similar to: Testing Nonlinear Restrictions

Dear colleagues, Von Bertalanffy model is commonly adjust to data on fish length (TL) and age (AGE) TL= Linf*(1-exp(-K*(AGE-t0)). Linf, K and t0 are parameters of the model. One main goal of the growth study is the comparison of growth parameter estimates between sexes of the same species, or estimates from different populations. The realibility statistical tests normally applied are highly

I?m trying to find datasets that will give me residuals, after applying the lm function, with no normality, non linearity, and heteroscedacity so I can try to exemplify those cases in the linear regression model. Can you give any advice on what datasets would be appropiate? I can?t use the ones in the alr3 package because those have already been seen in class. Thank you very much :-) natorro

Hello, I am doing a simple regression using lm(Y~X). As my response and my predictor seemed to be skewed and I can't meet the model assumptions. Therefore I need to transform my variables. I wanted to ask what is the preferred way to find out if predictor and/or response needs to be transformed and if yes how (log-transform?). I found a procedure in "A modern approach to Regressoin in

Full_Name: Rick Stahlhut Version: 2.9.1 OS: os x 10.5.7 Submission from: (NULL) (128.151.71.23) I upgraded to 2.9.1 today from 2.9.0. I work with large CDC (center for disease control) datasets and start, frequently, with a series of 23 large-ish merges to create the final dataset I work on. I do this each time because (a) R is fast. why not? and b) the datasets occasionally get updated by

Dear all, I'm a newbie in R. I am running simulations using a fixed bandwidth in nonparametric regressions, sending all the output to a file myoutput.txt using sink("myoutput.txt"), & R is printing all warnings by the end of the simulation on the file. I know the source of the problem & can take care of it. However, finding a 50 MB file (where all the output is printed, e.g.

Dear All, A couple of questions about the nls package. 1. I'm trying to run a nonlinear least squares regression but the routine gives me the following error message: step factor 0.000488281 reduced below `minFactor' of 0.000976563 even though I previously wrote the following command: nls.control(minFactor = 1/4096), which should set the minFactor to a lower level than the default

Estimados todos: Me es grato escribir a esta lista de ayuda para R, ya que comparto 100% la filosofía de Software libre en especial software en Estadística ya que es la carrera que estoy siguiendo. El motivo de este mensaje es por un par de dudas que no pude resolver: 1. He utilizado las funciones para realizar pruebas de normalidad (kolmogorov-smirnov, cuando n>50) y (Shapiro, cuando

Hi all, I am attempting to apply a nonlinear model developed using nls to a new dataset and assess the fit of that model. At the moment, I am using the fitted model from my fit dataset as the starting point for an nls fit for my test dataset (see below). I would like to be able to view the t-statistic and p-values for each of the iterations using the trace function, but have not yet worked out

GENERAL REFERENCE ON NONLINEAR OPTIMIZATION? What are your favorite references on nonlinear optimization? I like Bates and Watts (1988) Nonlinear Regression Analysis and Its Applications (Wiley), especially for its key insights regarding parameter effects vs. intrinsic curvature. Before I spent time and money on several of the refences cited on the help pages for "optim",

I am using R on a Windows XP professional platform. The following code is part of a bigger one CODE press=function(y,x){ library(qpcR) models.press=numeric(0) cat("\n") dep=y print(dep) indep=log(x) print(indep) yfit=dep-PRESS(lm(dep~indep))[[2]] cat("\n yfit\n") print(yfit) yfit.orig=yfit presid=y-yfit.orig press=sum(presid^2)

Hi all, I'm looking for the algorithm to calculate maximum intrinsic and parameter effects curvature of Bates & Watts (1980). I have Bates & Watts (1980) original article, Bates et al (1983) article, Seber & Wild (1989) and Ratkowsky (1983) books. Those sources show steps and algorithm to get this measures but I don't know translate C code to R language and I've no success

Dear R-users, One of the main reasons I moved from GAUSS to R (as an econometrician) was because of the existence of the library LOCFIT for local polynomial regression. While doing some checking between my former `GAUSS code' and my new `R code', I came to realize LOCFIT is not quite doing what I want. I wrote the following example script:

I have a function in R^2, say f <- function(x,y) { ...skipped } I want to plot this function using contour, persp. wireframe, etc. I know that the function has a global minimum at (x0, y0) The naive approach is to evaluate the function on the outer product of two arrays, like this: sx <- c(seq(-3, x0, len = 100), seq(x0, 3, len = 100)[-1]) sy <- c(seq(-3, y0, len = 100), seq(y0, 3,

Dear R-helpers, I'm running Sweave() on a file. First run: > Sweave('20080331.Rnw') Writing to file 20080331.tex Processing code chunks ... 1 : term hide (label=setup) 2 : echo term verbatim (label=oatvar) 3 : echo term verbatim (label=oatvar1) 4 : echo term verbatim (label=oat2wt) 5 : echo term verbatim (label=oat2wt) 6 : echo term verbatim (label=lm) 7 : echo term

Dear all, Sandy Weisberg and I would like to announce version 2.0-0 of the car package, now on CRAN. We've released this major revision of the package in anticipation of the publication of An R Companion to Applied Regression, Second Edition (Sage, in press), co-authored by us, which should be available before the end of the year. The new version of the car package has a number of new

Dear all, Sandy Weisberg and I would like to announce version 2.0-0 of the car package, now on CRAN. We've released this major revision of the package in anticipation of the publication of An R Companion to Applied Regression, Second Edition (Sage, in press), co-authored by us, which should be available before the end of the year. The new version of the car package has a number of new

Dear all, Is there a way to retrieve standard errors from nls models? The help page tells me that arguments such as se.fit are ignored... Many thanks and best wishes Christoph -- Dr. rer.nat. Christoph Scherber University of Goettingen DNPW, Agroecology Waldweg 26 D-37073 Goettingen Germany phone +49 (0)551 39 8807 fax +49 (0)551 39 8806 Homepage http://www.gwdg.de/~cscherb1

Dear R experts, Did someone implemented French Curve yet? Or can anyone point me some papers that I can follow to implement it? thanks in advance for your help. Paul

Hi! I'm learning mgcv, and reading Simon Wood's book on GAMs, as recommended to me earlier by some folks on this list. I've run into a question to which I can't find the answer in his book, so I'm hoping somebody here knows. My outcome variable is binary, so I'm doing a binomial fit with gam(). I have five independent variables, all continuous, all uniformly

Hello. I am trying to fit an asymptotic relationship (nonlinear) to some ecological data, and am having problems. I am interested in the upper bound on the data (i.e. if there is an upper limit to 'y' across a range of 'x'). As such, I am using the nonlinear quantile regression package (nlrq) to fit a michaelis mention type model. The errors I get (which are dependant on