Re: [R] Non linear regression - Von Bertalanffy Growth Function - "singular gradient matrix at initial parameter estimates"
Thank you for the tip. Indeed, nlxb in nlmrt works and results are not crazy. I would like however to assess goodness-of-fit (gof) and ultimately to compare it with gof from linear regression (fitted with same variables). Before I used AICc to compare the nls() and lm() fit, however I get now an error message concerning the method loglike and its non compatibility with nlmrt class object. I guess it is because we use now Marquardt method to minimise sum-of square instead of Gauss-Newton? I am right? Or this is just an incompatibility coming between AICc function and nlmrt objects? Is there an R function to do that? Best, Xochitl C. <>< <>< <>< <>< Xochitl CORMON +33 (0)3 21 99 56 84 Doctorante en écologie marine et science halieutique PhD student in marine ecology and fishery science <>< <>< <>< <>< IFREMER Centre Manche Mer du Nord 150 quai Gambetta 62200 Boulogne-sur-Mer <>< <>< <>< <>< Le 19/08/2015 15:11, ProfJCNash a écrit : Packages nlmrt or minpack.lm use a Marquardt method. minpack.lm won't proceed if the Jacobian singularity is at the starting point as far as I'm aware, but nlxb in nlmrt can sometimes get going. It has a policy that is aggressive in trying to improve the sum of squares, so will use more effort than nls when both work. JN On 15-08-18 12:08 PM, Xochitl CORMON wrote: Dear all, I am trying to estimate VBGF parameters K and Linf using non linear regression and nls(). First I used a classic approach where I estimate both parameters together as below with "alkdyr" being a subset per year of my age-length-key database and running in a loop. vbgf.par <- nls(Lgtcm ~ Linf *(1 - exp(-K * (Age - tzero))), start = c(K= 0.07, Linf = 177.1), data=alkdyr) I obtain an estimation of both parameters that are strongly correlated. Indeed after plotting Linf ~ K and fitting a linear regression I obtain a function (Linf = a + b*K) with R2= 0.8 and a = 215, b = -763. In this context, to take into account explicitly correlation between parameters, I decided to fit a new non linear regression derivate from VBGF but where Linf is expressed depending on K (I am most interested in K). To do so, I tried this model: vbgf.par <- nls(Lgtcm ~ (a + (b*k)) *(1 - exp(-k * (Age - tzero))), start = c(k= 0.07, a= 215, b=-763), data=alkdyr) Unfortunately at this point I cannot go further as I get the error message "singular gradient matrix at initial parameter estimates". I tried to use alg= plinear (which I am not sure I understand properly yet). If I give a starting value for a and b only, I have an error message stating "step factor below minFactor" (even when minFactor is set to 1000). Any help will be more than welcome as this is quite urgent Best, Xochitl C. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
Re: [R] Non linear regression - Von Bertalanffy Growth Function - singular gradient matrix at initial parameter estimates
Packages nlmrt or minpack.lm use a Marquardt method. minpack.lm won't proceed if the Jacobian singularity is at the starting point as far as I'm aware, but nlxb in nlmrt can sometimes get going. It has a policy that is aggressive in trying to improve the sum of squares, so will use more effort than nls when both work. JN On 15-08-18 12:08 PM, Xochitl CORMON wrote: Dear all, I am trying to estimate VBGF parameters K and Linf using non linear regression and nls(). First I used a classic approach where I estimate both parameters together as below with alkdyr being a subset per year of my age-length-key database and running in a loop. vbgf.par - nls(Lgtcm ~ Linf *(1 - exp(-K * (Age - tzero))), start = c(K= 0.07, Linf = 177.1), data=alkdyr) I obtain an estimation of both parameters that are strongly correlated. Indeed after plotting Linf ~ K and fitting a linear regression I obtain a function (Linf = a + b*K) with R2= 0.8 and a = 215, b = -763. In this context, to take into account explicitly correlation between parameters, I decided to fit a new non linear regression derivate from VBGF but where Linf is expressed depending on K (I am most interested in K). To do so, I tried this model: vbgf.par - nls(Lgtcm ~ (a + (b*k)) *(1 - exp(-k * (Age - tzero))), start = c(k= 0.07, a= 215, b=-763), data=alkdyr) Unfortunately at this point I cannot go further as I get the error message singular gradient matrix at initial parameter estimates. I tried to use alg= plinear (which I am not sure I understand properly yet). If I give a starting value for a and b only, I have an error message stating step factor below minFactor (even when minFactor is set to 1000). Any help will be more than welcome as this is quite urgent Best, Xochitl C. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
[R] Non linear regression - Von Bertalanffy Growth Function - singular gradient matrix at initial parameter estimates
Dear all, I am trying to estimate VBGF parameters K and Linf using non linear regression and nls(). First I used a classic approach where I estimate both parameters together as below with alkdyr being a subset per year of my age-length-key database and running in a loop. vbgf.par - nls(Lgtcm ~ Linf *(1 - exp(-K * (Age - tzero))), start = c(K= 0.07, Linf = 177.1), data=alkdyr) I obtain an estimation of both parameters that are strongly correlated. Indeed after plotting Linf ~ K and fitting a linear regression I obtain a function (Linf = a + b*K) with R2= 0.8 and a = 215, b = -763. In this context, to take into account explicitly correlation between parameters, I decided to fit a new non linear regression derivate from VBGF but where Linf is expressed depending on K (I am most interested in K). To do so, I tried this model: vbgf.par - nls(Lgtcm ~ (a + (b*k)) *(1 - exp(-k * (Age - tzero))), start = c(k= 0.07, a= 215, b=-763), data=alkdyr) Unfortunately at this point I cannot go further as I get the error message singular gradient matrix at initial parameter estimates. I tried to use alg= plinear (which I am not sure I understand properly yet). If I give a starting value for a and b only, I have an error message stating step factor below minFactor (even when minFactor is set to 1000). Any help will be more than welcome as this is quite urgent Best, Xochitl C. -- Xochitl CORMON +33 (0)3 21 99 56 84 Doctorante en écologie marine et science halieutique PhD student in marine ecology and fishery science IFREMER Centre Manche Mer du Nord 150 quai Gambetta 62200 Boulogne-sur-Mer __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
Re: [R] Non linear regression - Von Bertalanffy Growth Function - singular gradient matrix at initial parameter estimates
These appear to be primarily statistics/nonlinear optimization issues that are off topic here, which is about R programming. Post on a statistics list like stats.stackexchange.com instead. Cheers, Bert Bert Gunter Data is not information. Information is not knowledge. And knowledge is certainly not wisdom. -- Clifford Stoll On Tue, Aug 18, 2015 at 9:08 AM, Xochitl CORMON xochitl.cor...@ifremer.fr wrote: Dear all, I am trying to estimate VBGF parameters K and Linf using non linear regression and nls(). First I used a classic approach where I estimate both parameters together as below with alkdyr being a subset per year of my age-length-key database and running in a loop. vbgf.par - nls(Lgtcm ~ Linf *(1 - exp(-K * (Age - tzero))), start = c(K= 0.07, Linf = 177.1), data=alkdyr) I obtain an estimation of both parameters that are strongly correlated. Indeed after plotting Linf ~ K and fitting a linear regression I obtain a function (Linf = a + b*K) with R2= 0.8 and a = 215, b = -763. In this context, to take into account explicitly correlation between parameters, I decided to fit a new non linear regression derivate from VBGF but where Linf is expressed depending on K (I am most interested in K). To do so, I tried this model: vbgf.par - nls(Lgtcm ~ (a + (b*k)) *(1 - exp(-k * (Age - tzero))), start = c(k= 0.07, a= 215, b=-763), data=alkdyr) Unfortunately at this point I cannot go further as I get the error message singular gradient matrix at initial parameter estimates. I tried to use alg= plinear (which I am not sure I understand properly yet). If I give a starting value for a and b only, I have an error message stating step factor below minFactor (even when minFactor is set to 1000). Any help will be more than welcome as this is quite urgent Best, Xochitl C. -- Xochitl CORMON +33 (0)3 21 99 56 84 Doctorante en écologie marine et science halieutique PhD student in marine ecology and fishery science IFREMER Centre Manche Mer du Nord 150 quai Gambetta 62200 Boulogne-sur-Mer __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
