Sentence examples for using the optim function from inspiring English sources

Optimization can also be tackled through simulated annealing (Kirkpatrick et al. 1983) using the optim function of the R. Reasonable starting values are chosen such that the estimated pdf of a sub-model fits well the histogram of the data.

We can obtain the MLE (widehat {boldsymbol {theta }}) of θ by maximizing the log-likelihood (17) either directly in R using the optim function, in SAS using the NLMixed procedure and in other statistical software or by solving the nonlinear likelihood equations obtained by differentiating (17).

The latter approach was implemented using the optim function.

The model was fitted using the optim function in the R software package (R Core Team, 2014) using the default Nelder Mead algorithm and a tolerance value of.

Using the optim function in R, we obtained an estimate of 0.678 for the shape parameter, and 0.02 for the rate parameter (n = 28, R ≈ 0.92).

Mixed beta functions were fitted to the CC100 CTOTAL frequency distributions of each coral colony, and maximum likelihood parameter values were solved using the optim function in R (L-BFGS-B method; [ 68]).

Individual (marginal) concentration response functions were fit to a Hill function with Hill parameter n=1, using the optim minimization function (specifying BFGS, a quasi-Newton method) in R (2.4.1; The R Project for Statistical Computing, http://www.r-project.org).org

We use the optim routine in the R software.

Maximum likelihood estimates were reached using the optim and dbetabinom functions in R62 (version × 64 3.2.3), which are included in the stats62 and emdbook63 packages respectively.

The likelihood-based analyses were performed in R (http://www.r-project.org) using the optim command to directly maximize the likelihood.

The log-likelihood for the model parameters can be maximized either directly by using the R (optim function), SAS (PROC NLMIXED), Ox program (MaxBFGS sub-routine) or by solving the nonlinear likelihood equations obtained by differentiating the log-likelihood.