View source: R/regression_models.R

Scaled logistic regression | R Documentation |

Scaled logistic regression.

```
sclr(y, x, full = FALSE, tol = 1e-07, maxiters = 100)
```

`y` |
The dependent variable; a numerical vector with two values (0 and 1). |

`x` |
A matrix with the data, where the rows denote the samples (and the two groups) and the columns are the variables. This can be a matrix or a data.frame (with factors). |

`full` |
If this is FALSE, the coefficients and the log-likelihood will be returned only. If this is TRUE, more information is returned. |

`tol` |
The tolerance value to terminate the Newton-Raphson algorithm. |

`maxiters` |
The max number of iterations that can take place in each regression. |

When full is FALSE a list including:

`theta` |
The estimated |

`be` |
The estimated regression coefficients. |

`loglik` |
The log-likelihood of the model. |

`iters` |
The number of iterations required by Newton-Raphson. |

When full is TRUE a list including:

`info` |
The estimated |

`loglik` |
The log-likelihood. |

`iters` |
The number of iterations required by Newton-Raphson. |

Michail Tsagris.

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.

Dunning AJ (2006). A model for immunological correlates of protection. Statistics in Medicine, 25(9): 1485-1497. https://doi.org/10.1002/sim.2282.

```
propols.reg
```

```
x <- matrix(rnorm(100 * 2), ncol = 2)
y <- rbinom(100, 1, 0.6) ## binary logistic regression
a <- sclr(y, x)
```

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