similar to: QUADRATIC TREND FOR LINK FUNCTIONS ON NON-STATIONARY GEV

Hi All, I am a newcomer to S/R. Could you please let me know how to model quadratic trends for the mul/sigl link functions when fitting non-stationary GEV distributions using the ismev package? Thanks Best Regards, Mohammad Ashrafuz Zaman PhD Candidate School of Engineering Building XC, Room 1.02 (Kingswood Campus) University of Western Sydney Locked Bag 1797, Penrith South DC NSW 1797

Hola a todos, Soy nuevo en R y estoy intentando modelizar una serie de datos no estacionarios usand la distribucion Generalizada de Valores Extremos GEV. ¿Podriais indicarme como se modeliza una tendencia polinómica (cuadrática, por ejemplo) en alguno de los 3 parámetros (situación, escala o forma)? He encontrado documentación a cerca de modelización linear o exponencial, pero no acabo de

Dear list users How is it possible to visualise both a linear trend line and a quadratic trend line on a plot of two variables? Here my almost working exsample. data(Duncan) attach(Duncan) plot(prestige ~ income) abline(lm(prestige ~ income), col=2, lwd=2) Now I would like to add yet another trend line, but this time a quadratic one. So I have two trend lines. One linear trend line

-----Original Message----- Message: 6 Date: Mon, 20 Jan 2003 01:11:24 +0100 From: Martin Michlmayr <tbm at cyrius.com> To: r-help at stat.math.ethz.ch Subject: [R] quadratic trends and changes in slopes I'd like to use linear and quadratic trend analysis in order to find out a change in slope. Basically, I need to solve a similar problem as discussed 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)

-----Original Message----- Message: 6 Date: Mon, 20 Jan 2003 01:11:24 +0100 From: Martin Michlmayr <tbm at cyrius.com> To: r-help at stat.math.ethz.ch Subject: [R] quadratic trends and changes in slopes I'd like to use linear and quadratic trend analysis in order to find out a change in slope. Basically, I need to solve a similar problem as discussed in

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

  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'd like to use linear and quadratic trend analysis in order to find out a change in slope. Basically, I need to solve a similar problem as discussed in http://www.gseis.ucla.edu/courses/ed230bc1/cnotes4/trend1.html My subjects have counted dots: one dot, two dots, etc. up to 9 dots. The reaction time increases with increasing dots. The theory is that 1 up to 3 or 4 points can be counted

This is not exactly an R question but the R code below may make my question more understandable. If one plots sin(x) where x runs from -pi to pi , then the curve hovers around zero obviously. so , in a"stationary in the mean" sense, the series is stationary. But, clearly if one plots the acf, the autocorrelations at lower lags are quite high and, in the "box jenkins"

Hi, I have been having difficulties in finding packages/ codes that simplify plotting of a GEV fitted to dataset (by L-moments) that would print out graph comprising dataset versus gumbel reduced variate n return period at the same. Anyone can help me on this? Thanks. [[alternative HTML version deleted]]

Hi all, I'd like to export the results of GPD or GEV analysis generated with the Extremes Toolbox for plotting in Grapher or Excel (for manipulation by our publications group). Is it possible to access the data used to generate the plots in extRemes, or do I need to code the analysis from scratch and write to file? Thanks, Ben Stay connected to the people that matter most with a

Hi, I'd like to export the results of GPD or GEV analysis generated with the Extremes Toolbox for plotting in Grapher or Excel (for manipulation by our publications group). Is it possible to access the data used to generate the plots in extRemes, or do I need to code the analysis from scratch and write to a file? Stay connected to the people that matter most with a smarter inbox.

Dear useRs, upon request, the VGAM package (currently version 0.7-1) has been officially released on CRAN (the package has been at my website http://www.stat.auckland.ac.nz/~yee/VGAM for a number of years now). VGAM implements a general framework for several classes of regression models using iteratively reweighted least squares (IRLS). The key ideas are Fisher scoring, generalized linear and

Buenas a todos. Estoy realizando unos ajustes a una serie de valores mensuales maximos con ismev. Los resultados son muy buenos. Por definicion, los valores que obtengo del ajuste de la funcion de distribucion GEV, me dan valores de periodos de retorno medios para los niveles de retorno que estudio. El problema es que estos valores son los medios esperables para esos periodos de retorno y yo, lo

Hi ! Sorry for my ignorance. I have two questions: 1) which is in R the function to make a covariogram of spatial data ? 2) does anyone know if there exist the possibility in R of performing kriging with an arbitrary covariance function ? If I have well understood the Krig procedure offers only three possibilities (expcov, gauscov and sphercov) that are the common models for stationary and

Hi I would like to use arima () to find the best arima model for y time series. The default in arima apparently is to use conditional sum of squares to find the starting values and then ML (as described on the help page). Now using the default may lead to error messages saying: "non-stationary ar part in CSS". When changeing the default to "ML" only the minimization

Hi, I have a sample of size 100 from a function in interval [0,1] which can be assumed to come from a scalar-valued stationary Gaussian process. There are about 500 observation points in the interval. I need an effective and fast way to simulate from the Gaussian process conditioned on the available data. I can of course estimate the mean and 500x500 covariance matrix from data. I have searched

How do I calculate the confidence interval for the value x given by the intersection of two quadratics (i.e. parabolas)? I fit two quadratics of the form: y = C1 + B1*x + A1*x^2 y = C2 + B2*x + A2*x^2 to two sets of points N1 and N2. I test for whether they intersect, if they do then I calculate the roots of: 0 = (C1 - C2) + (B1 - B2)*x + (A1 - A2)*x^2 to determine where they intersect

Hi, Does anybody have experience to solve an quadratic programming problem with quadratic constraints in R? It seems that the package "quadprog" only handles the quadratic programming with linear constraint. My probelm is to maximze x^T\Sigma_{xy} y, subject to x^Tx=1, y^T\Sigma_{yy} y=1, and sum(y)<t, or sum(y)=t, where x and y are the variable, and the Sigma's and t are