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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Xochitl CORMON

+33 (0)3 21 99 56 84

Doctorante en écologie marine et science halieutique

PhD student in marine ecology and fishery science

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IFREMER

Centre Manche Mer du Nord

150 quai Gambetta

62200 Boulogne-sur-Mer

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