similar to: aucRoc in caret package [SEC=UNCLASSIFIED]

I am learning the package "caret", after I do the "rfe" function, I get the error ,as follows: Error in `[.data.frame`(x, , retained, drop = FALSE) : undefined columns selected In addition: Warning message: In predict.lm(object, x) : prediction from a rank-deficient fit may be misleading I try to that manual example, that is good, my data is wrong. I do not know what

Hi, 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

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

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

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

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

I have a data set with 10 variables, and about 8000 instances (or objects/rows/samples). In addition I have one more ('class') variable that I have about 10 instances for, but for which I wish to impute values for. I am a little confused how to go about doing this, mostly as I'm not well-versed in it. Do I train the SOM with a data object that contains just the first 10 variables

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

Dear List, I have two questions about how to do predictions using lrm, specifically how to predict the ordinal response for each observation *individually*. I'm very new to cumulative odds models, so my apologies if my questions are too basic. I have a dataset with 4000 observations. Each observation consists of an ordinal outcome y (i.e., rating of a stimulus with four possible

Hi All, I installed R 2.5.1 recently on a PC (Windows XP Professional 2001) and tried to install some R packages. It took several minutes and gave me the following message. > utils:::menuInstallPkgs() --- Please select a CRAN mirror for use in this session --- Error in open.connection(file, "r") : unable to open connection In addition: Warning message: unable to connect to

Dear developers, I have jutst started working with caret and all the nice features it offers. But I just encountered a problem: I am working with a dataset that include 4 predictor variables in Descr and a two-category outcome in Categ (codified as a factor). Everything was working fine I got the results, confussion matrix etc. BUT for obtaining the AUC and predicted probabilities I had to add

Hi Tobias, Here is something I acquired from this mailing list some years ago. It works well for me: #---run in previous version (e.g. R 3.1.0) packages <- installed.packages()[,"Package"] save(packages, file="Rpackages_R3.1.0") #---run in new version load("Rpackages_R3.1.0") for (p in setdiff(packages, installed.packages()[,"Package"]))

Hi all, I would like create a new column in a data.frame (a1) to store 0, 1 data converted from a factor as below. a1$h2<-NULL for (i in 1:dim(a1)[1]) { if (a1$h1[i]=="H") a1$h2[i]<-1 else a1$h2[i]<-0 } My question: is it possible to remove the loop from above code to achieve the desired result? Thanks in advance, Jin Geoscience Australia Disclaimer: This e-mail

Version 4.63 of the caret package is now on CRAN. caret can be used to tune the parameters of predictive models using resampling, estimate variable importance and visualize the results. There are also various modeling and "helper" functions that can be useful for training models. caret has wrappers to over 99 different models for classification and regression. See the package vignettes

Version 4.63 of the caret package is now on CRAN. caret can be used to tune the parameters of predictive models using resampling, estimate variable importance and visualize the results. There are also various modeling and "helper" functions that can be useful for training models. caret has wrappers to over 99 different models for classification and regression. See the package vignettes

Hi All, Just thought you might be interested in a recently released R package, spm: Spatial Predictive Modelling. It aims to introduce some novel, accurate, hybrid geostatistical and machine learning methods for spatial predictive modelling. Of 22 functions available in spm, two functions are for accuracy assessment. Perhaps they are not only useful tools for spatial predictive modelling

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 +

Hi All, I tried to use xgboost to model and predict count data. The predictions are however not as expected as shown below. # sponge count data in library(spm) library(spm) data(sponge) data(sponge.grid) names(sponge) [1] "easting" "northing" "sponge" "tpi3" "var7" "entro7" "bs34" "bs11"

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

Dear R users, A new version (1.1.0) of the ?spm? package for spatial predictive modelling is now available on CRAN. The introductory vignette is available here: https://cran.rstudio.com/web/packages/spm/vignettes/spm.html There are several new enhancements to the package including a fast version of random forest in using ranger (rg) library(ranger) and the ability to convert relevant