View source: R/verification-model.R

psglm | R Documentation |

`psglm`

is used to fit generalized linear models to the verification process. This function requires a symbolic formula of the linear predictor, and a specified regression model.

```
psglm(formula, data, model = "logit", test = FALSE, trace = TRUE, ...)
```

`formula` |
an object of class "formula": a symbolic description of the model to be fitted. |

`data` |
an optional data frame containing the variables in the model. |

`model` |
a specified model to be used in the fitting. The suggestion regression models are logit, probit and threshold. If |

`test` |
a logical value indicating whether p-values of the regression coefficients should be returned. |

`trace` |
switch for tracing estimation process. Default |

`...` |
optional arguments to be passed to |

`psglm`

estimates the verification probabilities of the patients. The suggestion model is designed as a list containing: logit, probit and threshold.

`psglm`

returns a list containing the following components:

`coeff` |
a vector of estimated coefficients. |

`values` |
fitted values of the model. |

`Hess` |
the Hessian of the measure of fit at the estimated coefficients. |

`x` |
a design model matrix. |

`formula` |
the formula supplied. |

`model` |
the model object used. |

`glm`

```
data(EOC)
out <- psglm(V ~ CA125 + CA153 + Age, data = EOC, test = TRUE)
```

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