similar to: Cross-validation accuracy in SVM

Hi there - I have been trying to generate some simple stats and save the results to a file. My data looks like this: x y z exp 0 3 5 1 2 11 10 1 4 4 5 1 7 6 4 1 11 1 2 2 5 7 1 2 3 3

Hi there - Is there a way to write statistical models (trees, naïve Bayes, SVM, etc) to a file and import them again without loss of information? Thanks - Ton Ton van Daelen, PhD Director, Application Support Tel: (858) 279-8800 ext 217 Fax: (858) 279-8804 Web: www.scitegic.com Register now for the 2004 Pipeline Pilot user group meeting Jan 28-30 in San Diego:

Dear All, It's been several times I observed a very strange behaviour with R 1.2.0 under Windows NT 4.0 (with sp5): accuracies are lost in many cases. Things are back to normal when I exit R and restart it. Here is an example of my last encounter with this: > bt1 <- c(7.3, 8.1, 8.4, 7, 6, 7.2, 6.7, 8.3, 9, 7.4, 8.6, 6.2, 6.9, 7.6, 5.3, 5.6) > bt1 [1] 7 8 8 7 6 7 6 8 9 7 8 6 6 7 5 5

Hi: I am a newer in using R for data mining, and find the "e1071" pakage an excellent tool in doing data mining work! what frustrated me recently is that when I using the function "svm" and using the "cross=10" parameters, I got all the "accuracies" of the model greater than 1. Isn't that the accuracy should be smaller than 1? so I wander how, the

R 2.10 Windows 7 I am trying to plot three graphs on top of each other. I need to have the axises perfectly aligned. For some reason the ticks on the y axes are slightly off so they do not perfectly align. Can someone tell me how I can get the to overlay each other perfectly? I thought the yaxp parameter would solve my problem, but it does not. My data and code follows: >

R-Helpers, I'm sorry to have to ask this -- I've not used R very much in the last 8 or 10 months, and I've gotten rusty. I have the following (ff2 is a subset of a much, much larger dataset): > ff2 hostName user sys idle obsTime 10142 fred 0.4 0.5 98.0 2007-11-01 02:02:18 16886 barney 0.5 0.2 94.6 2007-10-25 19:12:12 8795 fred 0.0 0.1 99.8

Hi Step 1: I create a data.frame called iolm. Step 2: I create a conditional subset i_wtr. Step 3: In this subset I add 0.3 to all values in the IOLM_AST column. Step 4: Now I am looking for the best way to Œmerge¹ the altered subset back into the original iolm data.frame ## STEP 1 >iolm ID IOLM_AST IOLM_AXIS 1 1 1.15 165.33 2 2 1.20 79.00 3 3 0.40

Dear Sir, I have data, coming from tests, consisting of 300 values. Is there a way in R with which I can confirm this data to 68-95-99.8 rule or three-sigma rule? I need to look around percentile ranks and prediction intervals for this data. I, however, used SixSigma package and used ss.ci() function, which produced 95% confidence intervals. I still am not certain about percentile ranks

https://bugzilla.samba.org/show_bug.cgi?id=1476 abartlet@samba.org changed: What |Removed |Added ---------------------------------------------------------------------------- CC| |abartlet@samba.org Status|RESOLVED |REOPENED Resolution|WORKSFORME | -------

Hi all, I have a dataset of 2000 numbers ( it's noise measured with a scoop ) Now i want to know of my data is normal distributed (Gaussian distribution). I did already: - 68-95-99.7 test - Q-Q-plot and now i used "nortest library" and the Lilli.test() However i don't understad the output? lillie.test(z) Lilliefors (Kolmogorov-Smirnov) normality test data: z D =

Dear R folk: Perhaps I'm just dense today, but I am having trouble reproducing the principal components plotted and summarized by clusplot. Here is a brief example using the pluton dataset. clusplot reports that the first two principal components explain 99.7% of the variability. But this is not what princomp is reporting. I would greatly appreciate any advice. With best regards, -- Tom

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

I believe for rails projects you can put rspec''s code repos into vendor/plugins, and autotest will automatically use the rspec binary in that directory, and not the gem installed. Is there an easy way to do this for non-rails projects? There are a few advancements on trunk which I would like to take advantage of. Tips welcome ; ). Scott Taylor

Hello all, turns out i'm having a bad R week. I am at my wits end with a function that I am trying to write. When I run the lines of code outside of a function, I get the desired output. When I wrap the lines of code into a function it doesn't work as expected. Not sure what is going on here. I suspected that the syntax of the if statement with the for loop was the culprit, but when I

Hello, Here is my network: ------------------ ------------- ----------- Linux box ----------- GRE --------- Cisco ---------- ------------------ ------------- What I wan to accomplish. I want ripv2 to go across (both ways) through the GRE tunnel. No packets are being passed thought the GRE

Hi, I am using lmer (from the package lme4) to predict a binary response variable (REL) from a bunch of fixed effects and two random effects (Speaker_ID and NPhead_lemma): fit <- lmer(REL ~ SPEAKER_GENDER + log(SPEECHRATE) + SQSPEECHRATE + ..... + (1|Speaker_ID) + (1|NPhead_lemma), family="binomial", data=data.lmer, method="Laplace", model=T, x=T) I

Hi Everyone, I've got a question about data representation. I have some psychometric data with 5 scores for 15 different groups. I've been asked to show some kind of mean plots. The data below is the mean and SD for a given group, unfortunately my employer doesn't want me posting full datasets. :( The groups V,W,X,Y,Z are divided into Bottom, (B), Middle (M) and Top (T). An

Hi Duncan, I'm not sure about this: + if (!Accuracy) + // If it's not a floating point number then it must be 'fast'. + return getFastAccuracy(); Since you allow accuracies bigger than 1 in setFPAccuracy(), integers should be treated as float. Or at least assert. Also, I'm thinking you should carry the annotation forward on all uses of an annotated result, or make

If we use the ROCR package to find the accuracy of a classifier pred <- prediction(svm.pred, testset[,2]) perf.acc <- performance(pred,"acc") Do we?find the maximum accuracy?as follows?(is there a simplier way?): > max(perf.acc at x.values[[1]]) Then to find the cutoff point that maximizes the accuracy?do we do the following?(is there a simpler way): > cutoff.list <-

https://bugzilla.mindrot.org/show_bug.cgi?id=1476 Summary: .ssh/known_hosts does not save port number Classification: Unclassified Product: Portable OpenSSH Version: 5.0p1 Platform: All OS/Version: Linux Status: NEW Severity: minor Priority: P2 Component: ssh AssignedTo: unassigned-bugs at