The output of `birdie()`

is an object of class `birdie`

, which supports
many generic functions. Notably `coef.birdie()`

returns the main model
estimates of outcome given race, and `fitted.birdie()`

returns a table
analogous to the output of `bisg()`

with updated race probabilities.

## Usage

```
# S3 method for birdie
coef(object, subgroup = FALSE, ...)
# S3 method for birdie
fitted(object, ...)
# S3 method for birdie
residuals(object, x_only = FALSE, ...)
# S3 method for birdie
predict(object, adj = NULL, ...)
# S3 method for birdie
simulate(object, nsim = 1, seed = NULL, ...)
# S3 method for birdie
plot(x, log = FALSE, ...)
# S3 method for birdie
tidy(x, subgroup = FALSE, ...)
# S3 method for birdie
glance(x, ...)
# S3 method for birdie
augment(x, data, ...)
# S3 method for birdie
formula(x, ...)
# S3 method for birdie
nobs(object, ...)
# S3 method for birdie
vcov(object, ...)
# S3 method for birdie
print(x, ...)
# S3 method for birdie
summary(object, ...)
```

## Arguments

- object, x
A

`birdie`

model object- subgroup
If

`TRUE`

, return subgroup-level (rather than marginal) coefficient estimates as a 3D array.- ...
Potentially further arguments passed from other methods

- x_only
if

`TRUE`

, calculate fitted values using covariates only (i.e., without using surnames).- adj
A point in the simplex that describes how BISG probabilities will be thresholded to produce point predictions. The probabilities are divided by

`adj`

, then the racial category with the highest probability is predicted. Can be used to trade off types of prediction error. Must be nonnegative but will be normalized to sum to 1. The default is to make no adjustment.- nsim
The number of vectors to simulate. Defaults to 1.

- seed
Used to seed the random number generator. See

`stats::simulate()`

.- log
If

`TRUE`

, plot estimated probabilities on a log scale.- data
A data frame to augment with

`Pr(R | Y, X, S)`

probabilities

## Details

The internal structure of `birdie`

objects is not designed to be accessed
directly. The generics listed here should be used instead.

## Functions

`coef(birdie)`

: Return estimated outcome-given-race distributions.`fitted(birdie)`

: Return an updated race probability table.`bisg()`

estimates`Pr(R | G, X, S)`

; this table estimates`Pr(R | Y, G, X, S)`

.`residuals(birdie)`

: Return the residuals for the outcome variable. Useful in sensitivity analyses and to get an idea of how well race, location, names, etc. predict the outcome.`predict(birdie)`

: Create point predictions of individual race. Returns factor vector of individual race labels. Strongly not recommended for any kind of inferential purpose, as biases may be extreme and in unpredictable directions.`simulate(birdie)`

: Simulate race from the posterior distribution`Pr(R | Y, G, X, S, Theta-MAP)`

. Does not account for uncertainty in model parameters.`plot(birdie)`

: Visualize the estimated conditional distributions for a BIRDiE model.`tidy(birdie)`

: Put BIRDiE model coefficients in a tidy format.`glance(birdie)`

: Glance at a BIRDiE model.`augment(birdie)`

: Augment data with individual race predictions from a BIRDiE model.`formula(birdie)`

: Extract the formula used to specify a BIRDiE model.`nobs(birdie)`

: Return the number of observations used to fit a BIRDiE model.`vcov(birdie)`

: Return the estimated variance-covariance matrix for the BIRDiE model estimates, if available.`print(birdie)`

: Print a summary of the model fit.`summary(birdie)`

: Print a more detailed summary of the model fit.

## Examples

```
methods(class="birdie")
#> [1] augment coef fitted formula glance nobs plot
#> [8] predict print residuals simulate summary tidy vcov
#> see '?methods' for accessing help and source code
```