similar to: random forest proximities

Good Day, Included below is some code to generate data and to fit a mixed effects model to this fake data. The code works as expected when I call the function "lme" in Splus but not in R. The error message from calling lme in R is: "Error in getGroups.data.frame(dataMix, groups) : Invalid formula for groups" I installed the nlme package for R around 20 August

Hi, I have a data set with 1000 observations and 260 predictors. The predictor variables are all ordinal. There are 2 classes labeled as, F and T with class proportions of 0.44 and 0.56, respectively. In a call to the function randomForest() with mytry=1 and nodesize=1 and ntree=100 the resulting classifier puts all observations in class T. When I change nodesize to nodesize=5 I get the

Hi, I have a dataframe, y, with 1000 rows and 266 columns. The first column of y is a 2 level factor response; columns 2:266 are each ordinal variables that can take on one of the values 1, 2, or 3. I'm trying to fit a model with the calls: (A) y.tree <- tree(pref~.,data=y,mindev=0,minsize=2) and (B) y.tree <- tree(pref~.,data=y,split='gini') pref is the name of the factor

Hi, In Splus if I call the function crosstabs() the output is a contigency table; in each cell of the table is printed: N, N/RowTotal, N/ColTotal, N/Total. N is the number of observations in each cell. The same call to xtabs() in R will produce the contigency table but the only entry in each cell is N. How can I get the same relative frequencies that crosstabs() gives? Thanks, mike --

Hi, here are three results of random forest (version 4.0-1). The results seem to be more or less the same which is strange because I changed the classwt. I hoped that for example classwt=c(0.45,0.1,0.45) would result in fewer cases classified as class 2. Did I understand something wrong? Christian x1rf <- randomForest(x=as.data.frame(mfilters[cvtrain,]),

Hi All, I am looking for a reference that explains how the randomForest function in the randomForest package uses the classwt parameter. Here: http://tolstoy.newcastle.edu.au/R/e4/help/08/05/12088.html Andy Liaw suggests not using classwt. And according to: http://r.789695.n4.nabble.com/R-help-with-RandomForest-classwt-option-td817149.html it has "not been implemented" as of 2007.

Date: Tue, 27 Jun 2000 16:09:43 -0600 (MDT) From: "W. Scott Wilburn" <wilburn at lanl.gov> To: ssh at clinet.fi Subject: F-secure -> Openssh Compatibility We have Macintoshes running Fsecure SSH client 1.0.1 which are unable to connect to a server running Openssh 2.1.1 on Red Hat 6.2. I believe that the problem is a bug with Fsecure, since a 30-day trial version of 1.0.2 works

Dear R-helpers, I'm working on mass spectra in randomForest/R, and following the recommendations for the case of noisy variables, I don't want to use the default mtry (sqrt of nvariables), but I'm not sure up to which proportion mtry/nvariables it makes sense to increase mtry without "overtuning" RF. Let me tell my example: I have 106 spectra belonging to 4 classes, the

Is there anyway to plot a matrix using a 3d bar plot. Something like bar3 in matlab? The example in demo hist3d does a 3d barplot for binned data, but has anyone tried something for a simple matrix with spaces betwen bars and axis labels using matrix dimnames or 1,2,3? stages<-letters[1:3] A<-matrix(c( 0.21, 0.21,0.03, 0.55, 0.58, 0.09, 1.30, 1.35, 0.22), nrow=3, byrow=TRUE,

Dear help list, I think I found a bug a the R Random Forest. Hopefully, you are able to reproduce it. I use R version 2.7.2 and RF version 4.5-27. This is a minimal code to describe the problem: library(randomForest) tries <- 20 dimension <- 20 n <- 200 outlyingness <- rep(NaN,tries) for (o_number in 1:tries){ features <- matrix(rnorm(n*dimension,0,1),n,dimension) #Generate

Hi all, Can some one tell me the difference between the following two formulas? 1. epiG.rf <-randomForest(gamma~.,data=data, na.action = na.fail,ntree = 300,xtest = NULL, ytest = NULL,replace = T, proximity =F) 2.epiG.rf <-randomForest(gamma~.,data=data, na.action = na.fail,ntree = 300,xtest = NULL, ytest = NULL,replace = T, proximity =F) [[alternative HTML version deleted]]

I'm new to this list, so please forgive me if this has been discussed before. It appears that one of the (commendable) design goals of OpenSSH is to re-use existing open-source libraries wherever possible in order to simplify the OpenSSH code and hopefully improve security in the process. As exhibited by the current, non-open SSH, supporting all of the nuances of authentication and logins

"classwt" in the current version of the randomForest package doesn't work too well. (It's what was in version 3.x of the original Fortran code by Breiman and Cutler, not the one in the new Fortran code.) I'd advise against using it. "sampsize" and "strata" can be use in conjunction. If "strata" is not specified, the class labels will be used.

Hello Everyone! It's been a while since I last posted a question! Hope everyone has been doing well! ~~~ CONTEXT ~~~ I have recently entered a beginner-level competition on kaggle. The goal of the competition is to build a model that predicts who did/did not survive on the Titanic. I decided to use random forests as I have been wanting to learn the algorithm and the competition

Greetings R Users! I am posting to inquire about the proximity matrix in the randomForest R-package. I am having difficulty pushing very large data through the algorithm and it appears to hang on the building of the prox matrix. I have read on Dr. Breiman's website that in the original code a choice can be made between using an N x N matrix OR to increase the ability to compute large

Hi, I am trying to model a dataset with the response variable Y, which has 6 levels { Great, Greater, Greatest, Weak, Weaker, Weakest}, and predictor variables X, with continuous and factor variables using random forests in R. The variable Y acts like an ordinal variable, but I recoded it as factor variable. I ran a simulation and got OOB estimate of error rate 60%. I validated against some

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

O.K. I know it was too late to make changes for the next release, but thankfully there werent any changes needed. Kerberos over ssh protocol 1 worked out of the box from CVS today (2002/10/04-14:30) The krb5 patches we were using (I think from Olaf K) was all in the code. The only thing I had to patch to get things working was removing the scard-install from the Makefile.in The compiled code

Hi, I need to resample rows in a data frame by subsets L3 <- LETTERS[1:3] d <- data.frame(cbind(x=1, y=1:10), fac=sample(L3, 10, repl=TRUE)) x y fac 1 1 1 A 2 1 2 A 3 1 3 A 4 1 4 A 5 1 5 C 6 1 6 C 7 1 7 B 8 1 8 A 9 1 9 C 10 1 10 A I have seen this used to sample rows with replacement d[sample(nrow(d), replace=T), ] x y fac 7 1 7 B 2

Hi, I am currently writing code to input a few thousand files, run them through the Random Forests package, and then output corresponding results. When I use the code below: zz<-textConnection("ex.lm.out", "w") sink(zz)