search for: rfimpute

Displaying 9 results from an estimated 9 matches for "rfimpute".

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2007 Aug 10
1
rfImpute
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.defaul...
2003 Aug 26
1
rfImpute (for randomForest) crashed
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 wanted to send a report to Microsoft (which I chose not to do). When I clicked on ?To see what d...
2008 May 05
1
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 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% 11.49% ntree OOB...
2006 Mar 29
2
missing value replacement for test data in random forest
Hi, In R, how to do missing value replacement for test data in randome forest in the way Breiman decribed. thanks in advance iris
2010 Jun 30
2
anyone know why package "RandomForest" na.roughfix is so slow??
...y. But I hesitate to duplicate a function that is already in the package, since I presume the na.roughfix should be as quick as possible and it should also be well "tailored" to the requirements of random forest. Has anyone else seen that this is really slow? (I haven't noticed rfImpute to be nearly as slow, but I cannot say for sure: my "predict" data sets are MUCH larger than my model data sets, so cleaning the prediction data set simply takes much longer.) If so, any ideas how to speed this up? Thanks!...
2011 Dec 02
2
Imputing data
So I have a very big matrix of about 900 by 400 and there are a couple of NA in the list. I have used the following functions to impute the missing data data(pc) pc.na<-pc pc.roughfix <- na.roughfix(pc.na) pc.narf <- randomForest(pc.na, na.action=na.roughfix) yet it does not replace the NA in the list. Presently I want to replace the NA with maybe the mean of the rows or columns or
2013 Jan 28
1
RandomForest and Missing Values
Dear All, I would like to use a randomForest algorithm on a dataset. The set is not particularly large/difficult to handle, but it has some missing values (both factors and numerical values). According to what I found https://stat.ethz.ch/pipermail/r-help/2005-September/078880.html https://stat.ethz.ch/pipermail/r-help/2007-January/123117.html
2004 Jan 12
0
new version of randomForest (4.0-7)
...r extracting the importance measure. o The predict() method has an option to return predictions by the component trees. o There is a new getTree() function for looking at one of the trees in the forest. o For dealing with missing values in the predictor variables, there are na.roughfix() and rfImpute(), which correspond to the `missquick' and `missright' options in Breiman's V4/V5 code. Both works for classification as well as regression. o There is an experimental bias reduction step in regression (the corr.bias argument in randomForest) that could be very effective for some...
2004 Jan 12
0
new version of randomForest (4.0-7)
...r extracting the importance measure. o The predict() method has an option to return predictions by the component trees. o There is a new getTree() function for looking at one of the trees in the forest. o For dealing with missing values in the predictor variables, there are na.roughfix() and rfImpute(), which correspond to the `missquick' and `missright' options in Breiman's V4/V5 code. Both works for classification as well as regression. o There is an experimental bias reduction step in regression (the corr.bias argument in randomForest) that could be very effective for some...