similar to: problem with rfImpute (package randomForest)

I am having trouble with the rfImpute function in the randomForest package. Here is a sample... clunk.roughfix<-na.roughfix(clunk) > > clunk.impute<-rfImpute(CONVERT~.,data=clunk) ntree OOB 1 2 300: 26.80% 3.83% 85.37% ntree OOB 1 2 300: 18.56% 5.74% 51.22% Error in randomForest.default(xf, y, ntree = ntree, ..., do.trace = ntree, : NA not

In trying to execute this line in R (Version 1.7.1 (2003-06-16), under windows XP pro), with the randomForest library (about two weeks old) loaded, the program crashed: bost4rf <- rfImpute(TargetDensity~.,data=bost4rf0) Specifically, an XP dialog box popped up, saying ?R for windows GUI front-end has encountered a problem and needs to close.? That was the dialog saying asking whether I

Dear Administrator, I am using linux (suse 10.2). While attempting rfImpute, I am getting the following error message: > Members <- rfImpute(Status ~ ., data = Members) Error in .C("classRF", x = x, xdim = as.integer(c(p, n)), y = as.integer(y), : C symbol name "classRF" not in DLL for package "randomForest". I need the help to sort out above error.

Hello R-user! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) I tried rfImpute (randomForest) and as far as I understood should it replace NA`s using a proximity matrix: > set.seed(100000) > Subset5Imputed<-rfImpute(Sex~., data=Subset5) ntree OOB 1 2 300: 11.78% 12.36% 11.21% ntree OOB 1 2 300: 12.07% 12.64%

R 2.7.2 Windows XP I am using apply to compute a series of Student's t-test from two matrices, sample1 and sample2. boo<-apply(sample1,1,t.test,sample2) I want to pick of the p-values from the tests, but can't seem to get it to work. I have tried several methods to get the values including: boo<-apply(sample1,1,t.test$t.test,sample2) boo<-apply(sample1,1,t.test,sample2)$t.test

After ctree builds a tree, how would I determine the direction missing values follow by examining the BinaryTree-class object? For instance in the example below Bare.nuclei has 16 missing values and is used for the first split, but the missing values are not listed in either set of factors. (I have the same question for missing values among numeric [non-factor] values, but I assume the answer

Dear All, I'm getting a error while trying to apply the BreastCancer dataset (package=mlbench) to kknn (package=kknn) that I don't understand as I'm new to R. The codes are as follow: rm = (list = ls()) library(mlbench) data(BreastCancer) library(kknn) BCancer = na.omit(BreastCancer) d = dim(BCancer)[1] i1 = seq(1, d, 2) i2 = seq(2, d, 2) t1 = BCancer[i1, ] t2 =

require(randomForest) rf.pred<-predict(fit, valid, type="prob") > rf.pred[1:20, ] 0 1 16 0.0000 1.0000 23 0.3158 0.6842 43 0.3030 0.6970 52 0.0886 0.9114 55 0.1216 0.8784 75 0.0920 0.9080 82 0.4332 0.5668 120 0.2302 0.7698 128 0.1336 0.8664 147 0.4272 0.5728 148 0.0490 0.9510 153 0.0556 0.9444 161 0.0760 0.9240 162 0.4564 0.5436 172 0.5148 0.4852 176 0.1730

R-help, I am trying to perform a basic anlaysis of the BreastCancer data from "mlbench" using the svm() function in "e1071". I use the following code library("e1071") library("mlbench") data(BreastCancer) BC <- subset(BreastCancer, select=-Id) pairs(BC) model <- svm(Class ~ ., data=BC, cross=10) ## plot(model, BC, ) tobj <- tune.svm(Class ~ .,

Apologies - I was not trying to correct you Brian, but to explore how the situation could arise. I'm sure you had a good idea why the namespace (or a reference to it) had been saved, but this was not clear to me and I thought, possibly not to others either. Thanks for putting me right over parent environments vs. enclosures - again I was not trying to correct you with the point I made there,

Last week Giovanni Parrinello posted a message asking why various packages were loaded when he loaded an .Rdata file. Brian Ripley replied saying he thought it was because the saved workspace contained a reference to the namespace of ipred. (Correspondence copied below). This begs the question: how did the reference to the namespace of ipred come to be in the .Rdata file? Brian did say it is

Hi Everybody, i again have the problem in using tune.knn(), its giving an error saying missing values are not allowed.... again here is the script for BreastCancer Data, library(e1071) library(mda) trdata<-data.frame(train,row.names=NULL) attach(trdata) xtr <- subset(trdata, select = -Class) ytr <- Class bestpara <-tune.knn(xtr,ytr, k = 1:25, tunecontrol = tune.control(sampling

Last week Giovanni Parrinello posted a message asking why various packages were loaded when he loaded an .Rdata file. Brian Ripley replied saying he thought it was because the saved workspace contained a reference to the namespace of ipred. (Correspondence copied below). This begs the question: how did the reference to the namespace of ipred come to be in the .Rdata file? Brian did say it is

Hello Everybody, I am reffering David Meyer's Benchmarking Support Vector Machines , Report No.78 (Nov.2002), i am newly working with R but i am not sure how it is handling missing values in the benchmark datasets, I would be very thankful to you if you could let me know how to handle those missing numerical & categorical variables in the data (e.g. BreastCancer). because, i am

Hi Jan-Paul, You definitely want to be careful with na.omit in randomForest -- that wipes out any row with even one NA. If NAs are sprawled throughout your dataset, na.omit might end up killing a lot of rows. Here's my usual MO for missing values: 1) "impute" in Hmisc fills in gaps with the mean, median, most common value, etc. 2) rfImpute: fits a forest on the rows available and

Does anyone know a reason why, in principle, a call to randomForest cannot accept a data frame with missing predictor values? If each individual tree is built using CART, then it seems like this should be possible. (I understand that one may impute missing values using rfImpute or some other method, but I would like to avoid doing that.) If this functionality were available, then when the trees

I have a question regarding NA in randomForest (in R). I have a dataset which include both numerical and non-numerical variables, and the data includes some NA. I tried to use na.roughfix but then i get an error message "na.roughfix only works for numeric or factor". I also tried rfImpute but this does not work either because I have some NA in my response variable. Does anyone have som

Hi all, I am using the package "random forest" for random forest predictions. I like the package. However, I have fairly large data sets, and it can often take *hours* just to go through the "na.roughfix" call, which simply goes through and cleans up any NA values to either the median (numerical data) or the most frequent occurrence (factors). I am going to start

I have a set of timestamp data that I have in a text file that I would like to import into R for analysis. The timestamps are formated as follows: DT_1,DT_2 [2006/08/10 21:12:14 ],[2006/08/10 21:54:00 ] [2006/08/10 20:42:00 ],[2006/08/10 22:48:00 ] [2006/08/10 20:58:00 ],[2006/08/10 21:39:00 ] [2006/08/04 12:15:24 ],[2006/08/04 12:20:00 ] [2006/08/04 12:02:00 ],[2006/08/04 14:20:00 ] I can get

Hi, In R, how to do missing value replacement for test data in randome forest in the way Breiman decribed. thanks in advance iris