Description Usage Arguments Details Value

This function estimates the predictive effects and levels for variables within a model using the delta method.

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`mod` |
model object, currently only support those of class |

`var_interest` |
name of the variable of interest, must correspond to a covariate in the model |

`data` |
data.frame that margins should run over, defaults changes based on class-specific method |

`weights` |
numeric, vector of weights used to generate predicted levels,
defaults changes based on class-specific method. Must be equal to the number
of rows in |

`vcov_mat` |
the variance-covariance matrix, defaults changes based on class-specific method |

`dof` |
integer, the degrees of freedom used for the T statistic in an OLS model, defaults changes based on class-specific method |

`type` |
either |

`base_rn` |
numeric, if |

`at_var_interest` |
vector, if type == 'levels', the values for the
variable of interest at which levels should be calculated.
If |

`at` |
list, should be in the format of |

`cofint` |
numeric, confidence interval (must be less than 1), defaults to 0.95 |

`...` |
additional parameters passed to class-specific methods |

The variable for the predictive margin is specified by `var_interest`

. If
margins are only needed at particular values of `var_interest`

,
`at_var_interest`

should be used. If margins of `var_interest`

are
needed at across the levels of a *different* variable in the model,
`at`

should be used.

If higher-order polynomial terms (e.g. *y ~ x + x^2*) are added
using the R function `poly`

, the `raw = TRUE`

argument should be used to include the basic polynomial terms
instead of orthogonal polynomial terms. If orthogonal polynomials are used,
`marg`

will fail when the user specifies `at`

for a small set
of values for the variable in question (e.g. `at = list(x = 10)`

),
since `poly`

needs more data to calculate orthogonal polynomials
(e.g. `poly(10, 2)`

fails, but `poly(c(10, 8, 3), 2)`

will run).

P values are calculated with T tests for gaussian families, and Z tests
otherwise. If a new variance-covariance matrix is provided (e.g. for
clustering standard errors), the degrees of freedom for the T test / p-value
calculation may need to be specified using `dof`

.

This function currently only supports `glm`

and
`ivreg`

objects. If you would like to use `lm`

objects, consider running a `glm`

with family `gaussian`

.

When calculating predicted levels and effects for models built using weights,
`marg`

returns weighted averages for levels and effects by default.
Users can remove this option by setting `weights = NULL`

.

list of dataframes with predicted margins/effects, standard errors, p-values, and confidence interval bounds

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