cdfquantregH {cdfquantreg} | R Documentation |

## Zero/One inflated CDF-Quantile Probability Distributions

### Description

`cdfquantregH`

is the a function to fit a Zero/One inflated CDF-Quantile regression with a variety of distributions .

### Usage

```
cdfquantregH(
formula,
zero.fo = ~1,
one.fo = ~1,
fd = NULL,
sd = NULL,
data,
family = NULL,
type = "ZI",
start = NULL,
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 location and dispersion submodels can be separated by '|'. So |

`zero.fo` |
A formula object to indicate the predictors for the zero component, only input as |

`one.fo` |
A formula object to indicate the predictors for the one component, only input as |

`fd` |
A string that specifies the parent distribution. |

`sd` |
A string that specifies the child distribution. |

`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 |

`type` |
A string variable to indicate whether the model is zero-inflated |

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

`control` |
Control optimization parameters (See |

`...` |
Currently ignored. |

### Details

The cdfquantreg function fits a quantile regression model with a distributions from the cdf-quantile family selected by the user (Smithson and Shou, 2015). The model is specified in a two-part formula, one part containing the predictors of the location parameter, and the second part containing the predictors of the dispersion parameter. The models are fitted in two stages, the first of which uses the Nelder-Mead algorithm and the second of which takes the estimates from the first stage and applies the BFGS algorithm to refine the estimates.

### Value

An object of class `cdfqrH`

will be returned. Generic functions such as summary,print (e.g., print.cdfqr) 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.

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

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

### Examples

```
data(cdfqrExampleData)
# For one-inflated model
ipcc_high <- subset(IPCC, mid == 1 & high == 1 & prob!=0)
fit <- cdfquantregH(prob ~ valence | valence,one.fo = ~valence,
fd ='t2',sd ='t2', type = "OI", data = ipcc_high)
summary(fit)
# For zero-inflated model
ipcc_low <- subset(IPCC, mid == 0 & high == 0 & prob!=1)
fit <- cdfquantregH(prob ~ valence | valence, zero.fo = ~valence,
fd ='t2',sd ='t2', type = "ZI", data = ipcc_low)
# For zero &one-inflated model
ipcc_mid <- subset(IPCC, mid == 1 & high == 0)
fit <- cdfquantregH(prob ~ valence | valence, zero.fo = ~valence,
one.fo = ~valence,
fd ='t2',sd ='t2', type = "ZO", data = ipcc_mid)
```

*cdfquantreg*version 1.3.1-2 Index]