similar to: Using apply() with a function involving ode()

Hi I want help for "repeat" because it takes a long time repeat{ for (i in 1:n){ probb[i]=sum(Wc[z[,j]>=yb[i]]) } deltab[,b]=rbinom(n,1,probb) if(length(which(is.na(deltab[,b])==T))==0){break} } This code works but takes much computation time. Anyone could help me to find a solution to take a shor time. Thanks Jorge -- View this message in context:

The irr package seems to install correctly: > install.packages("irr") trying URL 'http://cran.us.r-project.org/src/contrib/irr_0.61.tar.gz' Content type 'application/x-tar' length 13848 bytes opened URL ================================================== downloaded 13Kb * Installing *source* package 'irr' ... ** R ** data ** help >>>

Dear R-colleagues, I am quite a newbie to R fighting my stupidity to solve a probably quite simple problem of generating artificial data with defined features. I am conducting a study of inter-observer-agreement in child-bronchoscopy. One of the most important measures is Kappa according to Fleiss, which is very comfortable available in R through the irr-package. Unfortunately medical doctors

Full_Name: Nuno Monteiro Version: 2.3.1 OS: Windows XP HE Submission from: (NULL) (84.9.38.207) I'm using the FEAR library to perform Data Envelopment analysis with a 36,000 obs dataset. The function dea is working fine but then when I try to use the boot.sw98 to come up with some sensitivity analysis I get the following error:

Hi, I have two separate questions relating to the KernSmooth package. I am using the dpik function from the KernSmooth package and receive the error Warning message: In kappam * Gcounts : longer object length is not a multiple of shorter object length I saw an earlier post , but the issue was using the bkde fxn and the person appeared to be using too small of a bandwidth.

Dear List, I'm using glmulti for model averaging in R. There are ~10 variables in my model, making exhaustive screening impractical - I therefore need to use the genetic algorithm (GA) (call: method = "g"). I need to include random effects so I'm using glmulti as a wrapper for lme4. Methods for doing this are available here

Saket Joshi asks: > -----Original Message----- > From: Saket Joshi [mailto:joshi at engr.orst.edu] > Sent: Monday, May 27, 2002 6:15 PM > To: r-help at stat.math.ethz.ch > Subject: [R] fitting a model for enumerated datatypes > > Hi, > > I have used the scan() function to read data from a file as follows: > > x <- scan("myfile",

Hi All, I am using fleiss kappa for inter rater agreement. Are there any know issues with Fleiss kappa calculation in R? Even when I supply mock data with total agreement among the raters I do not get a kappa value of 1. instead I am getting negative values. I am using the irr package version 0.70 Any help is much appreciated. Thanks and Regards M [[alternative HTML version deleted]]

Hi, Given that R reports Type I sums of squares, isn't it a bit anachronistic that it re-orders terms in formulae? > d <- expand.grid(y=rnorm(8), + A=factor(c(1,2)), + B=factor(c(1,2)), + C=factor(c(1,2))) > summary(aov(y ~ A+B+A:B+C,data=d)) Df Sum Sq Mean Sq F value Pr(>F) A 1 8.294e-34 8.294e-34 1.027e-33 1

Hello, I have 50 raters and 180 cases which are rated as malignant, probably malignant, probably benign, and benign. I want to compare all the raters but I want a weighted kappa to penalize differences between malignant and benign more than differences between malignant and probably malignant. I only found the weighted option in the 2-rater functions (kappa2, wkappa, cohen.kappa). The

I have a statistical problem which has given me no end of grief recently, and am posting here in the hope that somebody can give me a straight answer. I'm a IT postgrad, not a statistician, so people may have to speak really slowly and clearly for me to get it :) I am collecting simulation data, and the results are geometrically distributed (or approximately so). From what I can gather

I have 20 variables: 5,9,6,1,5,9,7,4,5,6,3,2,4,8,9,6,1,8,4,8 How do I calculate the corresponding quantiles from a normal distribution with the same mean and variance as the sample? Also, how do I draw a QQ plot of the data? Thanks for any help! -- View this message in context: http://www.nabble.com/Quantiles-and-QQ-plots-tf4925742.html#a14097909 Sent from the R help mailing list archive at

Dear R-colleagues, this is a question from a R-newbie medical doctor: I am evaluating data on inter-observer-reliability in endoscopy. 20 medical doctors judged 42 videos filling out a multiple choice survey for each video. The overall-data is organized in a classical way: observations (items from the multiple choice survey) as columns, each case (identified by the two columns "number of

Hello, after searching through the archives and not finding a thread that answers this question, I thought I'd pass it on to the list. Given a data frame and given a factor variable that assigns a class to each case in the data frame, what is the most efficient way to sample a given number of cases from each class? I've found a roundabout solution that works as follows: for each class:

Does anyone have R functions or library to fit a beta-binomial distribution with glm? Thanks.

I came across a very cheap of the infamous blue book - The New S Language (1988). Is this book still relevant to modern S or R or am I wasting my time? -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the

Dear R-xperts I have just written a little hypergeometric function, included below [the hypergeometric function crops up when solving a common type of ODE]. It works fine on single values of the primary argument z, but vectorizing it is getting confusing. The best I have come up with so far just tests for z being longer than 1 and if so, uses sapply() recursively. This is fine, except that it

Hi all, I study epidemiology of soilborne disease. I have this ode model dS/dt = - (rp(t) X + rs(t) I) * S with X=1 ; rp(t) = ap exp( - bp*t) ; rs(t) = as exp (-0.5 ( ln (t/ds) / bs)? ) The data I have are not directly the infected individuals (which is a hidden state) but the Diseases ones (individuals who show aerial symptoms). I have studied with experiments the relationship between the

Hello All, I'm struggling to solve this ODE using R, vdpol <- function (h, v, t) ( list(c ( -0.1*v/(pi*(2*10*h-h^2)), (v = (-0.1*v/(pi*(2*10*h-h^2))^2) + 2*9.81*h)) )) library(deSolve) yini <- (c(h = 20, v=0)) nonstiff <- ode(y = yini, func = vdpol, times= seq(0, 30, by =

Dear All, I was using rk4 and lsoda to solve a ODE system. However, both of them gave bad accurate solutions, especially compared with Matlab solver ODE45. For example, ODE45 gave solutions that can go to a stable level (about 1.6) when time goes to infinity, however, the solutions from lsoda are decreasing to very very small (about 1e-130) numbers. Does R have more accurate ODE solvers as