Description Usage Arguments Details Value Author(s) References See Also Examples

This function controls the arguments to be passed to routines written in C for LQMM estimation. The optimization algorithm is based on the gradient of the Laplace log–likelihood (Bottai, Orsini and Geraci, 2014; Geraci and Bottai, 2014).

1 2 | ```
lqmm.fit.gs(theta_0, x, y, z, weights, cov_name, V, W, sigma_0, tau,
group, control)
``` |

`theta_0` |
starting values for the linear predictor. |

`x` |
the model matrix for fixed effects (see details). |

`y` |
the model response (see details). |

`z` |
the model matrix for random effects (see details). |

`weights` |
the weights used in the fitting process (see details). |

`cov_name` |
variance–covariance matrix of the random effects. Default is |

`V` |
nodes of the quadrature. |

`W` |
weights of the quadrature. |

`sigma_0` |
starting value for the scale parameter. |

`tau` |
the quantile(s) to be estimated. |

`group` |
the grouping factor (see details). |

`control` |
list of control parameters used for optimization (see |

In `lqmm`

, see argument `fit`

for generating a list of arguments to be called by this function; see argument `covariance`

for alternative variance–covariance matrices.

NOTE: the data should be ordered by `group`

when passed to `lqmm.fit.gs`

(such ordering is performed by `lqmm`

).

An object of class "list" containing the following components:

`theta` |
a vector of coefficients, including the "raw" variance–covariance parameters (see |

`scale` |
the scale parameter. |

`gradient` |
the gradient. |

`logLik` |
the log–likelihood. |

`opt` |
number of iterations when the estimation algorithm stopped for lower (theta) and upper (scale) loop. |

.

Marco Geraci

Bottai M, Orsini N, Geraci M. (2014). A gradient search maximization algorithm for the asymmetric Laplace likelihood, Journal of Statistical Computation and Simulation (in press).

Geraci M and Bottai M (2014). Linear quantile mixed models. Statistics and Computing, 24(3), 461–479.

1 2 3 4 5 6 7 8 9 10 |

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