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I applied a logistic regression using the nlmrt package to describe the relationship between biomass and the distance and site variables. I got this result, but I am not sure to understand the p-value of the parameters. Do these results mean that my model describes the variation of biomass as a function of distance in a significant way?

library(nlmrt)
nlxb0 <- nlxb(Proportion.Y ~  1/(1 + exp(A*(B - Dist_FPAcentroid))), data=jo1, 
              start = c(A = -0.3, B =3.2), trace=TRUE)
print(nlxb0)

###

nlmrt class object: x 
residual sumsquares =  21.554  on  74 observations
    after  5    Jacobian and  6 function evaluations
  name            coeff          SE       tstat      pval      gradient    JSingval   
A              -0.301798        0.1226     -2.462    0.01622   6.874e-11       4.641  
B                3.18605         1.053      3.025   0.003446   1.389e-11      0.5192

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    $\begingroup$ Yes, but it's not trustworthy, because it's unlikely that a least squares procedure would be appropriate for observations following this model. This looks like an attempt to perform a logistic or Binomial regression. If so, use an appropriate generalized linear model procedure. Indeed, could you explain the sense in which a variable with "proportion" in its name measures a quantity like "biomass"? That could help identify a more suitable model. -- But you already know all this, as explained in an answer to an earlier question. $\endgroup$ Commented Dec 19, 2022 at 20:38
  • $\begingroup$ Thanks for you answer @whuber. I am working on the movement of fish from the centre of a protected area to a non-protected area. I applied to my dataset a logistic decay D=1/(1+exp(S⋅(I−d)) where D is the proportion of biomass in the middle of the reserve, d is the distance from the centre of the protected area, S is the slope and I is the inflection point. The parameters to be estimated in this model were S and I. The idea is that the I parameter corresponds to the ochthyological biomass export distance. $\endgroup$ Commented Dec 19, 2022 at 20:47

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