Description Usage Arguments Value Author(s) See Also

Calculate marginal effects for binary variables. Functions calculate for variables that are only in the mean equation, only in the variance equation, and variables in both.

1 2 3 4 5 | ```
discrete.margin_meanonly(beta, X, whichVars, etas, link, std.dev)
discrete.margin_varonly(delta, Z, whichVars, sdmodel, etas, link, std.dev)
discrete.margin_both(beta, X, delta, Z, BothEqLocs, sdmodel, etas, link, std.dev)
``` |

`beta` |
Coefficients for the mean equation. |

`X` |
Variable values for the mean equation. |

`whichVars` |
Numeric vector stating indexes of variables that are binary and marginal effects are desired. |

`etas` |
Inputs to link functions. |

`link` |
specifies the link function for the estimated model. |

`std.dev` |
The calculated standard deviation of the error terms. |

`delta` |
Coefficients for the variance equation. |

`Z` |
Variable values for the variance equation. |

`sdmodel` |
Expression used to calculate standard deviation. |

`BothEqLocs` |
Dataframe describing locations of binary variables that are in both the mean and variance equations. |

Numeric vector of marginal effects. Has as attributes calculated components that are used to calculate derivatives of marginal effects.

Nathan Carroll, nathan.carroll@ur.de

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