search for: shrunken

Hello, I have binary labels from a nearest shrunken centroids prediction (package pamr). I need to obtain a ROC curve for this test. What is the easiest way to obtain one? Paolo Sonego

I have a windows server with a 250G drive the drive is partitioned as follows. partition 1: dell utility partition partition 2: fat32 windows c: partition partition 3: extended partition partition 5: logical NTFS partitition. NTFS partition was setup with 220G. but only about 18G was being used, so i shrank NTFS down to 50G. now i want to make a drive image to create a HVM domain. how can

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 all, In Tibshirani's PNAS paper about nearest shrunken centroid analysis of microarrays (PNAS vol 99:6567), they used cross validation to choose the amount of shrinkage used in the model, and then test the performance of the model with the cross-validated shrinkage in separate independent testing set. If I don't have the luxury of having independen...

...t;- model.matrix(fit1) %*% fit1$res[-(1:4),"est"] # Predict baseline survival at 5 years y5_Flex_1 <- summary(fit1,type="survival",t=5) y5_Flex <- y5_Flex_1[[1]]$est y5_Flex # Shrinkage factor vanH <- 0.9589255 heuristic_lp_flex = vanH*predf?????? # Now calculate the shrunken model baseline survival prob at 5 years by fixing the shrunken lp as an offset and predicting the baseline survival fit1_shrunk <- flexsurvspline(Surv(rfstime,rfs)~offset(heuristic_lp_flex),k=2,data=rotterdam) The error message is Error in model.matrix.default(forms[[i]], m) : model frame and...

...tried to replace line 3 by ,---- | as.formula(a~b,env=emptyenv()) | or | as.formula(a~b,env=NULL) `---- without the desired effect. Instead adding either ,---- | environment(out$f) <- emptyenv() | or | environment(out$f) <- NULL `---- has the desired effect (i.e. the saved object size is shrunken). unfortunately there is a new problem: ,---- | test <- function(x){ | x <- rnorm(1000000) | out <- list() | out$f <- a~b | environment(out$f) <- emptyenv() | out | } | d <- data.frame(a=1,b=1) | v <- test(1) | model.frame(v$f,data=d) | | Error in eval(expr, envir,...

...he loop (line 137). Depending on whether either of the code chunks at lines 98-114 or lines 115-130 have been executed on that previous occastion, "coefold" may or may not be permuted to the same order as "start" in this moment. Hence, it could happen that "start" is shrunken towards something that doesn't make sense in line 105 or line 122. Relating to point 2) above. Theoretically, during the main loop (lines 60-139) either of the chunks in lines 98-114 or lines 115-130 could be entered. In these chunks "coef" is set to the shrunken "start"...

...oost, logitboost), bagged models (trees, earth, fda), randomforests (randomforest and cforest), rule-based models (Ripper and M5 prime), discriminant models (lda, fda, rda, ssda, slda), kernel methods (lssvm, ksvm, rvm, gausspr), nnet, nnet with initial pca step, multinom, pls, plsda, gpls, nearest shrunken centroids, the lasso, the elastic net, supervised pca, knn, lvq and NaiveBayes. Recent changes include: - Estimation of class probabilities from PLS discriminant analysis using Bayes rule (in addition to softmax) - Added predict.train and predit.list - More lattice plots to visualize resampling r...

Dear R, I have a question concerning quantile regression models. I am using the quantile regression model to test the relationship between abalone and the percentage cover of algae etc at different sites and depths. An example is fit<-rq(abalone~%coversessileinvertebrates+factor(Depth)+factor(Site),tau=0.7) In this model depth is fixed and site is random. My question is, is it possible

...nkage factor using the gcv cross-validation: for (i in seq(1,40,1)) { glmba=gl1ce(resp~a+b+c+d+e, data = w, family = binomial(),bound=i) ecco=round(gcv(glmba,type="Tibshirani",gen.inverse.diag =1e11),digits=3) print(ecco) } #and it gives me 21 with the lowest gcv. #then I determine the shrunken coefficients: >gl1ce( resp ~ a + b + c + d + e, data = w, family = binomial(), bound = 21) Coefficients: (Intercept) a b c d e -4.749816 2.776215 4.342661 8.956583 8.661593 1.264660 Family: Family: binomial...

...oost, logitboost), bagged models (trees, earth, fda), randomforests (randomforest and cforest), rule-based models (Ripper and M5 prime), discriminant models (lda, fda, rda, ssda, slda), kernel methods (lssvm, ksvm, rvm, gausspr), nnet, nnet with initial pca step, multinom, pls, plsda, gpls, nearest shrunken centroids, the lasso, the elastic net, supervised pca, knn, lvq and NaiveBayes. Recent changes include: - Estimation of class probabilities from PLS discriminant analysis using Bayes rule (in addition to softmax) - Added predict.train and predit.list - More lattice plots to visualize resampling r...

...hat exposes the underlying image as a raw file? DFXML has an entry on the Forensics Wiki: http://www.forensicswiki.org/wiki/Category:Digital_Forensics_XML As for your external-to-filesystem data question: I think you got the essential non-file-system data. I can imagine data fragments from past/shrunken file systems, or hidden-data regions that fall outside what's recorded in the partition table. My imagination runs dry there, though. --Alex On Fri, Nov 22, 2013 at 12:56 PM, adrelanos <adrelanos@riseup.net> wrote: > Thank you all for your suggestions! > > Richard W.M. Jon...

Is this better off in the bugtracker? ****Summary:**** Allow windows to be shrunken to 'mindows' individually and have a mixed environment of scaled and full sized windows. Mindows would grow to full size on mouseover rather than being interacted with while small and would be re-positionable. I'd love to have the time and ability to contribute more to this idea, but...

Hello. Slope estimates in lme are shrinkage estimates which pull the OLS slope estimates towards the population estimates, the degree of which depends on the group sample size and the distance between the group-based estimate and the overall population estimate. Although these shrinkage estimates as said to be more precise with respect to the true values, they are also biased. So there is a

Thank you all for your suggestions! Richard W.M. Jones: > I keep meaning to write a comprehensive "virt-diff" tool. I needed it > myself just yesterday. Most interesting. I guess there are two reasons for creating such a tool: just compare the images (show the diff) and/or check for malicious additions in the other image. Did you consider implementing the former or both? Do

...;s random forests for classification and regression RBGL Interface to boost C++ graph lib RColorBrewer ColorBrewer palettes rcompgen Completion generator for R RCurl HTTP request interface rda Shrunken Centroids Regularized Discriminant Analysis Rgraphviz Provides plotting capabilities for R graph objects ROC utilities for ROC, with uarray focus rpart Recursive Partitioning RSQLite...

...and activate the buttons with SPACE etc.. but I couldn't see what I was doing, so it didn't make any difference. I used winetricks to install msxml4, wmp9, and d3dx9. Below I'm including the terminal log, and 3 shots trying it in the virtual desktop mode, and a close up of the little shrunken game screen in the upper left corner. Also, the sound works fine... and the mouse cursor when it's not above the top or bottom gnome panels shows up as the in game mouse cursor, but I couldn't capture that with the screenshot tool. > justin at justin-ubuntu:~/.wine/drive_c/Program Fil...

Hi All, It seems that the question of how may people use (or download) R, and it's packages is one that comes up on a fairly regular basis in a variety of forums (There was also recent thread on the subject on Stack Overflow). A couple of students at UCLA (including myself), wanted to address the issue, so we set up a system to get and parse the cran.stat.ucla.edu APACHE logs every night, and

...stic.org/packages/SHIP The SHIP-package allows the estimation of various types of shrinkage covariance matrices. These types differ in terms of the so-called covariance target (to be chosen by the user), the highly structured matrix which the standard unbiased sample covariance matrix is shrunken towards and which optionally incorporates prior biological knowledge extracted from the database KEGG. The shrinkage intensity is obtained via an analytical procedure. * sifds (0.9) Michael Lundholm http://crantastic.org/packages/sifds A data set with Swedish inflation forecasts and inf...