similar to: Random Forest %var(y)

Dear Researches, I am using RF (in regression way) for analize several metrics extract from image. I am tuning RF setting a loop using different range of mtry, tree and nodesize using the lower value of MSE-OOB mtry from 1 to 5 nodesize from1 to 10 tree from 1 to 500 using this paper as refery Palmer, D. S., O'Boyle, N. M., Glen, R. C., & Mitchell, J. B. O. (2007). Random Forest Models

Dear R-users, While making a prediction using the randomForest function (package randomForest) I'm getting the following error message: "Error in predict.randomForest(model, newdata = CV) : No forest component in the object" Here's my complete code. For reproducing this task, please find my 2 data sets attached ( http://www.nabble.com/file/p17855119/data.rar data.rar ).

Hello all, I am trying to use the tapply function to sum some values and change the column names of the resulting vector. I input Emp Et 1 10565 ACC 2 7515 ADM 3 625 AGF 4 6243 CNS 5 12721 EDU 6 3924 FIN 7 18140 HLH 8 3686 INF 9 15841 MFG 10 243 MIN 11 1864 MNG 12 4664 OSV 13 5496 PRF 14 4988 PUB 15 2166 REC 16 2153 REL 17 16082 RTL 18 3582 TRN 19 757 UTL 20

Hello All, Please consider the following: TotEmp<-c(19,6,1,1,8,44,2,33,48,1) ClusterType<-c("AGF","CNS","OSV","RTL","RTL","TRN","REL","ACC_CLUST","RTL","WHL") Taz<-c(0,0,0,100,100,100,101,101,102,103)

So im here now b/c im incredibly frustrated. Please consider the following: #Try 1 Data_<-list() Sn<-1:12 for(sn in Sn){ for(i in 1:10){ Data.X <- rnorm(100,0,10) Data_[[paste(sn,i,sep="-")]]<-Data.X } } ##Try 2 Data_<-list() Sn<-1:12 for(sn in Sn){ for(i in 1:10){ Data.X <- rnorm(100,0,10) Data_[[sn]][[i]]<-Data.X } } In Try 1 i am able to append

Dear Researches, I am using RF (in regression way) for analize several metrics extract from image. I am tuning RF setting a loop using different range of mtry, tree and nodesize using the lower value of MSE-OOB mtry from 1 to 5 nodesize from1 to 10 tree from 1 to 500 using this paper as refery Palmer, D. S., O'Boyle, N. M., Glen, R. C., & Mitchell, J. B. O. (2007). Random Forest Models

Hi, I have a question on read.table. I have a dataset with 273,000 lines and 195 columns. I used the read.table to load the data into R: trn<-read.table('train1.dat', header=F, sep='|', na.strings='.') I found it takes forever. then I run 1/10 of the data (test) using read.table again. And this time it finished quickly. So, there might be something wrong in my data

Dear All, I am using randomForest to predict the final selling price of some items. As it often happens, I have a lot of (noisy) historical data, but the question is not so much about data cleaning. The dataset for which I need to carry out some predictions are fairly recent sales or even some sales that will took place in the near future. As a consequence, historical data should be somehow

Hi all, When running a randomForest run using the following command: forestplas=randomForest(Prev~.,data=plas,ntree=200000) print(forestplas) I get the following result: Call: randomForest(formula = Prev ~ ., data = plas, ntree = 2e+05, importance = TRUE) Type of random forest: regression Number of trees: 2e+05 No. of variables tried at each split: 5

Hello, I'm trying to import into R files that contain data downloaded from logger devices as files with the following formats: .act .lig .trj .trn These files are essentially text files but use both tabs and commas as separators. I've tried the function scan: 1) scan("filename.act", what=character(0)) returns only two columns from the original 5 2) scan("copia.act",

Hi all (and Andy!), When running a randomForest run in R, I get the last part of an output (with do.trace=T) that looks like this: 1993 | 0.04606 130.43 | 1994 | 0.04605 130.40 | 1995 | 0.04605 130.43 | 1996 | 0.04605 130.43 | 1997 | 0.04606 130.44 | 1998 | 0.04607 130.47 | 1999 | 0.04606 130.46 | 2000 | 0.04605 130.42 | With the first column representing the

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

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As a follow-up to my email below: The input data frame to nnet() has dimensions: > dim(coreaff.trn.nn) [1] 5088 8 And the predictions from the neural network (35 records are dropped - see email below for more details) has dimensions: > pred <- predict(coreaff.nn1) > dim(pred) [1] 5053 1 So, the following line of R code does not work as the dimensions are

Sorry for these multiple postings. I solved the problem using na.omit() to drop records with missing values for the time being. I will worry about imputation, etc. later. I calculated the sum of squared errors for 3 models, linear regression, neural networks, and support vector machines. This is the first run. Without doing any parameter tuning on the SVM or playing around with the number of

I am exploring neural networks (adding non-linearities) to see if I can get more predictive power than a linear regression model I built. I am using the function nnet and following the example of Venables and Ripley, in Modern Applied Statistics with S, on pages 246 to 249. I have standardized variables (z-scores) such as assets, age and tenure. I have other variables that are binary (0 or 1). In

My P4_BUG.log 01 _ Installed Wine run with -v gives "Wine release 20000430" and error "fixme: pthread_atfork" 02 _ Edited /etc/wine.conf c:\windows\Profiles\Administrator\user.dat must be c:\windows\user.dat 03 _ Trying wine /wine/mnt/c/Programme/Play/vgap/Planets4/planets4.exe as "user" gives errors: - fixme: pthread_atfork - what

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

I have a data in the following form : CIN TRN_TYP 9079954 1 9079954 2 9079954 3 9079954 4 9079954 5 9079954 4 9079954 5 9079954 6 9079954 7 9079954 8 9079954 9 9079954 9 . . . . . . there are 100 types of CIN (9079954,12441087,15246633,...) and respective TRN_TYP first of all, I want this data to be

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]]