similar to: Changing the classification threshold for cost function

Hi, I am working on problem 2 of Chapter 8 in Data Analysis and Graphics Using R and don't know how to approach the second half of the question: In the data set (an artificial one of 3121 patients, that is similar to a subset of the data analyzed in Stiell et al., 2001) head.injury, obtain a logistic regression model relating clinically.important.brain.injury to other variables. Patients

Hi , everybody !!! I want to perform a cost-sensitive classification using the rpart as a base classifier . Is it possible ? Nissim -- View this message in context: http://r.789695.n4.nabble.com/Cost-sensitive-classification-tp3668749p3668749.html Sent from the R help mailing list archive at Nabble.com.

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

I recently use gbm for a binary classification problem. As expected, it gets very good results, based on Area under ROC with 7-fold cross validation. However, the application (malware detection) is cost-sensitive, getting a FP (classify a clean sample as a dirty one) is much worse than getting a FN (miss a dirty sample). I would like to tune the gbm model biased to very low FP rate. For this

Hello, I am just a beginner of R 1.9.0. I try to construct a predictive score for the development of liver cancer in cirrhotic patients. So dependant variable is binanry (cancer yes or no). Independant variables are biological data. The aim is to find out a cut-off value which differentiate (theoratically) from normal to pathological state for each biological data. How can I step in procedue to

Dear all, My question might be more of a statistics question than a question on R, although it's on how to apply the 'quantreg' package. Please accept my apologies if you believe I am strongly misusing this list. To be very brief, the problem is that I have data on only a random draw, not all of doctors' patients. I am interested in the, say, median number of patients of

Hello! The code below works - if you run it you'll see a stacked area chart generated based on the data example. I only have one understanding question about the legend location (the very last snippet of code): legend(par()$usr[2], mean(par()$usr[3:4]), rev(order.of.vars), xpd=T, bty="n", pch=15, col=all.colors[rev(order.of.colors)]) I see that par()$usr[2] = 14763.72

Hello! My code below creates a data frame and a plot for it. However, I can't figure out how to add a legend that is not ON the plot itself, but outside of it (e.g., to the right of my graph or below it). I tried something: I put a line par(xpd=T, mar=par()$mar+c(0,0,0,4)) right before my plot command), but that screwed up all my gridlines - they covered all graph and do not coincide with

Hello, everybody! I have a code below that creates a data set and then a stacked bar chart based on that data set. No need to look at it - just notice please that my horizontal axis is a date varible (x=my.data$date). I have a question about the last 2 lines of this code: grid(nx=NULL,ny=NULL,col = "lightgray", lty = "dotted",lwd = par("lwd")) axis(1, las = 2) Could

Dear R-ers! Asking for your help with building the stacked area chart for the following simple data (several variables - with date on the X axis): ### Creating a data set my.data<-data.frame(date=c(20080301,20080402,20080503,20090301,20090402,20090503,20100301,20100402,20100503), x=c(1.1,1.0,1.6,1,2,1.5,2.1,1.3,1.9),y=c(-4,-3,-6,-5,-7,-5.2,-6,-4,-4.9),

Dear R-list! I am using the e1071 package in R to solve a binary classification problem in a dataset of round 180 predictor variables (blood metabolites) of two groups of subjects (patients and healthy controls). I am confused regarding the correct way to assess the classification accuracy of the trained svm. (A) The svm command allows to specificy via the 'cross=k' parameter to specify a

Hello! If you run the whole code below, it'll produce a stacked diagram. And it looks good - because the tick-marks are aligned with the grid. However, if I stretch the graph window, grid becomes misaligned with the tickmarks. Or, rather, it seems aligned for the first and the last tick mark, but not for tickmarks in between. Can it be addressed? Thank you! Dimitri ### Creating a data set

On 28 June 2013 19:45, Chris Matthews <chris.matthews at apple.com> wrote: > Given this tradeoff I think we want to tend towards false positives (over > false negatives) strictly as a matter of compiler quality. > False hits are not binary, but (at least) two-dimensional. You can't say it's better to have any amount of false positives than any amount of false negatives

Dear R Helpers I am having difficulty understanding how to use the penalty matrix for the nomROC function in package 'nonbinROC'. The documentation says that the values of the penalty matrix code the penalty function L[i,j] in which 0 <= L[i,j] <= 1 for j > i. It gives an example that if we have an ordered response with 4 categories, then we might wish to penalise larger

Hi, I'm not used to thinking along these lines, and wanted to ask your advice: Suppose you have a sample of around 100, consisting of patients according to doctors, in which patients and doctors are given a questionnaire with categorical responses. Each patient somehow has roughly 3 doctors, or 3 rows of data. The goal is to assess by category of each question or DV the agreement between

I should describe the cost of false negatives and false positives, since I think it matters for how this problem is approached. False negatives mean we miss a real regression --- we don’t want that. False positives mean somebody has to spend some time looking at and reproducing the regression when there is not one --- bad too. Given this tradeoff I think we want to tend towards false positives

Hi, I have a scenario that I am failing to implement using the Queue app, but which I had thought would be commonplace... 1) (this bit works fine) I want a queue caller to have access to the basic set of agents initially, with an overflow to additional agents if they are busy - This is done using penalty: queues.conf: member => SIP/dev1,0,Agent1 member => SIP/dev2,0,Agent2 member =>

I'm looking for a function in R that extends kappa to multiple raters when there is more than one response per subject. For example, say a group of doctors have to assign diseases to patients. Each patient will be assigned one to many diseases, and the number of doctors assigning diseases to any one patient will be two to many. Here's an extremely simple example of the type of data I

R friends, I am using R 2.1.0 in a Win XP . I have a problem working with lists, probably I do not understand how to use them. Lets suppose that a set of patients visit a clinic once a year for 4 years on each visit a test, say 'eib' is performed with results 0 or 1 The patients do not all visit the clinic the 4 times but they missed a lot of visits. The test is considered positive if it

All, Appreciate any leads on the following: In a recent blind-validation study of a depression screening instrument we used a two-stage sampling design. In stage 1, we used a broad paper-and-pencil screen to identify likely positives (say 30% of entire sample). In stage 2 we conducted in-depth interviews with the 30% of likely positives plus another 20% of the negatives as controls.