search for: bertalanffi

Howdy, Last week I got some great help on why I was getting an error code when trying to run this model, thanks everyone!  I was able to get the code up and running beautifully for several data sets.  Now I am getting different errors with this new data set.  I can't figure out why, I have more data points with this species, and it is ordered exactly the same as the other species I have been

I apologize if this comes across as confusing. I will try to explain my situation as best I can. I have R bootstrapping my growth data for fish. It's resampling my database of age and length data and then produces several new datasets for me. In this case, it's resampling my data to create three new datasets of age and length data. Here is my code with my original data called

All - I have data: TL age 388 4 418 4 438 4 428 5 539 10 432 4 444 7 421 4 438 4 419 4 463 6 423 4 ... [truncated] and I'm trying to fit a simple Von Bertalanffy growth curve with program: #Creates a Von Bertalanffy growth model VonB=nls(TL~Linf*(1-exp(-k*(age-t0))), data=box5.4, start=list(Linf=1000, k=0.1, t0=0.1), trace=TRUE) #Scatterplot of the data plot(TL~age, data=box5.4,

Dear R-list members, Cynthia M. Jones wrote a paper (Fitting growth curves to retrospective size-at-age data, Fisheries Research 46(2000):123-129; abstract at http://www.elsevier.nl/gej-ng/10/19/44/70/24/37/abstract.html)where the SAS procedure MIXED, Macro NLINMIX (Littell et. al., 1996)was used to estimate the von Bertalanffy growth function parameters assuming that data from the same fish are

I'm fitting nonlinear functions to some growth data but I'm getting radically different results in R to another program (Prism). Furthermore the values from the other program give a better fit and seem more realistic. I think there is a problem with the results from the r nls function. The differences only occur with weighted data so I think I'm making a mistake in the weighting.

I am trying to compare Vbert growth curves from several years of fish data. I am following the code provided by: http://www.ncfaculty.net/dogle/fishR/gnrlex/VonBertalanffy/VonBertalanffy.pdf. Specifically the section on VBGM Comparisons between groups. ? This code is pretty cut and dry. I am able to run it perfectly with the "fake" data that is provided. But when I run it with my own

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 am looking into fitting a so-called double von Bertalanffy function to fish length-at-age data. Attempting to simplify the situation, the model looks like this ... Y ~ f(X; a,b,c) if x < Z Y ~ g(X; a,d,e) if x >= Z where * f and g are non-linear functions (the "traditional" "single" von Bertalanffy growth function), * Y (length) and X (age) are observed variables,

Hi there, I'm a PhD student investigating growth patterns in fish. I've been using the minpack.lm package to fit extended von Bertalanffy growth models that include explanatory covariates (temperature and density). I found the nls.lm comand a powerful tool to fit models with a lot of parameters. However, in order to select the best model over the possible candidates (without covariates,

Thanks to the help of people from this forum I was able to bootstrap my data and then apply a model to it. Thanks for all your help. Everything worked out well, but I am having a difficult time getting the new parameter values. I bootstrapped the data 300 times and I want to get the 300 sets of parameter estimates and plot them in Excel. Here is my code:

R-help, I'm trying to estimate some parameters using the Maximum Likehood method. The model describes fish growth using a sigmoidal-type of curve: fn_w <- function(params) { Winf <- params[1] k <- params[2] t0 <- params[3] b <- params[4] sigma <- params[5] what <- Winf * (1-exp(- k *(tt - t0)))^b

I have a fisheries Ricker stock-recruitment non-linear model which I am trying to code up in R (after failing in MATLAB). There are two fixed parameters and a random environmental effect that is linked to a first order lagged correlation fixed parameter. Are there any examples of fitting this model I could follow? Cheers Norm Good Fisheries Biologist (Mathematician) Southern Fisheries Centre

Hi In a dataset I have length and age for cod. The age, however, is ony given for 40-100% of the fish. What I need to do is to fill inn the NAs in a correct way, so that age has a value for each length. This is to be done for each sample seperately (there are 324 samples), meaning the NAs for sampleno 1 shall be calculated from the known values from sampleno 1. As for example length 55 cm

Hi, I have great problems with my work in R. I look for to model the growth of fish. I have "Longitudinal data", a serie of repeated measures for each individual. Using the corresponding packages "nlme" in R. I treat to fit to the data different growth functions, wich were entered by me. Unfortunately for no it was arrived at the convergence, several error messages appeared. I

Hello, I am attempting to fit a non-linear model (Von Bertalanffy growth model) to fish length-at-age data with the purpose of determining if any of the three parameters differ between male and female fish. I believe that I can successfully accomplish this goal assuming an additive error structure (illustrated in section 1 below). My trouble begins when I attempt this analysis using a model

Dear R-list members, Some months ago I wrote a message on the usage of gnls (nlme library) and here I come again. Let me give an example: I have a 10 year length-at-age data set of 10 fishes (see growth.dat at the end of this message) and I want to fit a von Bertalanffy growth model, Li= Linf*(1-exp(-k*(ti-t0))) where Li = length at age i, Linf= asymptotic length, k= curvature parameter, ti=

CRAN (and crantastic) updates this week New packages ------------ * EffectStars (1.0) Maintainer: Unknown Author(s): Gunther Schauberger License: GPL-2 http://crantastic.org/packages/EffectStars The package provides functions to visualize regression models with categorical response. The effects of the covariates are plotted with star plots in order to allow for an optical