# Estimation of Nonlinear Regression Parameters with CRS4HC

### Description

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).

### Usage

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### Arguments

`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. |

### Details

There are implemented methods for generic functions print, summary, plot.

### Value

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 |

### References

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

### Examples

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