similar to: Constrained minimisation

Dear All, Thank you for the replies to my first thread here: http://r.789695.n4.nabble.com/global-optimisation-with-inequality-constraints-td3799258.html. So far the best result is achieved via a penalised objective function. This was suggested by someone on this list privately. I am still looking into some of the options mentioned in the original thread, but I have been advised that there may

It would be really nice if nlm took a set of "..." optional arguments that were passed through to the objective function. This level of hacking is probably slightly beyond me: is there a reason it would be technically difficult/inefficient? (I have a vague memory that it used to work this way either in S-PLUS or in some previous version of R, but I could easily be wrong.) Here's

Dear R users, I am searching for a general minimisaton function like 'ms' in R. Please give me a hint! -- ------------------------------------------------------------ Dipl. Inform. J. Hedderich Institut f?r Medizinische Informatik und Statistik im Klinikum an der CAU Phone : 0431/5973182 Brunswiker Str. 10

Hi guys, I'm trying to use lmomco package. first I did the manual calculation on what is the estimates scale and location parameter given L-CV=0.2, L1=1000 L-moments and k (shape parameter) =- 0.1. so what i get is: location: 821.0445 scale: 260.7590 shape: -0.1000 #I assign this as GEV vectors using vec2par GEVpara2<-vec2par(c( 821.0445 , 260.7590 ,-0.1),'gev') #then I

I am estimating a large model by groups. How do you save the results and?returns the associated quantiles? For this example I need a data frame n?? ?xi??????? mu????????beta 1?? 0.1033614? 2.5389580 0.9092611 2? ?0.3401922? 0.5192882 1.5290615 3?? 0.5130798? 0.5668308 1.2105666 I also want to apply gevrlevelPlot() for each "n" or group. ? #Example n <- c(1, 1, 1, 1, 1, 1, 2, 2, 2,

I have a set of data that is distributed normally. How can I transform this data to fit an ArcTan distribution? [[alternative HTML version deleted]]

  Hi r-users,   I would like to use gev and my data (annual rainfall ) is as follows:   > head(dat,20) A B C D E F G H I J 1 45.1 41.5 58.5 50.1 46.0 49.1 37.7 49.1 59.8 54.0 2 50.3 39.8 49.4 56.4 49.4 48.8 42.1 49.8 49.4 58.3 3 41.7 39.3 44.6 39.1 35.7 41.5 40.8 40.8 38.5 45.6 4 50.7 33.9 48.4 28.2 35.5 39.1 61.4 17.0 30.7 38.3 5 39.3 30.6 46.9 23.8 25.8

i need HELPPP!! how do i calculate the RMSE value for two GEV models?first GEV is where the three parameters are constant.2nd GEV model a 4 parameter model with the location parameter is allowed to vary linearly with respect to time while holding the other parameters at constant. is there any programming code for this? i really really need help. please reply to me as soon as possible. thanks in

hello, i'am is new in R software.i have try to make a function but it can't give what it should.i dunno what have to do next. Can somebody help me to solve it.i'll very appreciate... ##GEV simulation(Non-stationary) dsim<-function(n, alpha, beta,sca,sha){ t <- 1:n location <- alpha + beta*t inv.df<-function(x) location + -(sca/sha)+(sca/sha)*(-log(x))^(-sha) u<-runif(n)

Hello R Community, I've been run the following codes. However, I've been getting an unusual segfault that I'm unable to trace its origin. Please give me a light to decipher the "caught segfault" Thanks for you attention. Bernardo. > options(STERM='iESS', editor='emacsclient') > rm(list = ls()) > > source("fgenIGLD.R") #RNG

Hi All, I am a newcomer to R. Could anyone explain me how to define link functions for either mu/sigma to allow for quadratic trends in the same, when fitting non-stationary GEV distributions? Thanks -- View this message in context: http://www.nabble.com/QUADRATIC-TREND-FOR-LINK-FUNCTIONS-ON-NON-STATIONARY-GEV-tp23360751p23360751.html Sent from the R help mailing list archive at Nabble.com.

Hi, I'm new to R, and am trying to embed R into another application. I'm calling gev.fit() from the ismev package, and it is crashing somewhere inside it. gdb is not catching it, and valgrind is not showing any memory corruption issues. I suspect it's memory corruption, because it doesn't crash in exactly the same spot each time. I'm running R 2.12.2 on a 64 bit linux (Ubuntu

I need to do a non-linear regression in the form of Y = a0 + a1 * arctan(a2 * x) + error. A data sample (X,Y) is available, but I can't remember how to run this sort of regression through R so that I get a value for a0, a1 and a2. Can someone please give me a hint? Thank you in advance.

Hello, I am using the lmomco package (lmom.ub and pargev) to compute the GEV parameters (location, scale, and shape), which are used to estimate return values. I was wondering how/if I can calculate upper and lower confidence (CI_u, CI_l) intervals for each return frequency using the GEV parameters to fill-in the table below? Xi (location) = 35.396 Alpha (scale) = 1.726 Kappa (shape) =

Hi all, When upgrading to 2.11.1 recently I noticed different results being produced by my code. After much digging I have finally narrowed it to a call to nlm(). This can be replicated by: FixedRemovals<-1836180125888 AbStageInitial<-2223033830403 Rates<- 0.3102445 nlm(function(rootM,Abund,Loss,OtherM) {(Loss-(rootM/(rootM+OtherM)* (1-exp(-(rootM+OtherM)))*

Dear All- I am looking at the bivariate point process model for extreme values as described in Section 8.3.2 of Coles book. I am stuck at a very low level in being unable to reproduce the initial step of transforming the data to unit Frechet margins. In particular, I am unable to reproduce the wave-surge pairs distribution after transformation to Frechet scale (Figure 8.6 of Coles

Ben Bolker gives some reasons why Numerical Recipes may be problematic as a starting point for R codes. CUP did a masterful job of marketing, but the license is restrictive as the links he gives points out. In some tests, I've also noted that some of the algorithms are less than stellar e.g, convergence tests in one or two optimization routines. Should we have a wiki item to help people find

Can anyone show me how to add a log Pearson type III plot using the evdistq() command to an extreme value plot using the lmom package? Attached sample code below... Thanks in advance, Dave library(lmom) # annual maximum daily streamflows Mackenzie River mackenzieRiver = c(26600, 30300, 34000, 32000, 29200, 28300, 28600, 26400, 28300, 28800, 29000, 22100, 32900, 31800, 21600, 32100, 27000,

Dear R helpers, I need to use KS and AD test for Generalized Pareto and Generalized extreme value. E.g. if I need to use KS for Weibull, I have teh syntax ks.test(x.wei,"pweibull", shape=2,scale=1) Similarly, for AD I use ad.test(x, distr.fun, ...) My problem is fir given data, I have estimated the parameters of GPD and GEV using lmom. But I am not able to find out the distribution

Hi, R-folks: I have been tryin many combination of parameter to make Matern variogram to work, but I can't find the available one. I'm near to be crazy. I tiped: A?o2003Selg.lf<-likfit(A?o2003Selg,cov.model="matern",ini.cov.pars=c(1.5,14),kappa=2.5,fix.kappa=FALSE,nugget=0.08,lambda=0.008,fix.lambda=FALSE,hessian=TRUE) the hessian shows: $hessian [,1]