Xtykutl

I try to estimate Bass Curves to analyse diffusion of innovation for different groups. Until now I use nlsLM() of the minpack.lm package to estimate the parameter of the curve/to fit the curve. I loop through different starting values to estimate the best fit using this command for the different starting values:

Bass.nls <- nlsLM(datapoints ~ M * (((P + Q)^2/P) * exp(-(P + Q) * time))/(1 + (Q/P) * exp(-(P + Q) * time))^2

                , start = list(M=m_start, P= p_start, Q=q_start)

                , trace = F

                , control = list(maxiter = 100, warnOnly = T) )

Since some groups have little data points many do not converge.

Venkatesan and Kumar (2002) suggest to use a Genetic Algorithm approach for bass model estimations when data is scarce. I have found some packages that implement GA in R (like GA, genalg, gafit). However, since I am new to the field, I don't know which package to use and how to use the bass formula in the packages.

• Is there a package you would recommend for this kind of estimation?


• If yes, is there an example for how to include the formula of the bass model in the code of the package?