similar to: About classification methods.

Dear R users, I'm looking for solution about how can I add a package to default load package list. Because, some packages, every time I use the package for analysis. I don't want type "load(package)" every time. On the R instruction, I should change .Rprofile file, but I couldn't find R for Mac. How can I add default load package? Jaeik Cho

Dear All, I'm classifying some data with various methods (binary classification). I'm interpreting the results via a confusion matrix from which I calculate the sensitifity and the fdr. The classifiers are trained on 575 data points and my test set has 50 data points. I'd like to calculate p-values for obtaining <=fdr and >=sensitifity for each classifier. I was thinking about

Hello, supposing that I have two or three clear categories for my data, lets say pet preferece across fish, cat, dog. Lets say most people rate their preference as being mostly one of the categories. I want to do pca on the data to see three 'groups' of people, one group for fish, one for cat and one for dog. I would like to see the odd person who likes both or all three in the

Hello all, This is more a statistical question then an R question, but I am sure it will have an R interpretation to it. If I wish to predict an outcome based on some potential features, I could (in some cases) use either regression or regression-tree. However, if my observations are divided to groups (for example by "subject"), I might then want to model that using a random effect for

Dear all, i searched for some classification methods and I have no glue if i took the right once. My problem: I have a matrix with 17000 rows and 33 colums (genes and patients). The patients are grouped into 3 diseases. No I want to classify the patients and for sure i want to know which rows are more helpful for the classification than others. I tried SVM and random forest. Do you think this

Hi, I am interested to use Kernel Density Estimation on bivariate data for multi-class classification. So far, I have managed to use the 'ks' package to plot the contours of the kernel density estimates based on 8-class training dataset with only 2 variables. However, I do not know how to make use of the Kernel Density Analysis results as input into a classifier (e.g. Bayesian

Dear R-philes, I am plotting ROC curves for several cross-validation runs of a classifier (using the function below). In addition to the average AUC, I am interested in obtaining a confidence interval for the average AUC. Is there a straightforward way to do this via the ROCR package? plot_roc_curve <- function(roc.dat, plt.title) { #print(str(vowel.ROC)) pred <-

Le jeudi 09 f?vrier 2012 ? 15:31 +0200, Kari Ruohonen a ?crit : > Hi, > I am trying to replicate a training of AttributeSelectedClassifier with > CFsSubsetEval, BestFirst and NaiveBayes that I have initially done with > Weka. Now, I am trying to use RWeka in R. > > I have a problem of passing arguments to the CfsSubsetEval, BestFirst > and NaiveBayes. I have first created an

Dear all, I have a couple of short noob questions for whoever can take them. I'm from a very non-stats background so sorry for offending anybody with stupid questions ! :-) I have been using logistic regression care of glm to analyse a binary dependent variable against a couple of independent variables. All has gone well so far. In my work I have to compare the accuracy of analysis to a C4.5

hello, i am so glad to write you. i am dealing now with writing my M.Sc in Applied Statistics thesis, titled " Data Mining Classifiers and Predictive Models Validation and Evaluation". I am planning to compare several DM classifiers like "NN, kNN, SVM, Dtree, and Naïve Bayes" according to their Predicting accuracy, interpretability, scalability, and time consuming etc. I have

Hi, erveryone, I show much thanks to Andy and Matthew on former questions. I now sample only a small segment of a image can segment the image into several classes by RandomForest successfully. Now I have some confusion on it: 1. What is the internal component classifier in RandomForest? Are they the CART implemented in the rpart package? 2. I use training samples to predict new samples. But

Hi All, I wrant to do Random Forest classification. I installed R, randomForest classifier package for R but dont know how to use it. Is there any Open Source Remote sensing application which do RF classification on satellite images? Anyone r has random forest classification example? Any language or package example no problem. Does anyone did it in R? if yes how? I google RF Classification

I am implementing an image classification algorithm using the randomForest package. The training data consists of 31000+ training cases over 26 variables, plus one factor predictor variable (the training class). The main issue I am encountering is very low overall classification accuracy (a lot of confusion between classes). However, I know from other classifications (including a regular decision

Dear r-users, I am looking for an implementation of the Naive Bayes classifier for a multi-class classification problem. I can not even find the Naive Bayes classifier for two classes, though I can not believe it is not available. Can anyone help me? Uschi -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

with pred.pca<-predict(splits[[i]]$pca,trainingData at samples)[,1:nPCs] dframe<-as.data.frame(cbind(pred.pca,class=isExplosive(trainingData,2))); results[[i]]$classifier<-ksvm(class~.,data=dframe,scaled=T,kernel="polydot",type="C-svc", C=C,kpar=list(degree=degree,scale=scale,offset=offset),prob.model=T) and a degree of 5 i get an error of 0 reported by the ksvm

> Hi, Check if the shared directory has the write permission for all. i.e. chmod 777 for the particular directory > Date: Sun, 19 Sep 1999 21:06:36 -0700 > From: Kenny Cho <Kenny.Cho@Corp.Sun.COM> > To: samba@samba.org > Subject: Problem copying files to samba mount. > Message-ID: <37E5B2CC.9ADC9F8A@ha1pal.corp.sun.com> > MIME-Version: 1.0 >

Hi I have a matrix with 30 observations and roughly 30000 variables, each obs belongs to one of two groups. With svm and slda I get into memory troubles ('cannot allocate vector of size' roughly 2G). PCA LDA runs fine. Are there any way to use the memory issue withe SVM's? Or can you recommend any other classification method for such huge datasets? P.S. I run suse 9.1 on a 2G RAM

Hi all, There is now a package available on CRAN that provides an R interface to Leo Breiman's random forest classifier. Basically, random forest does the following: 1. Select ntree, the number of trees to grow, and mtry, a number no larger than number of variables. 2. For i = 1 to ntree: 3. Draw a bootstrap sample from the data. Call those not in the bootstrap sample the

Hi all, There is now a package available on CRAN that provides an R interface to Leo Breiman's random forest classifier. Basically, random forest does the following: 1. Select ntree, the number of trees to grow, and mtry, a number no larger than number of variables. 2. For i = 1 to ntree: 3. Draw a bootstrap sample from the data. Call those not in the bootstrap sample the

Hi, I have a small doubt regarding naive Bayes. I am able to classify the data's properly but i am just stuck up with how to get the probability values for naive bayes. In the case of SVM we have "attr" function that helps in displaying the probability values. Is there any function similar to "attr" in naive Bayes that can be used for displaying the attribute values. my