similar to: calculating area between plot lines

Hi, I'm new to R and I've run into a problem that I'm not really sure how to express properly in the language. I've got a data table that I've read from a file containing some simple information about the performance of 4 algorithms. The columns are the name of the algorithm, the problem instance and the resulting score on that problem (if it wasn't solved I mark that

I care a lot about R's speed. So I decided to give REvolution's R (http://revolution-computing.com/) a try, which bills itself as an optimized R. Note that I used the free version. My machine is a Intel core 2 duo under Windows XP professional. The code I run is in the end of this post. First, the regular R 1.9. It takes 2 minutes and 6 seconds, CPU usage 50% Next, REvolution's R.

First, I love R and am grateful to be using this free and extremely high quality software. Recently I have been comparing two algorithms and naturally I programmed in R first. It is so slow that I can almost feel its pain. So I decided to do a comparison with Java. To draw 500,0000 truncated normal, Java program takes 2 second and R takes 72 seconds. Not a computer science major, I find it hard

Hello. I have yet to receive a response to my previous post, so I may have done a poor job asking the question. So, here is the general question: how can I run a run a multivariate (more than one non-independent, response variables) ordered probit regression model? I've had success doing this in the univariate case using the vglm() function in the VGAM package. For example:

Dear all, I would like to create a code for semiparametric Klein and Spady's estimator. For that I created a function that provides the log-likelihood function for each observation (so it is a function of betas and i, where i denotes the observation). Now, in order to maximize the log-likelihood function, I have to sum these log-likelihood functions for each i and so to get another function

How does one ideally handle and display multidimenstional contingency tables in R v. 2.6.2? E.g.: > prob1<- data.frame(victim=c(rep('white',4),rep('black',4)), + perp=c(rep('white',2),rep('black',2),rep('white',2),rep('black',2)), + death=rep(c('yes','no'),4), count=c(19,132,11,52,0,9,6,97)) > prob1 victim perp

Hello I have a rather simple code and for some reason it produces an error message. If someone can tell me why and how to fix it, I would be very greatful. Thank you in advance. ##### create data set.seed(10) n <- 200 # number of training points n.test <- 200 # number of test points p<-2 # dimension of input space z <-

Sorry, I pasted the wrong file in earlier... this is the correct one: pValCalculator(b, n=20, m=20) { ind <- 1:min(c(n,m)) prob <- (1-pnorm(b,sd=std*sqrt(ind))) prob1 <- sum((n-ind+1)*(m-ind+1)*prob) prob1 } inputData <- scan("/users/lvssso/projects/LAMA/output/pValLamaScore.tmp", list(block1 = "",block2 = "",width1 = 0,width2 = 0,alignment = 0,score

>>>>> jing hua zhao >>>>> on Mon, 24 Jun 2019 08:51:43 +0000 writes: > Hi All, > Thanks for all your comments which allows me to appreciate more of these in Python and R. > I just came across the matrixStats package, > ## EXAMPLE #1 > lx <- c(1000.01, 1000.02) > y0 <- log(sum(exp(lx))) > print(y0) ## Inf

I managed to solve the problem myself without using this code. thx 2011-11-24 12:26 keltezéssel, Kehl Dániel írta: > Dear Community, > > I am trying to write code for the following problem. > Lets assume we have a beta distribution. > I know one quantile, lets say, 10% of the mass lies above .8, that is > between .8 and 1. > In addition, I know that the average of this

Dear R-list, Hi everyone, Im trying to make an analysis of multidimensional contingency tables using R. I' working with the Agresti example where you have the data from 3 categories. The thing is how can I do the analisys using the G2 statistics. Somebody can send me an Idea? I attach the program where you can find the data. Best Regards, > prob1<-

Hello I have a rather simple code and for some reason it produces an error message. If someone can tell me why and how to fix it, I would be very greatful. Thank you in advance. ##### create data set.seed(10) n <- 200 # number of training points n.test <- 200 # number of test points p<-2 # dimension of input space z <-

Hi, Sorry to bother you guys with this syntax problem, this is my first R program, and I seem to not have grasped something right. I keep getting an error message from 'parse' on the line with the *** : pValCalculator(b, n=20, m=20) { ind <- 1:min(c(n,m)) prob <- (1-pnorm(b,sd=std*sqrt(ind))) prob1 <- sum((n-ind+1)*(m-ind+1)*prob) prob1 } inputData <-

Dear Professor Lumley I am relatively new to using R and also to logistic regression. We have analysed our Dudley Health Survey using the survey package. I am now trying to look at associations using svyglm but I am unsure of how to interpret the output and present the resulting model or whether there are any other things I should do to check the validity of the model. Below is an example of