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Quarto GHA Workflow Runner committed Sep 26, 2023
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2 changes: 1 addition & 1 deletion .nojekyll
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8 changes: 7 additions & 1 deletion api/Model.html
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Expand Up @@ -297,7 +297,7 @@ <h1>Model</h1>
<h2 class="anchored" data-anchor-id="parameters">Parameters</h2>
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<td><code>True</code></td>
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<td><code>center_predictors</code></td>
<td>bool</td>
<td>If <code>True</code> (default), and if there is an intercept in the common terms, the data is centered by subtracting the mean. The centering is undone after sampling to provide the actual intercept in all distributional components that have an intercept. Note that this changes the interpretation of the prior on the intercept because it refers to the intercept of the centered data.</td>
<td><code>True</code></td>
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<td><code>extra_namespace</code></td>
<td>dict</td>
<td>Additional user supplied variables with transformations or data to include in the environment where the formula is evaluated. Defaults to <code>None</code>.</td>
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27 changes: 14 additions & 13 deletions api/Model.qmd
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Expand Up @@ -6,19 +6,20 @@ Specification of model class.

## Parameters

| Name | Type | Description | Default |
|-------------------|------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------|
| `formula` | str or bambi.formula.Formula | A model description written using the formula syntax from the ``formulae`` library. | _required_ |
| `data` | pandas.DataFrame | A pandas dataframe containing the data on which the model will be fit, with column names matching variables defined in the formula. | _required_ |
| `family` | str or bambi.families.Family | A specification of the model family (analogous to the family object in R). Either a string, or an instance of class ``bambi.families.Family``. If a string is passed, a family with the corresponding name must be defined in the defaults loaded at ``Model`` initialization. Valid pre-defined families are ``"bernoulli"``, ``"beta"``, ``"binomial"``, ``"categorical"``, ``"gamma"``, ``"gaussian"``, ``"negativebinomial"``, ``"poisson"``, ``"t"``, and ``"wald"``. Defaults to ``"gaussian"``. | `'gaussian'` |
| `priors` | dict | Optional specification of priors for one or more terms. A dictionary where the keys are the names of terms in the model, "common," or "group_specific" and the values are instances of class ``Prior``. If priors are unset, uses automatic priors inspired by the R rstanarm library. | `None` |
| `link` | str or Dict\[str, str\] | The name of the link function to use. Valid names are ``"cloglog"``, ``"identity"``, ``"inverse_squared"``, ``"inverse"``, ``"log"``, ``"logit"``, ``"probit"``, and ``"softmax"``. Not all the link functions can be used with all the families. If a dictionary, keys are the names of the target parameters and the values are the names of the link functions. | `None` |
| `categorical` | str or list | The names of any variables to treat as categorical. Can be either a single variable name, or a list of names. If categorical is ``None``, the data type of the columns in the ``data`` will be used to infer handling. In cases where numeric columns are to be treated as categorical (e.g., group specific factors coded as numerical IDs), explicitly passing variable names via this argument is recommended. | `None` |
| `potentials` | A list of 2-tuples. | Optional specification of potentials. A potential is an arbitrary expression added to the likelihood, this is generally useful to add constrains to models, that are difficult to express otherwise. The first term of a 2-tuple is the name of a variable in the model, the second a lambda function expressing the desired constraint. If a constraint involves n variables, you can pass n 2-tuples or pass a tuple which first element is a n-tuple and second element is a lambda function with n arguments. The number and order of the lambda function has to match the number and order of the variables names. | `None` |
| `dropna` | bool | When ``True``, rows with any missing values in either the predictors or outcome are automatically dropped from the dataset in a listwise manner. | `False` |
| `auto_scale` | bool | If ``True`` (default), priors are automatically rescaled to the data (to be weakly informative) any time default priors are used. Note that any priors explicitly set by the user will always take precedence over default priors. | `True` |
| `noncentered` | bool | If ``True`` (default), uses a non-centered parameterization for normal hyperpriors on grouped parameters. If ``False``, naive (centered) parameterization is used. | `True` |
| `extra_namespace` | dict | Additional user supplied variables with transformations or data to include in the environment where the formula is evaluated. Defaults to `None`. | `None` |
| Name | Type | Description | Default |
|---------------------|------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------|
| `formula` | str or bambi.formula.Formula | A model description written using the formula syntax from the ``formulae`` library. | _required_ |
| `data` | pandas.DataFrame | A pandas dataframe containing the data on which the model will be fit, with column names matching variables defined in the formula. | _required_ |
| `family` | str or bambi.families.Family | A specification of the model family (analogous to the family object in R). Either a string, or an instance of class ``bambi.families.Family``. If a string is passed, a family with the corresponding name must be defined in the defaults loaded at ``Model`` initialization. Valid pre-defined families are ``"bernoulli"``, ``"beta"``, ``"binomial"``, ``"categorical"``, ``"gamma"``, ``"gaussian"``, ``"negativebinomial"``, ``"poisson"``, ``"t"``, and ``"wald"``. Defaults to ``"gaussian"``. | `'gaussian'` |
| `priors` | dict | Optional specification of priors for one or more terms. A dictionary where the keys are the names of terms in the model, "common," or "group_specific" and the values are instances of class ``Prior``. If priors are unset, uses automatic priors inspired by the R rstanarm library. | `None` |
| `link` | str or Dict\[str, str\] | The name of the link function to use. Valid names are ``"cloglog"``, ``"identity"``, ``"inverse_squared"``, ``"inverse"``, ``"log"``, ``"logit"``, ``"probit"``, and ``"softmax"``. Not all the link functions can be used with all the families. If a dictionary, keys are the names of the target parameters and the values are the names of the link functions. | `None` |
| `categorical` | str or list | The names of any variables to treat as categorical. Can be either a single variable name, or a list of names. If categorical is ``None``, the data type of the columns in the ``data`` will be used to infer handling. In cases where numeric columns are to be treated as categorical (e.g., group specific factors coded as numerical IDs), explicitly passing variable names via this argument is recommended. | `None` |
| `potentials` | A list of 2-tuples. | Optional specification of potentials. A potential is an arbitrary expression added to the likelihood, this is generally useful to add constrains to models, that are difficult to express otherwise. The first term of a 2-tuple is the name of a variable in the model, the second a lambda function expressing the desired constraint. If a constraint involves n variables, you can pass n 2-tuples or pass a tuple which first element is a n-tuple and second element is a lambda function with n arguments. The number and order of the lambda function has to match the number and order of the variables names. | `None` |
| `dropna` | bool | When ``True``, rows with any missing values in either the predictors or outcome are automatically dropped from the dataset in a listwise manner. | `False` |
| `auto_scale` | bool | If ``True`` (default), priors are automatically rescaled to the data (to be weakly informative) any time default priors are used. Note that any priors explicitly set by the user will always take precedence over default priors. | `True` |
| `noncentered` | bool | If ``True`` (default), uses a non-centered parameterization for normal hyperpriors on grouped parameters. If ``False``, naive (centered) parameterization is used. | `True` |
| `center_predictors` | bool | If ``True`` (default), and if there is an intercept in the common terms, the data is centered by subtracting the mean. The centering is undone after sampling to provide the actual intercept in all distributional components that have an intercept. Note that this changes the interpretation of the prior on the intercept because it refers to the intercept of the centered data. | `True` |
| `extra_namespace` | dict | Additional user supplied variables with transformations or data to include in the environment where the formula is evaluated. Defaults to `None`. | `None` |

## Methods

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