# find optimal penalized zero-inflated model

### Description

Fit penalized zero-inflated models, generate multiple paths with varying penalty parameters, therefore determine optimal penalty parameters

### Usage

1 2 3 4 |

### Arguments

`formula` |
symbolic description of the model, see details. |

`data` |
argument controlling formula processing
via |

`weights` |
optional numeric vector of weights. If |

`subset` |
subset of data |

`na.action` |
how to deal with missing data |

`offset` |
Not implemented yet |

`standardize` |
logical value, should variables be standardized? |

`family` |
family to fit |

`penalty` |
penalty considered as one of |

`lambdaCountRatio, lambdaZeroRatio` |
Smallest value for |

`maxit.theta` |
For family="negbin", the maximum iteration allowed for estimating scale parameter theta. Note, the default value 1 is for computing speed purposes, and is typically too small and less desirable in real data analysis |

`gamma.count` |
The tuning parameter of the |

`gamma.zero` |
The tuning parameter of the |

`...` |
Other arguments passing to |

### Details

find optimal lambdaZeroRatio for penalized zero-inflated Poisson, negative binomial and geometric model

### Value

An object of class zipath with the optimal lambdaZeroRatio

### Author(s)

Zhu Wang <zwang@connecticutchildrens.org>

### References

Zhu Wang, Shuangge Ma, Michael Zappitelli, Chirag Parikh, Ching-Yun Wang and Prasad Devarajan (2014) *Penalized Count Data Regression with Application to Hospital Stay after Pediatric Cardiac Surgery*, *Statistical Methods in Medical Research*. 2014 Apr 17. [Epub ahead of print]

Zhu Wang, Shuangge Ma, Ching-Yun Wang, Michael Zappitelli, Prasad Devarajan and Chirag R. Parikh (2014)
*EM for Regularized Zero Inflated Regression Models with Applications to Postoperative Morbidity after Cardiac Surgery in Children*, *Statistics in Medicine*. 33(29):5192-208.

Zhu Wang, Shuangge Ma and Ching-Yun Wang (2015) *Variable selection for zero-inflated and overdispersed data with application to health care demand in Germany*, *Biometrical Journal*. 57(5):867-84.

### See Also

`zipath`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 | ```
## Not run:
## data
data("bioChemists", package = "pscl")
## inflation with regressors
## ("art ~ . | ." is "art ~ fem + mar + kid5 + phd + ment | fem + mar + kid5 + phd + ment")
fm_zip2 <- tuning.zipath(art ~ . | ., data = bioChemists, nlambda=10)
summary(fm_zip2)
fm_zinb2 <- tuning.zipath(art ~ . | ., data = bioChemists, family = "negbin", nlambda=10)
summary(fm_zinb2)
## End(Not run)
``` |