Zero inflated Gamma regression | R Documentation |

Zero inflated Gamma regression.

```
zigamma.reg(y, x, full = FALSE, tol = 1e-07, maxiters = 100)
```

`y` |
The dependent variable, a numerical vector with numbers, zeros and higher. |

`x` |
A numerical matrix with the indendent variables. We add, internally, the first column of ones. |

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

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

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

Two regression models are fitted, a binary logistic regression and a Gamma regression model to the non-zero responses.

Depending on whether "full" is TRUE or not different outputs are returned. In general, the regression coefficients, the iterations required by Newton-Raphson and the deviances are returned. If full is TRUE, a matrix with their standard errors and the Wald test statistics is returned as well.

Michail Tsagris.

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

Mills, Elizabeth Dastrup (2013). Adjusting for covariates in zero-inflated gamma and zero-inflated log-normal models for semicontinuous data. PhD thesis, University of Iowa.

` zigamma.mle, ztp.reg `

```
y <- rgamma(100, 4, 1)
y[sample(100, 10)] <- 0
x <- rnorm(100)
a <- zigamma.reg(y, x)
```

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