# Generate a Generalized Linear Model (GLM) Response model

### Description

Constructs a `GlmResponse`

object.

### Usage

1 2 3 |

### Arguments

`formula` |
an object of class |

`family` |
a |

`data` |
(optional) data frame. |

`covariate` |
(optional) a matrix or a formula specifying additional covariates. See details. |

`combine` |
a function taking two arguments which produces a new predictor matrix for the GlmModel. The first argument is the prediction from a LearningModel and the second argument is matrix specified through the covariate argument. |

`parameters` |
an (optional) named list with (starting) values of the parameters. If no values are supplied, defaults are used. |

`ntimes` |
an optional vector with, for each repetition in the data, the total number of trials. |

`replicate` |
logical to indicate whether model parameters are identical for each replication in ntimes. |

`fixed` |
a logical vector indicating whether model parameters are fixed or free |

`base` |
assign one of the levels of the criterion variable the role of base category. |

`parStruct` |
a ParStruct object. If supplied, the fixed argument above will be ignored |

`subset` |
(optional) subset. |

### Details

The `GlmResponse`

function sets up a GLM to use as ResponseModel.
If the returned `GlmResponse`

is to be used as part of an
`McplModel`

, then the formula should contain an entry
for the predictions of the learningModel in that McplModel. When fitting the
McplModel, the predictions of the learningModel at the current iteration will be
used. It is therefore necessary to give the name of the predictions of the
learning model in the formula, so that this variable can be replaced with
the current variable in the estimation. Ideally, the variable specified by
`learnmmodpred`

should not be contained in the data.frame specified
by `data`

.

### Value

A (fitted) object of class `GlmResponse`

.

### Author(s)

Maarten Speekenbrink