similar to: Stochastic Frontier: Finding the optimal scale/scale efficiency by "frontier" package

Hi! A few weeks ago, I have uploaded a new R package "frontier" for Stochastic Frontier Analysis (SFA) [1] to CRAN [2]. It includes the FORTRAN code of Tim Coelli's [3] software "Frontier 4.1" [4]. Hence, the R package "frontier" should have the same capabilities as "Frontier 4.1", i.e. Maximum Likelihood Estimation of Stochastic Frontier Production

Hi! A few weeks ago, I have uploaded a new R package "frontier" for Stochastic Frontier Analysis (SFA) [1] to CRAN [2]. It includes the FORTRAN code of Tim Coelli's [3] software "Frontier 4.1" [4]. Hence, the R package "frontier" should have the same capabilities as "Frontier 4.1", i.e. Maximum Likelihood Estimation of Stochastic Frontier Production

I am trying to use the CART in a survival analysis. I have three variables of interest (all 3 ordinal - x, y and z, each of them with 5 categories) from which I want to make smaller groups (just an example 1st category from X variable with the 2nd and 3rd categories from the Y category and 2, 3 and 4 categories from the Z category etc) based on their, let's say, association with mortality. Now

Dear R users, I would like to inform you that everything of the "micEcon" package that is related to Stochastic Frontier Analysis (SFA) has been moved to the "frontier" package, because this is a more appropriate place for the functions "front41WriteInput", "front41ReadOutput", and "front41Est", and the corresponding (S3) methods. The data

Dear R users, I would like to inform you that everything of the "micEcon" package that is related to Stochastic Frontier Analysis (SFA) has been moved to the "frontier" package, because this is a more appropriate place for the functions "front41WriteInput", "front41ReadOutput", and "front41Est", and the corresponding (S3) methods. The data

Dear all, I want to program my own models about Stochastic Frontier Analysis and Linear programming (Data Envelopment Analysis). In this context, is there anyone that may help me with some simple tutorials and scripts about these issues? Thanks a lot. -- Marcus Vinicius Pereira de Souza, Prof. [[alternative HTML version deleted]]

Dear current (and future) users of the "frontier" package, We are approaching the first stable version (1.0) of the frontier package, which provides tools for microeconomic Stochastic Frontier Analysis (SFA). I have uploaded a kind of beta release (version 0.99) of this package to CRAN. The most important differences to version 0.9 affect the user interface. I have modified the

Dear current (and future) users of the "frontier" package, We are approaching the first stable version (1.0) of the frontier package, which provides tools for microeconomic Stochastic Frontier Analysis (SFA). I have uploaded a kind of beta release (version 0.99) of this package to CRAN. The most important differences to version 0.9 affect the user interface. I have modified the

I need to estimate maximum tree crown radius and am looking for a package to prepare stochastic frontier models in R. I have not found any package references on Nabble R help, google, or R help. Any tips on a package for this? With regards, Aaron Trowbridge Researcher BV Research Centre Smithers B.C. -- View this message in context:

Hi all I'm using the function bayesx to estimate a simple model, for example: library(R2BayesX) ## generate some data set.seed(111) n <- 200 ## regressor dat <- data.frame(x = runif(n, -3, 3)) ## response dat$y <- with(dat, 1.5 + sin(x) + rnorm(n, sd = 0.6)) ## estimate models with ## bayesx REML and MCMC b1 <- bayesx(y ~ sx(x), method = "REML", data = dat) I want

Dear all, I want to finished my paper by latent class Stochastic Frontier Analysis , but i can not find the package, is there anyone that may help me Thanks a lot. [[alternative HTML version deleted]]

Dear all I am happy to announce that version 1.0 of the "frontier" package is available on CRAN. The R package "frontier" provides tools for analysing efficiency and productivity using the "stochastic frontier" approach. This R package is based on Tim Coelli's DOS software "FRONTIER 4.1" and has been available on CRAN for almost 5 years now. After many

Hi All R Experts, I met with below mentioned statistics in paper "Stock Index Volatility Forecasting with High Frequency Data" by Eugenie Hol, Siem Jan Koopman http://ideas.repec.org/p/dgr/uvatin/20020068.html I would like to ask that what is "Box-Ljung portmantacau statistic based on N squared autocorrelation" ? Is it same as "Box-Ljung Statistics" of stats

Hi I found on google that there is an implementation of hot-deck imputation in SAS: http://ideas.repec.org/c/boc/bocode/s366901.html Is there anything similar in R? Many Thanks Eleni Rapsomaniki

Dear friends, Over the past weeks, I have been asking a lot of questions about how to use R in Bayesian analysis. I am brand new to R, but I am very pleased with it. I started with winbugs but I found winbugs to be a limited software, not bad but has several limitations. By contrast, R allows the analyst to tackle any problem with a huge set of tools for any kind of analysis. I love R. In

Hi there everyone, I have the following model (this is naturally a simplified version just for showing my problem, in case you're wondering this is a translog cost function with the associated cost share equations): C ~ á + â1 log X + â2 log Y + ã1 log Z + ã2 log XX C1 ~ â1 + â2 log YY + ã1 log ZZ Then I have some restrictions on the coefficients, namely that the sum of â equal 1 and the

Just to toss in my very limited experiences with the Grandstream phone-- I haven't tested it enough to really know nor is my Asterisk config set up enough to fully try all the features. Mostly, it just works. It was very easy to configure and get running. I've been toting it around to clients as a show and tell exhibit and it has helped get people excited about the possibilities. Voice

Hello R users & R friends, I just want to ask you if density() can produce a confidence interval, indicating how "certain" the density() line follows the true frequency distribution based on the sample you feed into density(). I've heard of loess.predict(loess(y ~ x), se=TRUE) which gives you a SE estimate of the smoothed scatterplot - but density() kernel smoothing is not the

Dear R fans ( and wiki fans), I am just writing a draft to introduce confidence intervals of various "effect sizes" to my students. Surely, I'll recommend the package MBESS in R. Currently, it means I have to recommend R's interface at first. As a statistics teacher in a dept of psychology, I often have to reply why not to teach SPSS. Psychologists and their students hate to

A question about the inner workings of glm() and dpois(): Suppose I call glm(y ~ x, family=poisson, weights = w) where y contains NON-INTEGER (but still nonnegative) values. (a) Does glm() still correctly maximise the weighted Poisson loglikelihood ? (i.e. the function given by the same formal expression as the weighted loglikelihood of independent Poisson variables Y_i except that the