search for: concavity

Dear Friends, Here is an algorithm for finding concave hulls: http://get.dsi.uminho.pt/local/ Has anyone implemented such an algorithm in R? RSiteSearch('concave hull') didn't reveal one (I think). _____________________________ Professor Michael Kubovy University of Virginia Department of Psychology Postal Address: P.O.Box 400400, Charlottesville, VA 22904-4400 Express Parcels

I am modeling a trend surface using trmat and want to trim the resulting matrix to the area enclosed by my real data (i.e., remove all the extrapolated areas). I was using chull and in.chull to calculate the convex hull and change all the other values created by trmat to NA. However, my real data has portions that are slightly concave so chull would give me slivers that are extrapolations from

Dear friends, Do you know how to calculate the CONCAVE hull of a set of points (2- dimensional or n-dimensional)? is that possible in R? (With a "smoothing" parameter of course). Best, -- Corrado Topi Global Climate Change & Biodiversity Indicators Area 18,Department of Biology University of York, York, YO10 5YW, UK Phone: + 44 (0) 1904 328645, E-mail: ct529 at york.ac.uk

...stochastic frontier analysis. The only approach I know to work with R is to estimate a translog production function by sfa or other related function in frontier package, and then use the Ray 1998 formula to find the scale efficiency. However, as the textbook Coelli et al 2005 point out that the concavity may not be satisfied, one needs to impose the nonpositive definiteness condition so that the scale efficiency <1. How can I do it with frontier package? Is there any other SFA model/function in R recommended to find out the scale efficiency and optimal scale? Thanks, Miao [[alternati...

Hi All, I am plotting vertical lines using xyplot in lattice and type="h". It works well, but the problem is that the tops of the lines are convex and the bottoms are concave. Is there a way to flatten the tops and bottoms? Here's my code: Source<-matrix(1:30,10,3) colnames(Source)<-c("x","y1","y2") Source<-data.frame(Source) xyplot(y2+y1~x,

A newbie question: I'm trying to decide whether to run a maximum likelihood estimation in R or Stata and am wondering if the R mle routine is reasonably robust. I'm fairly certain that, with this data, in Stata I would get a lot of complaints about non-concave functions and unproductive steps attempted, but would eventually have a successful ML estimate. I believe that, with the

I'd like to mention that there is a new quantile regression package "nprq" on CRAN for additive nonparametric quantile regression estimation. Models are structured similarly to the gss package of Gu and the mgcv package of Wood. Formulae like y ~ qss(z1) + qss(z2) + X are interpreted as a partially linear model in the covariates of X, with nonparametric components defined as

Hi Arun, thank-you very much! The 2nd option worked perfectly. That was what I wanted. Now, I have another question. I am using the R packages dataRetrieval and EGRET from https://github.com/USGS-CIDA/WRTDS. I have 2 objects Daily and Sample that have the naming convention (Names = "21NC02WQ.C1000000" or whatevver the list of site names happens to be) that I need to have after running

Hello! I have a dataset with the following two vectors: year<-c(1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009)

...anged the name or labeling from BudgeTone to ExpensiTone but kept the good pricing that would help a lot. It lacks elegance. The soon to be released grey model may improve it's image. You cannot wall mount this phone (easily). There are mounting holes on the bottom and the handset has a little concavity in the right place but there's no nub/pin to keep the handset in place when on hook. I haven't asked the Grandstream folks about this--maybe I got a dud or maybe they already plan to address this. I just bought a new house and am seriously thinking about sprinkling these throughout and usi...

I have been building an R function to calculate the ***observed*** (as opposed to expected) Fisher information matrix for parameter estimates in a rather complicated setting. I thought I had it working, but I am getting a result which is not positive definite. (One negative eigenvalue. Out of 10.) Is it the case that the observed Fisher information must be positive definite --- thereby

Im trying to ascertain whether or not the facilities of R are sufficient for solving an optimization problem I've come accross. Because of my limited experience with R, I would greatly appreciate some feedback from more frequent users. The problem can be delineated as such: A utility function, we shall call g is a function of x, n ... g(x,n). g has the properties: n > 0, x lies on the

Hi, Does anyone know if there is a function to find the maximum likelihood estimates of glm using Newton Raphson metodology instead of using IWLS. Thanks Valeska Andreozzi -------------------------------------------------------- Department of Epidemiology and Quantitative Methods FIOCRUZ - National School of Public Health Tel: (55) 21 2598 2872 Rio de Janeiro - Brazil

Hi, I have a quick question regarding estimation of a truncation regression model (truncated above at 1) using MLE in R. I will be most grateful to you if you can help me out. The model is linear and the relationship is "dhat = bhat0+Z*bhat+e", where dhat is the dependent variable >0 and upper truncated at 1; bhat0 is the intercept; Z is the independent variable and is a uniform

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...en L1 and L0 regularization. One nice feature of this family is that the single-coordinate optimization problems are convex, making it ideal for coordinate descent. The package fits the regularization surface for each parameter - a surface over the two-dimensional space of tuning parameters. The concavity parameter gamma indexes the member of the family, and lambda is the usual Lagrange penalty parameter which determines the strength of the penalty. Sparsenet is extremely fast. For example, with 10K variables and 1K samples, the entire surface with 10 values of gamma and 50 values of lambda takes...

...en L1 and L0 regularization. One nice feature of this family is that the single-coordinate optimization problems are convex, making it ideal for coordinate descent. The package fits the regularization surface for each parameter - a surface over the two-dimensional space of tuning parameters. The concavity parameter gamma indexes the member of the family, and lambda is the usual Lagrange penalty parameter which determines the strength of the penalty. Sparsenet is extremely fast. For example, with 10K variables and 1K samples, the entire surface with 10 values of gamma and 50 values of lambda takes...

Hello! I would like to get MLE for parameter lambda of Poisson distribution. I can use fitdistr() for this. After looking a bit into the code of this function I can see that value for lambda and its standard error is estimated via estimate <- mean(x) sds <- sqrt(estimate/n) Is this MLE? With my poor math/stat knowledge I thought that MLE for Poisson parameter is (in mixture of LaTeX

Hi, Can anyone help me with the following. I have been using R for Monte Carlo simulations and got some results I couldn't explain. Therefor I performed following short test: -------------- mean.sds <- NULL sample.sizes <- 3:30 for(N in sample.sizes){ dum <- NULL for(I in 1:5000){ x <- rnorm(N,0,1) dum <- c(dum,sd(x)) } mean.sds<- c(mean.sds,mean(dum)) }

Hi all, I am wondering if anyone can help me to resolve a problem. I am trying to use tc command in linux to drop udp packets of specific diffserv value. I am able set diffserv value successfully in the udp packet using command:- [root@scotch src]#iptables --table mangle --append OUTPUT \ --out-interface eth0 --protocol udp --source-port 5060 \ --jump DSCP --set-dscp 8 but i am not able to