search for: trainy

...i, I have a question about using lm on matrix, have to admit it is very trivial but I just couldn't find the answer after searched the mailing list and other online tutorial. It would be great if you could help. I have a matrix "trainx" of 492(rows) by 220(columns) that is my x, and trainy is 492 by 1. Also, I have the newdata testx which is 240 (rows) by 220 (columns). Here is what I got: py <- predict(lm(trainy ~ trainx ), data.frame(testx)) Warning message: 'newdata' had 240 rows but variable(s) found have 492 rows The fitting formula I intended is: trainy ~ trainx[,1...

Hi all, I used the following code and data to get auc values for two sets of predictions: library(caret) > table(predicted1, trainy) trainy hard soft 1 27 0 2 11 99 > aucRoc(roc(predicted1, trainy)) [1] 0.5 > table(predicted2, trainy) trainy hard soft 1 27 2 2 11 97 > aucRoc(roc(predicted2, trainy)) [1] 0.8451621 predicted1: 1 1 2 2 2 1 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2...

Sir, This query is related to randomForest regression using R. I have a dataset called qsar.arff which I use as my training set and then I run the following function - rf=randomForest(x=train,y=trainy,xtest=train,ytest=trainy,ntree=500) where train is a matrix of predictors without the column to be predicted(the target column), trainy is the target column.I feed the same data for xtest and ytest too as shown. On verifying I found, rf$mse[500] and rf$test$mse[500] are different(the r-sq...

Hi all, I would like to do cross validation in random forest using rfcv function. As the documentation for this package says: rfcv(trainx, trainy, cv.fold=5, scale="log", step=0.5, mtry=function(p) max(1, floor(sqrt(p))), recursive=FALSE, ...) however I don't know how to build trianx and trainy for my data set, and I could not understand the way trainx is built in the package documentation example for iris data set. Here is...

Hi all, I would like to do cross validation in random forest using rfcv function. As the documentation for this package says: rfcv(trainx, trainy, cv.fold=5, scale="log", step=0.5, mtry=function(p) max(1, floor(sqrt(p))), recursive=FALSE, ...) however I don't know how to build trianx and trainy for my data set, and I could not understand the way trainx is built in the package documentation example for iris data set. Here is my...

...ng I try to that manual example, that is good, my data is wrong. I do not know what reanson? my code is : subsets<-c(1:5,10,15,20,25) ctrl<-rfeControl(functions=lmFuncs, method = "cv", verbose=FALSE,returnResamp="final") lmProfile<-rfe(trainDescr,trainY,sizes=subsets,rfeControl=ctrl) before it, I have do some pre-process and my data is in the attachment. Please help me. thank you! kevin http://n4.nabble.com/file/n996068/trainDescr.txt trainDescr.txt http://n4.nabble.com/file/n996068/trainY.txt trainY.txt -- View this message in context: htt...

Any responds?! On Wednesday, August 23, 2017 5:50 AM, Elahe chalabi via R-help <r-help at r-project.org> wrote: Hi all, I would like to do cross validation in random forest using rfcv function. As the documentation for this package says: rfcv(trainx, trainy, cv.fold=5, scale="log", step=0.5, mtry=function(p) max(1, floor(sqrt(p))), recursive=FALSE, ...) however I don't know how to build trianx and trainy for my data set, and I could not understand the way trainx is built in the package documentation example for iris data set. Here is...

...ent plots of the som at this point show some interesting features to me, but are quite difficult to interpret. # there's much work needed here to understand it, but for now I want to see if it's possible to impute values for another variable... # here's where I lose it, missing values, trainY, don't get it. bw.predict <- predict(bw.som, newdata=scale(bw), trainX=???, trainY=???) Ben. [[alternative HTML version deleted]]

All, The kohonen predict function is returning NA for SOM predictions regardless of data used... even the package example for a SOM using wine data is returning NA's Does anyone have a working example SOM. Also, what is the purpose of trainY, what would be the dependent data for an unsupervised SOM? As may be apparent to you by my questions, I am very new to kohonen maps and am very grateful for any assistance offered. Thanks in advance! Below is terminal output: > sessionInfo() R version 2.10.1 (2009-12-14) i386-pc-mingw32 locale:...

Hi, at the moment I use "logon script = scripts\%G.bat", and have two scripts for my two main user groups. Now there are some special users which should have different scripts, so I thought, if there is a way of adding an option to use two login scripts; e.g. if there is an user script, use it, if not than use the group script. Or do I have to maintain that "problem" by

Hi, I am trying to construct a svmpoly model using the "caret" package (please see code below). Using the same data, without changing any setting, I am just changing the seed value. Sometimes it constructs the model successfully, and sometimes I get an ?Error in indexes[[j]] : subscript out of bounds?. For example when I set seed to 357 following code produced result only for 8

Hi, I have a fairly simple linear regression using the lm function. There are about 100 variables and 30,000 rows of data. It runs fine and produces a decent looking R2 value. I'm interested in performing a stepwise variable selection to see if things can be cleaned up a bit. Calling the step function returns ONE iteration (all the variables) and then stops. No errors are reported.

I'm using caret to assess classifier performance (and it's great!). However, I've found that my results differ between R2.* and R3.* - reported accuracies are reduced dramatically. I suspect that a code change to kernlab ksvm may be responsible (see version 5.16-24 here: http://cran.r-project.org/web/packages/caret/news.html). I get very different results between caret_5.15-61 +