similar to: How are feature weights extracted from 'superpc' analysis?

Hi, I am using the superpc package. By superpc.train (data, type="regression") I calculated the standardized regression coefficients for measuring the univariate effect of each feature on a continuous response y. By superpc.cv(compute.fullcv=TRUE, compute.preval=FALSE, n.components=3, n.fold=10) I used cross validation to estimate the optimal feature threshold and choose only

Hi, Reading help("Documentation"), I'm led to believe that a help call like: ?myFun(x, sqrt(wt)) Will search for help on the appropriate method in the case that myFun is generic. This isn't working for me. Here is an example using the Biobase package: ## If Biobase is not installed source("http://bioconductor.org/biocLite.R") biocLite("Biobase")

Running R2.4.0 on Apple Mac OS X 10.4.8, in Emacs ESS mode, and also R.app. In an attempt to learn a bit more about a particular method (geneNames in package affy) I invoked getMethods("geneNames") which produced geneNames methods, but not the one in affy (output below). I had to know the signature (AffyBatch) in order to find the method > getMethod("geneNames",

On 10/11/2006 2:48 PM, Seth Falcon wrote: > Hi, > > Reading help("Documentation"), I'm led to believe that a help call > like: > > ?myFun(x, sqrt(wt)) > > Will search for help on the appropriate method in the case that myFun > is generic. This isn't working for me. Here is an example using the > Biobase package: > > ## If Biobase is

I'm puzzled by as.matrix. It appears to work differently for Surv objects. Here is a session from my computer: tmt% R --vanilla > library(survival) Loading required package: splines > ytest <- Surv(1:3, c(1,0,1)) > is.matrix(ytest) >[1] TRUE > attr(ytest, 'type') [1] "right" > attr(as.matrix(ytest), 'type') [1] "right" >

The standard errors produced by predict.glm with type = "response" seem wrong. Here is an example using R 1.2 windows version along with the same problem in Splus. The standard errors for type = "link" are the same in both systems. R1.2> set.seed(10) R1.2> ytest <- 100*.95^(0:9) + rnorm(10,sd = 5) R1.2> ytest [1] 103.96964 97.60590 88.43220 85.90504

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 ).

Hi, here are three results of random forest (version 4.0-1). The results seem to be more or less the same which is strange because I changed the classwt. I hoped that for example classwt=c(0.45,0.1,0.45) would result in fewer cases classified as class 2. Did I understand something wrong? Christian x1rf <- randomForest(x=as.data.frame(mfilters[cvtrain,]),

Dear R-help, I was bitten by the behavior of all() when given logical(0): It is TRUE! (And any(logical(0)) is FALSE.) Wouldn't it be better to return logical(0) in both cases? The problem surfaced because some un-named individual called randomForest(x, y, xtest, ytest,...), and gave y as a two-level factor, but ytest as just numeric vector. I thought I check for that in my code by testing

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]]

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

Hello R community, I have cross-posted with r-sig-geo as this issue could fall under either interest group I believe. I just came accross the alphahull package and am very pleased I may not need to use CGAL anymore for this purpose. However, I am having a problem computing alpha shapes with my point data, and it seems to have to do with the spatial configuration of my points (which form

Hello, I came across a problem when building a randomForest model. Maybe someone can help me. I have a training- and a testdataset with a discrete response and ten predictors (numeric and factor variables). The two datasets are similar in terms of number of predictor, name of variables and datatype of variables (factor, numeric) except that only one predictor has got 20 levels in the training

Hi - When using the randomForest function for regression, I get different results for mean-squared error of the predictions depending on whether or not I specify to calculate variable importance. There is an example below. I looked briefly at the source code, but couldn't find anything that would indicate why calculating variable importance would (or should) change predictions. I'm

Are the results dramatically different? The result would be expected to be somewhat different, as setting importance=TRUE would make many calls to the random number generator (for permuting OOB data in each variable), making all but the first tree in the forest different than if importance=FALSE. Cheers, Andy > From: Scott Gilpin > > Hi - > > When using the randomForest

Hello, I want to use nnet package in R, to train and simulate a NN and get the value of MSE. I am reading in a file which has 19 input variables and one output variable and has a total of 2000 observations. The first column in the file is a column just for giving the serial numbers of the observations. I have already read in the file and also extracted the different values into the matrices to

Hello, I basically want to use R-help, and post some problems which I am facing. The Ref is a well known Genome Biology paper "Bioconductor: open software development for computational biology and bioinformatics" by Robert C Gentleman et al., 2004. Generating Heatmaps till Fig2 is working so I think esetSel is not the problem.. However, for generating the Figure 3, for GO annotations the

Dear mailing list, I have a C function that gives me a wrong result when I run it under Windows 7. This is the code under Linux (RHEL5): > library(phenoTest) > data(epheno) > sign <- sample(featureNames(epheno))[1:20] > score <- getFc(epheno)[,1] > head(score) 1007_s_at 1053_at 117_at 121_at 1255_g_at 1294_at -1.183019 1.113544 1.186186 -1.034779 -1.044456

Hi, there: I spent some time on this but I think I really cannot figure it out, maybe I missed something here: my data looks like this: > dim(trn3) [1] 7361 209 > dim(val3) [1] 7427 209 > mg.rf2<-randomForest(x=trn3[,1:208], y=trn3[,209], data=trn3, xtest=val3[, 1:208], ytest=val3[,209], importance=T) my test data has 7427 observations but after prediction, > dim(mg.rf2$votes)

Hello, does any one know how to interpret this output in R? > Classification with logitboost > fit <- logitboost(xlearn, ylearn, xtest, presel=50, mfinal=20) > summarize(fit, ytest) Minimal mcr: 0 achieved after 6 boosting step(s) Fixed mcr: 0 achieved after 20 boosting step(s) What is "mcr" mean? Thanks [[alternative HTML version deleted]]