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>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 wrote: > >> Message: 10 >> Date: Tue, 2 Jul 2013 19:01:55 +0700 >> From: Robbie >> Weterings<robbie.weterings@**gmail.com<robbie.weteri...@gmail.com> >> > >> To:r-help@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<cafe5dhzrm%2bbpg1v77ezhun%2btacv64j_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/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]] >> > -- 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/facebook/Facebook.html> *www.catdropfoundation.org* <http://www.catdropfoundation.org/> *www.facebook.com/catdropfoundation*<http://www.facebook.com/catdropfoundation> *Boorn 45, 9204 AZ, Drachten, The Netherlands* [[alternative HTML version deleted]]
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