Fractional polynomial regression with one independent variable | R Documentation |

Fractional polynomial regression with one independent variable.

```
fp(y, x, aa, di = NULL, type = "normal", full = FALSE, seb = FALSE,
tol = 1e-07, maxiters = 100)
```

`y` |
The dependent variable, a numerical vector. |

`x` |
A vector, the independent variable. |

`aa` |
A vector with two values indicating the range of the optimal value of |

`di` |
This is valid only for the Weibull regression. A vector with 1s and 0s indicating the censored value. The value of 1 means uncesored value, whereas the value of 0 means censored value. |

`type` |
The type of regression model: "normal", "logistic", "poisson", "spml" (SPML regression), "gamma", "normlog", "weibull", "negbin". |

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

`seb` |
Do you want the standard error of the estimates to be returned? TRUE or FALSE. |

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

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

The independent variable is power transformed and this function searches for the optimal power.

A list including:

`a` |
The power that yields the optimal fit. |

`mod` |
The model with the independent variable power transformed. |

Michail Tsagris.

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

Royston P. and Altman D. G. (1994). Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling. Journal of the Royal Statistical Society: Series C (Applied Statistics), 43(3): 429-453.

` logistic_only, poisson_only `

```
y <- rnorm(100)
x <- abs( rnorm(100) )
mod <- fp(y, x, c(-2, 2) )
```

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