cdfquantregFT {cdfquantreg} | R Documentation |

## CDF-Quantile Finite Tailed Probability Distributions

### Description

`cdfquantregFT`

is a function to fit a cdf quantile regression with a variety of finite tailed distributions. It can account for data that has boundary values.

### Usage

```
cdfquantregFT(
formula,
fd = NULL,
sd = NULL,
mu.fo = NULL,
inner = FALSE,
version = "V",
data,
family = NULL,
start = NULL,
ssn = 20,
control = cdfqr.control(...),
...
)
```

### Arguments

`formula` |
A formula object, with the dependent variable (DV) on the left of an ~ operator, and predictors on the right. For the part on the right of '~', the specification of the dispersion (sigma; first) and skewness (theta; second) submodels can be separated by '|'. So |

`fd` |
A string that specifies the parent distribution. At the moment, only "arcsinh", "cauchit" and "t2" can be used. See details. |

`sd` |
A string that specifies the child distribution. At the moment, only "arcsinh", "cauchy" and "t2" can be used. See details. |

`mu.fo` |
A formula object to indicate the predictors for the location submodel if the 3-parameter distribution is used, only input as |

`inner` |
A logic value that indicates if the inner ( |

`version` |
A string indicates that which version will be used. "V" is the tilt transformation while "W" indicates the Jones Pewsey transformation. |

`data` |
The data in a data.frame format |

`family` |
If 'fd' and 'sd' are not provided, the name of a member of the family of distributions can be provided (see below) for details of family functions) |

`start` |
The starting values for model fitting. If not provided, default values will be used. |

`ssn` |
The number of searches on optimal starting values to be performed. If model does not converge, can increase this number. |

`control` |
Control optimization parameters (See |

`...` |
Currently ignored. |

### Details

The cdfquantregFT function fits a quantile regression model with a distributions from the cdf-quantile finite tailed distributions. Here is the list of currently available distributions.

**Bimodal Shape Distributions**

Distribution | R input | Alternative Input | Available Version |

ArcSinh-ArcSinh | `fd = "arcsinh", sd = "arcsinh"` | `family = "arcsinh-arcsinh"` | `"V", "W"` |

ArcSinh-Cauchy | `fd = "arcsinh", sd = "cauchy"` | `family = "arcsinh-cauchy"` | `"V", "W"` |

Cauchit-ArcSinh | `fd = "cauchit", sd = "arcsinh"` | `family = "cauchit-arcsinh"` | `"V", "W"` |

Cauchit-Cauchy | `fd = "cauchit", sd = "cauchy"` | `family = "cauchit-cauchy"` | `"V", "W"` |

T2-T2 | `fd = "t2", sd = "t2"` | `family = "t2-cauchy"` | `"V", "W"` |

### Value

An object of class `cdfqrFT`

will be returned. Generic functions such as summary,print and coef can be used to extract output (see summary.cdfqr for more details about the generic functions that can be used).
Class of object is a list with the following output:

- coefficients
A named vector of coefficients.

- residuals
Raw residuals, the difference between the fitted values and the data.

- fitted
The fitted values, including full model fitted values, fitted values for the mean component, and fitted values for the dispersion component.

- rmse
The model root mean squared errors

- rmseLogit
The root mean squared errors between the logit of the fitted values, and the logit of the response values.

- vcov
The variance-covariance matrix of the coefficient estimates.

- AIC, BIC
Akaike's Information Criterion and Bayesian Information Criterion.

- deviance
The deviance for the model.

### Examples

```
data(cdfqrExampleData)
fit <- cdfquantregFT(pnurse ~ Ambulance |Ambulance ,
fd = "arcsinh", sd = "arcsinh", inner = FALSE, version = "V", data = yoon)
summary(fit)
```

*cdfquantreg*version 1.3.1-2 Index]