Description Usage Arguments Value

View source: R/functions2eval.R

Make a MonteCarlo simulation from initial value on each schemes' coeficients. Initial sample has a 'nsample'*10 population number for each parameter in scheme, determined by a LHS (Latin Hypercubic Sample) with frontiers +- 5 times the value of coeficient: $$ A_i = [a_i-5a_i;a_i+5a_i] $$ The MonteCarlo design follows a similar way in genetic algorithms, where a initial population of value for parameter produce a L_i serie that is evaluated with choosen estatistic (stats). Always two equal dimenson population are compared and choosen combinations of parameters that produce a 'stats' minor than a o.5 quantile. That way ensures that best combinations be conserved if its performance is better than a half of total combinations 'stats' value. Finally a coeficients combination with best performance prevales and is considered the optimization. Furthermore, a 'nsample', 'max_iter' and 'stats' will define a result.

1 | ```
MonteCarlo(data, E_fun, func, coefs, nsample, max_iter, stats)
``` |

`data` |
Data frame with all atmospherics variables |

`E_fun` |
Function for emissivity |

`func` |
Function from cloud cover calculation |

`coefs` |
Coeficients in E_fun function |

`nsample` |
Number of population individuous |

`max_iter` |
Maximun number of iterations |

`stats` |
Statistical index to be minimized |

Vector with best combination of parameters

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