# Help with nonlinear least squares regression curve fitting Classic List Threaded 3 messages Open this post in threaded view
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## Help with nonlinear least squares regression curve fitting

 Hi everyone. This is my first post to this forum and I'm hoping someone can help. I'm trying to finish up some analysis for my thesis and this is the last problem I have. I have calculated data for 15 different species of birds; below is an example of one species and what the data might look like. I have three a priori nonlinear curves that I want to test each data set against in order to see which of the three curves has the best fit. (I suspect the fit won't be that great for any of them in some instances.) The curves' functions that I want to test are in the code here (hopefully correctly): Inverse Quadratic Curve: fitmodel <- nls(Area ~ (-a*Year)*(Year + b), data = df, start=list(a=??, b=??, c=??)) Sigmodial Curve: fitmodel <- nls(Area~a/(1+exp(-(b+c*Year))), data=df, start=list(a=???, b=???, c=??)) Double sigmoidal Curve: fitmodel <- nls(Area~a+2b(1/(1+exp(-abs(-c*Year+d)))-1/2)*sign(-c*Year+d), data=df, start=list(a=???, b=???, c=???) My problem is I can't really figure out how to choose the correct starting values to avoid getting the singular matrix error. Any help as to how to go about this would be appreciated! Does everything look right? My method is okay? If I can get the fits to run I plan on using AIC to select the best curve for each of the 15 species. I thank you in advance for your consideration and help on this! Cheers, Corey Callaghan df: Area                 Year 104.7181283 1984 32.88026974 1985 56.07395863 1986 191.3422143 1987 233.4661392 1988 57.28317116 1989 201.1273404 1990 34.42570796 1991 165.8962342 1992 58.21905274 1993 114.6643724 1994 342.3461986 1995 184.8877994 1996 94.90509356 1997 45.2026941 1998 68.6196393 1999 575.2440229 2000 519.7557581 2001 904.157509 2002 1107.357517 2003 1682.876061 2004 40.55667824 2005 740.5032604 2006 885.7243469 2007 395.4190968 2008 1031.314519 2009 2597.544987 2010 1316.968695 2011 848.7093901 2012 5076.675075 2013 6132.975491 2014