R: Tidying methods for an rstanarm model

rstanarm_tidiers {broom} | R Documentation

## Tidying methods for an rstanarm model

### Description

These methods tidy the estimates from `stanreg-objects` (fitted model objects from the rstanarm package) into a summary.

### Usage

    ## S3 method for class 'stanreg'
    tidy(x, parameters = "non-varying", intervals = FALSE,
      prob = 0.9, ...)

    ## S3 method for class 'stanreg'
    glance(x, looic = FALSE, ...)

### Arguments

`x` | Fitted model object from the rstanarm package. See `stanreg-objects`.
`parameters` | One or more of `"non-varying"`, `"varying"`, `"hierarchical"`, `"auxiliary"` (can be abbreviated). See the Value section for details.
`intervals` | If `TRUE` columns for the lower and upper bounds of the `100*prob`% posterior uncertainty intervals are included. See `posterior_interval` for details.
`prob` | See `posterior_interval`.
`...` | For `glance`, if `looic=TRUE`, optional arguments to `loo.stanreg`.
`looic` | Should the LOO Information Criterion (and related info) be included? See `loo.stanreg` for details. Note: for models fit to very large datasets this can be a slow computation.

### Value

All tidying methods return a `data.frame` without rownames. The structure depends on the method chosen.

When `parameters="non-varying"` (the default), `tidy.stanreg` returns one row for each coefficient, with three columns:

`term` | The name of the corresponding term in the model.
`estimate` | A point estimate of the coefficient (posterior median).
`std.error` | A standard error for the point estimate based on `mad`. See the Uncertainty estimates section in `print.stanreg` for more details.

For models with group-specific parameters (e.g., models fit with `stan_glmer`), setting `parameters="varying"` selects the group-level parameters instead of the non-varying regression coefficients. Addtional columns are added indicating the `level` and `group`. Specifying `parameters="hierarchical"` selects the standard deviations and (for certain models) correlations of the group-level parameters.

Setting `parameters="auxiliary"` will select parameters other than those included by the other options. The particular parameters depend on which rstanarm modeling function was used to fit the model. For example, for models fit using `stan_glm.nb` the overdispersion parameter is included if `parameters="aux"`, for `stan_lm` the auxiliary parameters include the residual SD, R^2, and log(fit_ratio), etc.

If `intervals=TRUE`, columns for the `lower` and `upper` values of the posterior intervals computed with `posterior_interval` are also included.

`glance` returns one row with the columns

`algorithm` | The algorithm used to fit the model.
`pss` | The posterior sample size (except for models fit using optimization).
`nobs` | The number of observations used to fit the model.
`sigma` | The square root of the estimated residual variance, if applicable. If not applicable (e.g., for binomial GLMs), `sigma` will be given the value `1` in the returned object.

If `looic=TRUE`, then the following additional columns are also included:

`looic` | The LOO Information Criterion.
`elpd_loo` | The expected log predictive density (`elpd_loo = -2 * looic`).
`p_loo` | The effective number of parameters.

### See Also

`summary.stanreg`

### Examples

    ## Not run:
    fit <- stan_glmer(mpg ~ wt + (1|cyl) + (1+wt|gear), data = mtcars,
                      iter = 300, chains = 2)
    # non-varying ("population") parameters
    tidy(fit, intervals = TRUE, prob = 0.5)

    # hierarchical sd & correlation parameters
    tidy(fit, parameters = "hierarchical")

    # group-specific deviations from "population" parameters
    tidy(fit, parameters = "varying")

    # glance method
    glance(fit)
    glance(fit, looic = TRUE, cores = 1)

    ## End(Not run)

[Package broom version 0.4.4 Index]