id_plot_cov | R Documentation |

This function will calculate marginal effects, or the first derivative of the IRT/ideal point model with respect to the hierarchical covariate, separately for the two poles of the latent variable. These two marginal effects permit the interpretation of the effect of the covariate on with respect to either end of the latent variable.

```
id_plot_cov(
object,
calc_varying = T,
label_high = "Liberal",
label_low = "Conservative",
cov_type = "person_cov",
use_items = "all",
pred_outcome = NULL,
high_quantile = 0.95,
low_quantile = 0.05,
filter_cov = NULL,
new_cov_names = NULL,
recalc_vals = NULL
)
```

`object` |
A fitted |

`calc_varying` |
Whether to marginalize covariate effects over
discrimination parameters to calculate a meaningful quantity for the effect of
covariates on the latent scale (see vignette). Defaults to |

`label_high` |
What label to use on the plot for the high end of the latent scale |

`label_low` |
What label to use on the plot for the low end of the latent scale |

`cov_type` |
Either |

`pred_outcome` |
For discrete models with more than 2 categories,
or binary models with missing data, which outcome to predict. This should
be a character value that matches what the outcome was coded as in the data
passed to |

`high_quantile` |
The upper limit of the posterior density to use for calculating credible intervals |

`low_quantile` |
The lower limit of the posterior density to use for calculating credible intervals |

`filter_cov` |
A character vector of coefficients from covariate plots to exclude from plotting (should be the names of coefficients as they appear in the plots) |

`new_cov_names` |
A character vector of length equal to the number of covariates (plus 1 for the intercept) to change the default labels. To see the default labels, use the plot function with this option blank. The character vector should be of th form used by |

`recalc_vals` |
A character value of length three that can be used to create a new variable that is a sum of two other variables. The first two values of the character vector are the names of these parameters, while the third value is the name of the new combined variable. Note that if the parameters are renamed, the new names should be used in this option. |

Because the marginal effects are always with respect to a given
outcome/response, the outcome to be predicted must be specified
in `pred_outcome`

. If it is not specified, the function
will prompt you to select one of the outcome's values in the data.

The ends of the latent variable can be specified via the
`label_low`

and `label_high`

options, which will use those
labels in the ensuing plot.

To exclude parameters from the plot, use the `filter_cov`

option.
Note that the parameters must be specified using the underlying model
syntax (however they are labeled in the plot). You can also change
the names of parameters using the `new_cov_names`

option.

Note that the function produces a `ggplot2`

object, which can
be further modified with `ggplot2`

functions.

A `ggplot2`

plot that can be further customized with `ggplot2`

functions if need be.

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