glmmML.fit | R Documentation |

This function is called by `glmmML`

, but it can also be called
directly by the user.

glmmML.fit(X, Y, weights = rep(1, NROW(Y)), cluster.weights = rep(1, NROW(Y)), start.coef = NULL, start.sigma = NULL, fix.sigma = FALSE, cluster = NULL, offset = rep(0, nobs), family = binomial(), method = 1, n.points = 1, control = list(epsilon = 1.e-8, maxit = 200, trace = FALSE), intercept = TRUE, boot = 0, prior = 0)

`X` |
Design matrix of covariates. |

`Y` |
Response vector. Or two-column matrix. |

`weights` |
Case weights. Defaults to one. |

`cluster.weights` |
Cluster weights. Defaults to one. |

`start.coef` |
Starting values for the coefficients. |

`start.sigma` |
Starting value for the mixing standard deviation. |

`fix.sigma` |
Should sigma be fixed at start.sigma? |

`cluster` |
The clustering variable. |

`offset` |
The offset in the model. |

`family` |
Family of distributions. Defaults to binomial with logit link. Other possibilities are binomial with cloglog link and poisson with log link. |

`method` |
Laplace (1) or Gauss-hermite (0)? |

`n.points` |
Number of points in the Gauss-Hermite
quadrature. Default is |

`control` |
Control of the iterations. See |

`intercept` |
Logical. If TRUE, an intercept is fitted. |

`boot` |
Integer. If > 0, bootstrapping with |

`prior` |
Which prior distribution? 0 for "gaussian", 1 for "logistic", 2 for "cauchy". |

In the optimisation, "vmmin" (in C code) is used.

A list. For details, see the code, and `glmmML`

.

Göran Broström

Broström (2003)

`glmmML`

, `glmmPQL`

, and
`lmer`

.

x <- cbind(rep(1, 14), rnorm(14)) y <- rbinom(14, prob = 0.5, size = 1) id <- rep(1:7, 2) glmmML.fit(x, y, cluster = id)

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