Description Usage Arguments Details Value References Examples

`qrmix`

estimates the components of a finite mixture model by using quantile regression to select a group of quantiles that satisfy an optimality criteria chosen by the user.

1 2 |

`formula` |
an object of class |

`data` |
an optional data frame that contains the variables in |

`k` |
number of clusters. |

`Ntau` |
an optional value that indicates the number of quantiles that will be considered for quantile regression comparison. |

`alpha` |
an optional value that will determine the minimum separation between the k quantiles that represent each of the k clusters. |

`lossFn` |
the loss function to be used to select the best combination of k quantiles. The available functions are |

`fitMethod` |
the method to be used for the final fitting. Use |

`xy` |
logical. If |

`...` |
additional arguments to be passed to the function determined in |

The optimality criteria is determined by the `lossFn`

parameter. If, for example, the default value is used (`lossFn = "Squared"`

), the `k`

quantiles selected will minimize the sum of squared residuals. Use `"Bisquare"`

or `"Huber"`

to make the method less sensitive to outliers.

`qrmix`

returns an object of class "qrmix"

`coefficients` |
a matrix with k columns that represent the coefficients for each cluster. |

`clusters` |
cluster assignment for each observation. |

`quantiles` |
the set of k quantiles that minimize the mean loss. |

`residuals` |
the residuals, response minus fitted values. |

`fitted.values` |
the fitted values. |

`call` |
the matched call. |

`xy` |
the data used if xy is set to |

Emir, B., Willke, R. J., Yu, C. R., Zou, K. H., Resa, M. A., and Cabrera, J. (2017), "A Comparison and Integration of Quantile Regression and Finite Mixture Modeling" (submitted).

1 2 3 4 5 6 7 8 9 10 | ```
data(blood.pressure)
#qrmix model using default function values:
mod1 = qrmix(bmi ~ ., data = blood.pressure, k = 3)
summary(mod1)
#qrmix model using Bisquare loss function and refitted with robust regression:
mod2 = qrmix(bmi ~ age + systolic + diastolic + gender, data = blood.pressure, k = 3,
Ntau = 25, alpha = 0.1, lossFn = "Bisquare", fitMethod = "rlm")
summary(mod2)
``` |

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