Dear Prof. Nash, I tried to run nls with the nlxb function and as you mention it is fairly slower in terms of running the code. However, if I would have used this function earlier I would have saved a lot of time trying to find the start values. The output looks a little bit sloppy but I think it is very usefull to use in combination with nls.
Thanks Robbie On Thu, Jul 4, 2013 at 10:09 PM, Prof J C Nash (U30A) <nas...@uottawa.ca>wrote: > I was actually thinking of something a little different, but realize that > for what you are trying, the dummy variable approach is likely a much > better choice. Good luck. > > JN > > PS. If you try nlxb from nlmrt, let me know how it works for you. The > intent is to be much more aggressive in finding solutions, but when nls > works, nls should be faster. I've examples where nls fails from about 3/4 > of random starting points, but nlxb gets there 95% of the time. My goal was > to provide something that would be useful when users did not know good > starting values. Also for small residual problems that nls is explicitly > noted as being unsuited for. > > > On 13-07-04 06:40 AM, Robbie Weterings wrote: > >> Thanks for the advise Prof Nash, but I'm not sure if I understood it >> right. I managed to make a new model based on what I think you meant. >> What I did is; I created 3 variables (cat1, cat2, cat3) one for each >> category with either the value 1 or 0 and added these to the model so >> they work as different intercepts: >> >> model <- nls(rates ~ f*cat1 + d*cat2 + e*cat3 +((a * prey)/((1 + b * >> prey) * (1 + c * (pred-1)))), start = list(a = 0.14, b = 0.009, c=0.66, >> d=0.8,e=-0.04,f=1.4), trace = TRUE) >> >> Please correct me if i'm wrong. It seems to work and the model is >> satisfactory. It gives me similar results as what I had with the glm >> with all the variables being log transformed. I first tried to fit >> predator and prey for each category in a model like this, so I would get >> different curves for each category: >> >> model <- nls(rates ~ ((a*cat1) * ((b * prey)/((1 + c * prey) * (1 + d * >> (pred-1))) + ((e*cat1) * ((f * prey)/((1 + g * prey) * (1 + h * >> (pred-1))) ........ >> >> But it did not work moreover it would have resulted in too many >> parameters for the size of the dataset. It will never win it from any of >> the simpler models. >> >> I also got some advise about the gam model from someone at >> stats.stackexchange: >> /The gam model starts with knots set at 10, which you do not have enough >> >> data for, however you have densities of 5 and 6 groups - you can set >> your knots 1 fewer and the model should run. Something like >> model4<-gam(rates~s(pred,k=5)+**s(prey,k=4),data=data) / >> >> >> This also worked fine. However, I would prefer a nls model and by the >> look of it so do my residuals. >> With kind regards >> Robbie >> >> >> On Wed, Jul 3, 2013 at 8:51 PM, Prof J C Nash (U30A) <nas...@uottawa.ca >> <mailto:nas...@uottawa.ca>> wrote: >> >> If preytype is an independent variable, then models based on it >> should be OK. If preytype comes into the parameters you are trying >> to estimate, then the easiest way is often to generate all the >> possible combinations (integers --> fairly modest number of these) >> and run all the least squares minimizations. Crude but effective. >> nlxb from nlmrt or nlsLM from minpack.lm may be more robust in doing >> this, but less efficient if nls works OK. >> >> JN >> >> On 13-07-03 06:00 AM, r-help-requ...@r-project.org >> <mailto:r-help-request@r-**project.org <r-help-requ...@r-project.org>> >> wrote: >> >> Message: 10 >> Date: Tue, 2 Jul 2013 19:01:55 +0700 >> From: Robbie >> Weterings<robbie.weterings@__g**mail.com<http://gmail.com> >> <mailto:robbie.weterings@**gmail.com <robbie.weteri...@gmail.com> >> >> >> To:r-help@r-project.org >> <mailto:To%3Ar-help@r-project.**org<to%253ar-h...@r-project.org> >> > >> >> Subject: [R] Non-linear modelling with several variables >> including a >> categorical variable >> Message-ID: >> >> <CAFe5dHZRM+BpG1v77EzHun+__**tacV64J_9pnSFGh_xne5CSZ9qdQ@__** >> mail.gmail.com <http://mail.gmail.com> >> <mailto:CAFe5dHZRM%**2BBpG1v77EzHun%2BtacV64J_** >> 9pnSFGh_xne5CSZ9qdQ@mail.**gmail.com<cafe5dhzrm%252bbpg1v77ezhun%252btacv64j_9pnsfgh_xne5csz9...@mail.gmail.com> >> >> >> >> Content-Type: text/plain >> >> >> Hello everyone, >> >> I am trying to model some data regarding a predator prey >> interaction >> experiment (n=26). Predation rate is my response variable and I >> have 4 >> explanatory variables: predator density (1,2,3,4 5), predator >> size, prey >> density (5,10,15,20,25,30) and prey type (3 categories). I >> started with >> several linear models (glm) and found (as expected) that prey >> and predator >> density were non-linear related to predation rates. If I use a log >> transformation on these variables I get really nice curves and >> an adjusted >> R2 of 0.82, but it is not really the right approach for modelling >> non-linear relationships. Therefore I switched to non-linear >> least square >> regression (nls). I have several predator-prey models based on >> existing >> ecological literature e.g.: >> >> model1 <- nls(rates ~ (a * prey)/(1 + b * prey), start = list(a >> = 0.27,b = >> 0.13), trace = TRUE) ### Holling's type II functional response >> >> model2 <- nls(rates ~ (a*prey)/(1+ (b * prey) + c * (pred -1 )), >> start = >> list(a=0.22451, b=-0.18938, c=1.06941), trace=TRUE, subset=I1) ### >> Beddington-**DeAngelis functional response >> >> >> These models work perfectly, but now I want to add prey type as >> well. In >> the linear models prey type was the most important variable so I >> don't want >> to leave it out. I understand that you can't add categorical >> variables in >> nls, so I thought I try a generalized additive model (gam). >> >> The problem with the gam models is that the smoothers (both >> spline and >> loess) don't work on both variables because there are only a very >> restricted number of values for prey density and predator >> density. I can >> manage to get a model with a single variable smoothed using >> loess. But for >> two variables it is simply not working. The spline function does >> not work >> at all because I have so few values (5) for my variables (see >> model 4). >> >> model3 <- gam(rates~ lo(pred, span=0.9)+prey) ## this one is >> actually >> working but does not include a smoother for prey. >> >> model4 <- gam(rates~ s(pred)+prey) ## this one gives problems: >> *A term has fewer unique covariate combinations than specified >> maximum >> degrees of freedom* >> >> >> My question is: are there any other possibilities to model data >> with 2 >> non-linear related variables in which I can also include a >> categorical >> variable. I would prefer to use nls (model2) with for example >> different >> intercepts for each category but I'm not sure how to get this >> sorted, if it >> is possible at all. The dataset is too small to split it up into >> the three >> categories, moreover, one of the categories only contains 5 data >> points. >> >> Any help would be really appreciated. >> >> With kind regards, >> -- Robbie Weterings *Project Manager Cat Drop Thailand ** Tel: >> +66(0)890176087 * 65/13 Mooban Chakangrao, Naimuang Muang >> Kamphaeng Phet >> >> 62000, Thailand àÅ¢·Õè 65/13 Á.ªÒ¡Ñ§ÃÒÇ ¶¹¹ ÃÒª´íÒà¹Ô¹2 ã¹àÁ×ͧ >> ÍíÒàÀÍ/ >> ࢵ àÁ×ͧ¡íÒàྦྷྪà ¨Ñ§ËÇÑ´ ¡íÒàྦྷྪà 62000 >> <http://www.catdropfoundation.**__org >> <http://www.catdropfoundation.**org<http://www.catdropfoundation.org> >> >> >> <http://www.catdropfoundation.**__org/facebook/Facebook.html >> >> <http://www.catdropfoundation.**org/facebook/Facebook.html<http://www.catdropfoundation.org/facebook/Facebook.html> >> >> >> *www.catdropfoundation.org >> <http://www.catdropfoundation.**org<http://www.catdropfoundation.org> >> >* >> <http://www.catdropfoundation.**__org/ >> >> <http://www.catdropfoundation.**org/<http://www.catdropfoundation.org/> >> >> >> >> *www.facebook.com/__**catdropfoundation*<http://www.facebook.com/__catdropfoundation*> >> >> <http://www.facebook.com/**catdropfoundation*<http://www.facebook.com/catdropfoundation*> >> ><http://**www. >> ><http://www.>__facebook.com/**catdropfoundation<http://facebook.com/catdropfoundation> >> >> <http://www.facebook.com/**catdropfoundation<http://www.facebook.com/catdropfoundation> >> >__> >> >> *Boorn 45, 9204 AZ, Drachten, The Netherlands* [[alternative HTML >> version deleted]] >> >> >> >> >> -- >> Robbie Weterings >> /Project Manager Cat Drop Thailand >> >> //Tel: +66(0)890176087 >> / >> >> 65/13 Mooban Chakangrao, Naimuang Muang >> Kamphaeng Phet 62000, Thailand >> àÅ¢·Õè 65/13 Á.ªÒ¡Ñ§ÃÒÇ ¶¹¹ ÃÒª´íÒà¹Ô¹2 ã¹àÁ×ͧ ÍíÒàÀÍ/ >> ࢵ àÁ×ͧ¡íÒàྦྷྪà ¨Ñ§ËÇÑ´ ¡íÒàྦྷྪà 62000 >> >> <http://www.catdropfoundation.**org <http://www.catdropfoundation.org>> >> <http://www.catdropfoundation.**org/facebook/Facebook.html<http://www.catdropfoundation.org/facebook/Facebook.html> >> > >> /www.catdropfoundation.org/ >> <http://www.catdropfoundation.**org/<http://www.catdropfoundation.org/> >> > >> /www.facebook.com/**catdropfoundation/<http://www.facebook.com/catdropfoundation/> >> <http://www.facebook.com/**catdropfoundation<http://www.facebook.com/catdropfoundation> >> > >> /Boorn 45, 9204 AZ, Drachten, The Netherlands/ >> > [[alternative HTML version deleted]]
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