similar to: errorest

Hi, I am trying to get an error estimation for a classification done using lda. The examples work fine, however I don't get my own code to work. The data is in object d > d class hydrophobicity charge geometry 1 2 6490.0400 1434.9700 610.99902 2 2 1602.0601 400.6030 -5824.00000 3 2 969.0060 260.1360 -415.00000 4 1

Hi, I am wondering what does "mypredict.lda<-function(object, newdata)predict(object, newdata=newdata)$class" actually do? I run a few errorest commands in the same function on the same dataset using the same classifier lda. The only difference is some use "cv", other use "boot" and "632plus". They all share one mypredict.lda. Will it cause any

Dear all, Does anybody can explain this: different results got when all the same parameters are used in the errorest() in library ipred, as the following? errorest(Species ~ ., data=iris, model=randomForest, estimator = "cv", est.para=control.errorest(k=3), mtry=2)$err [1] 0.03333333 > errorest(Species ~ ., data=iris, model=randomForest, estimator = "cv",

Dear all, can anybody help me with the program below? The function predict.lda seems to be defined but cannot be used by errortest. The R version is 1.7.1 Thanks in advance, Stefan ---------------- library("MASS"); library("ipred"); data(iris3); tr <- sample(1:50, 25); train <- rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3]); test <- rbind(iris3[-tr,,1],

Greetings, I am having trouble calculating artificial neural network misclassification errors using errorest() from the ipred package. I have had no problems estimating the values with randomForest() or svm(), but can't seem to get it to work with nnet(). I believe this is due to the output of the predict.nnet() function within cv.factor(). Below is a quick example of the problem I'm

Dear R-users, I'd like to estimate standard errors (for lda) and make a boxplot with the "632plus" and "boot" error estimators included in package ipred (method: errorest). The "boot" estimator returns only a standard deviation but not the whole error data. Thank you in advance, regards, Antoine

Dear List, I have a question regarding "ipred" package. Under 10-fold cv, for different knn ( = 1,3,...25), I am getting same misclassification errors: ############################################# library(ipred) data(iris) cv.k = 10 ## 10-fold cross-validation bwpredict.knn <- function(object, newdata) predict.ipredknn(object, newdata, type="class") for (i in

Hello! I'm Taeho, a graduate student in South Korea. In order to get .632+ bootstrap error using random forest, I have tried to use 'ipred' package; more specifically the function 'errorest' has been used. Following the guidelines, I made a simple command line like below: error<-errorest(class ~ ., data=data, model=randomForest, estimator = "632plus")$err

This post is NOT a question, but an answer. For readers please disregard all earlier posts by myself about this question. I'm posting for two reasons. First to say thanks, especially to Dimitris, for suggesting the use of errorest in the ipred library. Second, so that the solution to this problem is in the archives in case it gets asked again. If one wants to run a k-fold cross-validation

Full_Name: Noel O'Boyle Version: 2.1.0 OS: Debian GNU/Linux Sarge Submission from: (NULL) (131.111.8.96) (1) Description of error The 10-fold CV option for the svm function in e1071 appears to give incorrect results for the rmse. The example code in (3) uses the example regression data in the svm documentation. The rmse for internal prediction is 0.24. It is expected the 10-fold CV rmse

1. This is _not_ a bug in R itself. Please don't use R's bug reporting system for contributed packages. 2. This is _not_ a bug in svm() in `e1071'. I believe you forgot to take sqrt. 3. You really should use the `tot.MSE' component rather than the mean of the `MSE' component, but this is only a very small difference. So, instead of spread[i] <- mean(mysvm$MSE), you

Sorry for the repeat. The PRESS statistic is defined as sum(y-yhat(i))^2, where yhat(i) denotes the ith predicted value using all the data except the ith case (as used typically in linear models). Thanks again Jacob Jacob L van Wyk Department of Mathematics and Statistics Rand Afrikaans University P O Box 524 Auckland Park 2006 South Africa Tel: +27-11-489-3080 Fax: +27-11-489-2832

Hi, I am using the Random Forest package to classify observations into one of two classes. My data is unbalanced with the minority class accounting for 7% of total data set. I have heard the 10-Fold Cross validation can help me with improving classification. But being new at most of this it's not something I can do from scratch on my own. So I have spent all this morning trying to find a

Hi Everyone, Is there a way to get predict.rpart() to return the nodes reached by the new examples in addition to the predicted probabilities it already returns? In other words, I would like to know the leaf node in the tree object that each new example data drops down to. Thanks in advance for your help. Osei

How to select the number of PLS components for GPLS for data sets with few samples? Concrete problem: My data set: 9 samples of class A and 37 of class B with 254 descriptors. In the paper: "Classification Using Generalized Partial Least Squares", Beiying Ding, Robert Gentleman, Bioconductor Project Working Papers, year 2004, paper 5 Section 2.6 Assessing Prediction: Cite:

Dear all, Can I get the leave-one-out cross validation error of randomForest in R? I only found tune(), which got the 10-fold cross validation error. Thanks for any information. Xin LIU This e-mail is from ArraDx Ltd The e-mail and any files transmitted with it are confidentia...{{dropped}}

Hi Everyone, I am new in using R and I was wondering if anybody knows how to do a leave-one-out cross-validation in R. Thanks Charles Découvrez les photos les plus intéressantes du jour. [[alternative HTML version deleted]]

Can someone help me with performing leave-out-one cross validation using R (model built is a Cox model)? Thanks. --------------------------------------------- David Verbel, MPH Senior Biostatistician Aureon Biosciences 28 Wells Avenue Yonkers, NY 10701 Phone: (914) 377-4021 Fax: (914) 377-4001 --------------------------------------------- [[alternative HTML version deleted]]

How to select training data set and test data set from the original data for performing cross-validation --------------------------------- [[alternative HTML version deleted]]

Dear, I wanna make cross-validation for the species data of species distribution models. Please kindly suggest any package containing cross validation suiting the purpose. Thank you. Elaine [[alternative HTML version deleted]]