similar to: Problems using rfImpute

Hello R-user! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) I try to find the most important variables to divide my dataset as given in a categorical variable using randomForest. Is randomForest() able to deal with count data? Or is there no difference because only the ranks are used in the trees? Thanks in advance Birgit Birgit Lemcke Institut f?r

R version 2.6.2 PowerBook G4 Hello R User, I try to perform an ANCOVA using the glm function. I have a dataset with continuous and categorical data (explanatory variables) and my response variable is also binary categorical. Fehler: NA/NaN/Inf in externem Funktionsaufruf (arg 4) Zus?tzlich: Warning messages: 1: In Ops.factor(y, mu) : - nicht sinnvoll f?r Faktoren (makes no sense for

Hello R-user! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) I try to find the most important variables to divide my dataset as given in a categorical variable. code: Test.rf4<-randomForest(Sex~.,na.action=na.roughfix, data=Subset4, importance=TRUE, proximity=TRUE, ntree=10000, do.trace=1000, keep.forest=FALSE) My dataset contains also ordered

Hello R-user community! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) Presently I try to run the function metaMDS (vegan) using an existing dissimilarity-matrix. As I would like to start with this matrix I thought I could just give the matrix using the x= -argument Test<-metaMDS(x=Dist.Gower) Fehler in inherits(comm, "dist") :

Hello R-user community! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) I did the following : pdf("InLnegthMaxHomogeneity.pdf") boxplot(inflorescence_length_Max~Sex, main="Bartletts Homogeneity for inflorescence length",data=FemMal_Sex) Homo<-bartlett.test(FemMal_Sex$inflorescence_length_Max,FemMal_Sex$Sex) text( 2, 500,

Hello R-Cracks, I am using R 2.6.1 on a PowerBook G4. I would like to perform a discriminant function analysis. I found lda in MASS but as far as I understood, is it only working with explanatory variables of the class factor. My dataset contains variables of the classes factor and numeric. Is there another function that is able to handle this? Thank you all in advance. Greetings Birgit

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 everybody, this is my first request about R so I am sorry if I send it to a bad mail or if I am not very clear. So my problem is about the use of rfImpute from randomForest package. I am interested in imputations of missing values and I read that randomForest can make it. So i write the following code : set.seed(100); library(mlbench) library(randomForest) data(BreastCancer)

Hello, I want to fit a Gompertz model for tree diameter growth that depends on a 4 levels edaphic factor (?Drain?) and I don?t manage to introduce the factor variable in the formula. Dinc is the annual diameter increment and D is the Diameter. >treestab > Dinc D Drain [1,] 0.03 26.10 2 [2,] 0.04 13.05 1 [3,] 0.00 24.83 1 [4,] 0.00 15.92 4

Dear list, havig the following matrix "Value" "Class" 13.00 1 12.80 1 11.78 1 11.70 2 11.61 2 11.95 2 11.55 2 12.40 3 11.40 1 12.27 1 12.49 3 11.39 4 11.80 4 12.39 3 12.72 3 12.18 3 11.64 3 11.50 4 12.81 4 11.31 4 11.95 2 12.65 2 11.66 2 12.19 3 12.84 1 11.90 1 11.11 4 12.75 4 how can I

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

Here is a sample of running iostat on a server that has a LUN from a SAN with multiple paths. I am specifying a device list that just grabs the bits related to the multi path device: $ iostat -dxkt 1 2 sdf sdg sdh sdi dm-7 dm-8 dm-9 Linux 2.6.18-371.8.1.el5 (db21b.den.sans.org) 06/20/2014 Time: 02:30:23 PM Device: rrqm/s wrqm/s r/s w/s rkB/s wkB/s avgrq-sz avgqu-sz await

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

Hi Hemant, As I suspected, the code broke when I got to the line: result <- rfm_auto(df, id="user_id", payment ="subtotal_amount", date="created_at") Error in rfm_auto(df, id = "user_id", payment = "subtotal_amount", date = "cr eated_at") : could not find function "rfm_auto" It looks like you are using the hoxo-m/easyRFM

I have a 12 disk ZFS+ volume and this morning tried to look at some data on it and the ''ls'' command just hung. So I ran a ''zpool status'' which also proceeded to hang and return no data. I had to leave for work and when I came home the computer had shut down, which is weird. So I started the machine back up and I was able to run a zpool status, it returned

I'm trying to perform an RFM analysis on the attached dataset, I'm able to get the results using the auto_rfm function but i want to define my own breaks for RFM. as follow r <-c(30,60,90) f <-c(2,5,8) m <-c(10,20,30) but when i tried to define my own breaks i got the identical result for RFM i.e 111 for every ID. please help me with this with working R script so that i can get

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

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