Description Usage Arguments Details Value Author(s) References Examples

Fits complex parametric models with intractable likelihood using the method proposed by Cox and Kartsonaki (2012).

1 2 |

`p` |
Number of parameters to be estimated. |

`q` |
Number of features / summary statistics. |

`n` |
Sample size. Usually equal to the number of observations in the data ( |

`r` |
Number of simulations to be run at each design point, in each iteration. |

`starting_values` |
A vector of starting values for the parameter vector. |

`h_vector` |
A vector of spacings |

`data_true` |
The dataset. |

`sim_data` |
A function which simulates data using the model to be fitted. |

`features` |
A function which calculates the features / summary statistics. |

`n_iter` |
Number of iterations of the algorithm to be performed. |

`print_results` |
If |

`variances` |
If |

Function `sim_data`

should simulate from the model, taking as arguments the sample size and the parameter vector.
Function `features`

must take as an argument the simulated data generated by `sim_data`

and calculate the features / summary statistics. The format of the dataset and the simulated data should be the same and should match the format needed by the function `features`

. Function `features`

must return a vector of length `q`

.

`estimates` |
The estimates of the parameters. |

`var_estimates` |
The covariance matrix of the final estimates. |

`L` |
The matrix of coefficients L. |

`sigma` |
The covariance matrix of the features. |

`zbar` |
The average values of the simulated features at each design point. |

`z_D` |
The values of the features calculated from the data. |

`ybar` |
The linear combinations of the simulated features at each design point. |

`y_D` |
The linear combinations of the features calculated from the data. |

Christiana Kartsonaki

Cox, D. R. and Kartsonaki, C. (2012). The fitting of complex parametric models. *Biometrika*, **99** (3): 741–747.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
# estimate the mean of a N(2, 1) distribution
sim_function <- function(n, mu) {
rnorm(n, unlist(mu), 1)
}
features_function <- function(data) {
a <- median(data)
b <- sum(data) - (min(data) + max(data))
c <- (min(data) + max(data)) / 2
return(c(a, b, c))
}
fit1 <- fit.model(p = 1, q = 3, n = 100, r = 100, starting_values = 5, h_vector = 0.1,
data_true = rnorm(100, 2, 1), sim_data = sim_function, features = features_function,
n_iter = 50, print_results = TRUE, variances = TRUE)
``` |

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