Hello! On Thu, Feb 3, 2011 at 6:27 AM, m234 <mhairialexan...@gmail.com> wrote: > > II functional response for 2 data sets: > > nls(eaten~(a*suppl)/(1+a*h*suppl) > > where eaten is the number of prey eaten by a predator and suppl is the > number of prey initially supplied to the same predator. > > I have parameter estimates of 'a' and 'h' for the two populations studied > and would like to know if there is a significant different in the estimates > between the two i.e. a1 vs a2, h1 vs h2. I would like to bootstap the data > to get multiple (~1000) estimates and compare then via a ttest or > equivalent. Is it possible to do this and obtain multiple estimations which
Hi, Please read the posting guide--a complete example of your code a link to a data frame will get you more helpful answers. I can tell you how to do bootstrap re-sampling on a model that is estimated from one data set. I wrote up some notes on that last summer (go to middle of this http://pj.freefaculty.org/R/SummerCamp2010/PJ-Lectures/functions1.pdf). I have other intro R material floating about under the R part of that link. But I'm a bit stumped by your question for statistical reasons-- nothing to do with R. From a statistical point of view, how do you test the difference between coefficients from 2 different fitted nls models, which are based on separate data samples? I don't think that's a trivial stat question, even if you have large samples and you only need to calculate that estimated difference one time. Then I wonder, how would a person do re-sampling when there are 2 data sets involved. If you know of a cite on that, I'd like to see it. One avenue I'd consider is to stack the 2 data sets together and rewrite my nls to estimate one equation with indicator variables (dummy variables) to separate the estimates for the 2 separate equations. But there would be some "pooling" tests you'd have to run first, to justify the idea that the 2 sets of data belong in the same analysis in the first place. Know what I mean? Suppose eaten is one long column, for both sets combined, but create suppl1 and suppl2 that are 0 for "the other" data set's cases, but suppl for the right one. Fit this: combmod <- nls(eaten~(a1*suppl1)/(1+a1*h1*suppl1) + (a2*suppl2)/(1+a2*h2*suppl2)) This would conceivably allow comparison of a1 and a2. I think. I'm trying to remember sampling theory on nls. Well, in summary, I think you've got a harder stat problem than you have R problem. If you write out the code you use for a whole exercise to do this 1 time, we might see what to do. But remember to post the full working example--as much as you have, anyway. -- Paul E. Johnson Professor, Political Science 1541 Lilac Lane, Room 504 University of Kansas ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
