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 100000000000).
Any help will be more than welcome as this is quite urgent....
Best,
Xochitl C.
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