This function estimates the regression coefficients of a nonlinear regression function using least squares. The minimization is performed by the CRS algorithm with four competing local heuristics. Algorithm is described in Tvrdík et al. (2007).
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formula |
(obligatory) a nonlinear formula including variables and parameters |
data |
(obligatory) data frame in which to evaluate the variables in |
a |
(obligatory) a vector of length equal to number of parameters representing lower bounds of search space (bounds for parameters must be specified in the same order they appear on right-hand side of |
b |
(obligatory) a vector of length equal to number of parameters representing upper bounds of search space (bounds for parameters must be specified in the same order they appear on right-hand side of |
N |
(optional) size of population. Default value is |
my_eps |
(optional) is used for stopping condition. Default value is 1e-15. |
max_evals |
(optional) is used for stopping condition, specifies maximum number of objective function evaluations per dimension (dimension=nonlinear model parameter). Default value is 40000. |
delta |
(optional) controls the competition of local heuristics. Default value is 0.05. delta > 0. |
w0 |
(optional) controls the competition of local heuristics. Default value is 0.5. w0 > 0. |
There are implemented methods for generic functions print, summary, plot.
An S3 object of class crs4hc
. This object is a list of:
model |
a list of two items, includes estimates of nonlinear model parameters and minimal residual sum of squares |
algorithmInfo |
a list of three items with some internal info about algorithm run |
data |
a data frame that was passed to function as the |
other |
a list of four items which include info about nonlinear model |
Tvrdík, J., Křivý, I., and Mišík, L. Adaptive Population-based search: Application to Estimation of Nonlinear Regression Parameters. Computational Statistics and Data Analysis 52 (2007), 713–724. Preprint URL http://www1.osu.cz/~tvrdik/wp-content/uploads/CSDA-06SAS03e.pdf
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