# Logistic regressions with constrained FP and FN misclassifications

### Description

Fit logistic regressions with misclassification correction. The FP and FN parameters are constrained to be equal.

### Usage

1 | ```
logistic4p.e(x, y, initial, max.iter = 1000, epsilon = 1e-06, detail = FALSE)
``` |

### Arguments

`x, y` |
x is a data frame or data matrix containing the predictor variables and y is the vector of outcomes. The number of rows in x must be the same as the length of y. |

`initial` |
starting values for the parameters in the model(the misclassification parameter and those in the linear predictor); if not specified, the default initials are 0 for the misclassification parameters and estimates obtained from the logistic regression for the parameters in the linear predictor. |

`max.iter` |
a positive integer giving the maximal number of iterations; if it is reached, the algorithm will stop. |

`epsilon` |
a positive convergence tolerance epsilon; the iterations converge when max(|par-par_old|)<epsilon. |

`detail` |
logical indicating if the itermediate output should be printed after each iteration. |

### Value

`estimates` |
a named matrix of estimates including parameter estimates, standard errors, z-scores, and p-values. |

`n.iter ` |
an integer giving the number of iteration used |

`d` |
the actual max absolute difference of the parameters of the last two iterations, d=max(|par.final-par_old|). |

`loglike` |
loglikelihood evaluated at the parameter estimates. |

`AIC` |
Akaike Information Criterion. |

`BIC` |
Bayesian Information Criterion. |

`converged` |
logical indicating whether the current procedure converged or not. |

### Author(s)

Haiyan Liu and Zhiyong Zhang

### Examples

1 2 3 4 5 6 7 |