similar to: ranking predictive features in logsitic regression

Dear friends. I have a paper (details below) examining the risk of renal failure after an operation. A logistic regression was done, and the coefficients to two regressors (age and creatinine) plus intercept with standard errors are given. These coefficients must be dependent in estimation, and when no details are given, I thought how I could most informatively get an impression as to how

Hello Sir, I am a third-year student at the Department of mathematics at IIT Kharagpur. I have good experience in Information Retrieval and Machine Learning. I have read many chapters of the book Introduction to Information Retrieval. Recently I am doing a project on tagging a question on a Q&A Forum using ranking the tags and probabilistic inference. I also have software development

Hey Everyone, Im fresh new in R, and Im supposed to write a code to give me a correlation between two rankings. So I have two ranking lists, which contain file names, e.g.: Ranking list 1: file1.java file3.java file2.java Ranking list 2: fiile2.java file4.java file1.java I need to see how much are these two ranking lists are alike, get a correlation between them. I dont even know where to

Hey Everyone, Im fresh new in R, and Im supposed to write a code to give me a correlation between two rankings. So I have two ranking lists, which contain file names, e.g.: Ranking list 1: file1.java file3.java file2.java Ranking list 2: fiile2.java file4.java file1.java I need to see how much are these two ranking lists are alike, get a correlation between them. I dont even know where to

I have a small matrix where the columns represents a ranking and the values are the number of times each ranking was obtained eg 1 2 3 x 1 2 0 y 0 1 2 z 2 0 1 I'd like to be able to return an average of the ranking obtained average x 1.67 y 2.67 z 1.67 Whats the nicest way to do this? I'm new to the language and looking for an elegant solution :) Thanks Ben

Hello, This is not exactly an R question, but I suspect that there is an R procedure that does what I am calling (for lack of a better name) "histogram ranking". I'm trying to evaluate a set of regression features by segregating by target class and comparing the feature histograms. My idea is that if the histograms are the same for two different classes then there is no

Hi, I am doing predictive modelling of Multivariate Time series Data of a Motor in R using various models such as Arima, H2O.Randomforest, glmnet, lm and few other models. I created a function to select a model of our choice and do prediction. Model1 <- function(){ .. return() } Model2 <- function(){ ... return() } Model3 <- function(){ ... return() } main <-

Dear Sirs and madams :-) I am trying to teach myself multidimensional scaling. To that effect I have collected a survey asking people to rank 10 philosophers and politicians according to their preference. I have collected 61 answers. The data is organized in ten columns and 61 rows. the columns are "choice_1", "choice_2", "choice_3" etc. The cells is the name of the

Dear developeRs, with R 2.4.1 (and also 2.4.0), the function str() fails on objects of class relimplmbooteval, if there are unused slots, which is very often the case. I am not sure whether this is a bug in str() or a correct behavior of str() that unmasks some sloppiness in my usage of S4 classes (that I am not aware of)? Reproducible example (package relaimpo needed): The program

I want to make a glm and then use predict. I have a fairly small sample (4000 cases) and I want to train on 90% and test on 10% but I want to do it in slices so I test on every 10th case and train on the others. Is there some simple way to get these elements? Stephen -- 21/10/2005 [[alternative HTML version deleted]]

Hello, I want to discuss my idea on ranking in IR system which I think can be good extension to Xapian. If I am not too late to discuss it then please consider it. I first give you brief background of me, I am a Masters student working on my thesis in the Information Retrieval. I today only got a mail from one of the professor from Europe whom i am going to join for Ph.D about GSoC and more

Congratulations to all the CentOS folding at home team members for breaking through the 200 world ranking barrier. Its taken a bit of time but we've all helped to do it. Our next target is obviously to achieve 150th in the world ranking. But, to help achieve that we're going to need more members and more machines. So, if you think it sounds interesting and you want to learn more, then

Hi all, I am trying to create simulated data for exploring reclassfication measures in a logistic setting with two continuous predictors and I would like to set the average population probability of outcome rather than the logistic beta0. Is there a way to find a beta0 that will generate the desired average population probability of outcome given x,y and their odds ratios? #Here is an

Hi I can get all my features by doing this: > logistic.model = glm(similarity ~ ., family=binomial, data = cData[3001:3800,]) I can get the product of all my features by this: logistic.model = glm(similarity ~ . ^ 2, family=binomial, data = cData[3001:3800,]) I don't seem to be able to get polys by doing this: logistic.model = glm(similarity ~ poly(.,2), family=binomial, data

Here's a more general question following up on the specific question I asked earlier: Can anybody recommend an R command other than mle.aic() (from the wle package) that will give back a ranked list of submodels? It seems like a pretty basic piece of functionality, but the closest I've been able to find is stepAIC(), which as far as I can tell only gives back the best submodel, not a

Kevin Burton has posted about poor ranking in Lucene preferring shorter documents over longer ones[1]. A similar search in Xapian returns documents in the expected order: Performing query `Xapian::Query(foo)' 3 results found ID 3 99% [foo foo foo] ID 2 94% [foo foo] ID 1 80% [foo] Anyone know what Lucene is doing here? Their FAQ doesn't mention what weighting scheme they use, and I

Hello, I am Mohiuddin Abdul Qader, final year student from dept of CSE in Bangladesh University of Engineering & Technology(BUET). My major was artificial intelligence & i finished my course on Machine Learning and Pattern Recognition this year. I am very keen to contribute in open source community. I have just completed my thesis on 'Location Based Structured Web Search'. For the

Hi Say I have some data, two columns in a table being a binary outcome plus a predictor and I want to plot a graph that shows the percentage positives of the binary outcome within bands of the predictor, e.g. Outcome predictor 0 1 1 2 1 2 0 3 0 3 0

I'm trying to run the generalized least square approach for my 2-additive problem, unfortunately this error appeared. I have tried to figure out the error from the mailing list but couldn't find the solution. Any help is highly appreciated. This is my source code: >a1 <- c(76.18, 61.84, 60.4, 69.09) > a2 <- c(51.01, 50.39, 87.62, 52.03) > a3 <- c(80.08, 48.49, 90.86,

I would like to make a ranking operation more efficient if possible. The goal is to rank a set of points representing objective function values such that points which are "dominated" by no others have rank 1, those which are dominated by one other point have rank 2, etc. In the example with two dimensions below, objective functions 1 and 2 are to be minimized. Points a-e are