined boxplots) besides each other for each of the three species (Setosa, Versicolour and

can you recommend a good manual for R that starts with a data set and gives demonstrations on what can be done using R? I downloadedR Langauage definition and An introduction to R but haven't found them overly useful. I'd really like to be able to follow some tutorials using a dataset or many datasets. The datasets I have available on R are Data sets in package 'datasets':

Hello, I'll use part of the iris dataset for an example of what I want to do. > data(iris) > iris<-iris[1:10,1:4] > iris Sepal.Length Sepal.Width Petal.Length Petal.Width 1 5.1 3.5 1.4 0.2 2 4.9 3.0 1.4 0.2 3 4.7 3.2 1.3 0.2 4 4.6 3.1 1.5

Hi, Since I've had no replies on my previous post about my problem I am posting it again in the hope someone notice it. The problem is that the randomForest function doesn't take datasets which has instances only containing a subset of all the classes. So the dataset with instances that either belong to class "a" or "b" from the levels "a", "b" and

I have a question on the output generated by randomForest in classification mode, specifically, the confusion matrix. The confusion matrix lists the various classes and how the forest classified each one, plus the classification error. Are these numbers essentially averages over all the trees in the forest? If so, is there a way I can get the standard deviation values out of the randomForest,

I've previously installed R on Solaris and everything went fine there. I'm now trying to install on Irix 6.5 (about which, unfortunately, I have less than thorough knowledge) and it is failing. R itself seems to be working okay. Initially, Irix's dumb make program didn't understand some Makefile syntax in the tests, so I just ignored them and installed it. After the