similar to: Plotting Comparisons with Missing Data

Hi everyone. I have these data: probClass<-seq(0,0.9,0.1) prob1<-c(0.0070,0.0911,0.1973,0.2949,0.3936,0.5030,0.5985,0.6869,0.7820,0.8822) prob2<-c(0.0066,0.0791,0.2358,0.3478,0.3714,0.3860,0.6667,0.6400,0.7000,1.0000) # which I'm plotting as follows: plot(probClass,prob1,xlim=c(0,1),ylim=c(0,1),xaxs='i',yaxs='i',type="n") lines(probClass,prob1)

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

Folks: I thought the following excerpt from Bruce McCullough's post would be a good candidate for the R fortunes package -- except that it's about Excel, not R! So I nominate it... but leave it to others to say whether it's really "qualified" to be nominated. ---- "The idea that the Excel solver "has a good reputation for being fast and accurate" does not

The idea that the Excel solver "has a good reputation for being fast and accurate" does not withstand an examination of the Excel solver's ability to solve the StRD nls test problems. Solver's ability is abysmal. 13 of 27 "answers" have zero accurate digits, and three more have fewer than two accurate digits -- and this is after tuning the solver to get a good

>> Yes, I'd strongly suggest implementing one of the approaches based >> on the >> chrec analysis we have in the scev pass. >> > > Yup, just get the array accesses under scev form, and set a constraint > system from these. Once you set up the constraint systems for the > dependence distance vectors, you're almost done. You can look at how > this

Hi, I can't seem to get any Excel Add-Ins to work. In particular, I need the 'Solver' for an assignment ... I've tried with two office versions (2007/2003), which I've managed to install successfully (you have to enable the Solver add-in during installation), but it's the same every time. When I try to start it up I get a message saying can't access SOLVER.XLA, and

Dear guys, I've put the puzzle solver running on LLVM 2.1. Well, at least partially, for it is failing three of SPEC2000 benchmarks. I will try to debug it now. The results are not as good as before. I mean, the puzzle solver is still the same, but the default allocator is producing very good code now. Even though, the puzzle solver produces faster code for half the benchmarks. It

Hello -- The question I have is about the gmm() function from the 'gmm' package (v. 1.4-5). The manual accompanying the package says that the gmm() function is programmed to use either of four numerical solvers -- optim, optimize, constrOptim, or nlminb -- for the minimization of the GMM objective function. I wonder whether there is a way to pass controls to a solver used while calling

Just thought I'd ask. For those who've never seen TK!Solver, I strongly recommend taking a look. So far as I can tell, it's the only product of its type available, retail or open source, for any platform. What makes TK!Solver so cool is that it adaptively back-solves pretty much any unknown from any set of equations you give it. (or vector full of unknowns, producing a vector of

This is applicable to either using optim or the differential equation solver or any similar solver Suppose I want to use the differential equation solver and this is my code d<-y[2] vdpol<-function(t,y) { list(c(1, d, 3, 4 ) } stiff<-ode(y=rep(0,4),times=c(0,1),func=dvdpol,parms=1) The thing is I want d to be composed of one of state variables in the

Hi all, i'm trying to find a solver possibility analogous to the Excel Solver in R. Since i just started with R, I have only little knowledge. Can someone help me by solving the problem? I have the following 'starting position': z = rnorm(1,0,1) y <- function(x,z){2*x - 1 + z} I am looking for a certain "x" in such a way that the result of the function 'y'

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

Just wondering if there's an R package which does tricks similar to what TK!Solver does. TK!Solver, for those not lucky enough to have found it, basically allows one to define a bunch of equations, assign values to an arbitrary subset of the variables, from which it calculates (either directly when possible or back-solving when not) the values of the other variable(s). thanks Carl