add_fitted_draws: Add draws from the posterior fit, predictions, or residuals of a model to a data frame

Description

Given a data frame and a model, adds draws from the (possibly transformed) posterior "fit" (aka the linear/link-level predictor), the posterior predictions of the model, or the residuals of a model to the data frame in a long format.

Usage

add_fitted_draws(newdata, model, value = ".value", ..., n = NULL,
  seed = NULL, re_formula = NULL, category = ".category",
  dpar = FALSE, scale = c("response", "linear"))
fitted_draws(model, newdata, value = ".value", ..., n = NULL,
  seed = NULL, re_formula = NULL, category = ".category",
  dpar = FALSE, scale = c("response", "linear"))
# S3 method for default
fitted_draws(model, newdata, ...)
# S3 method for stanreg
fitted_draws(model, newdata, value = ".value", ...,
  n = NULL, seed = NULL, re_formula = NULL, category = ".category",
  dpar = FALSE, scale = c("response", "linear"))
# S3 method for brmsfit
fitted_draws(model, newdata, value = ".value", ...,
  n = NULL, seed = NULL, re_formula = NULL, category = ".category",
  dpar = FALSE, scale = c("response", "linear"))
add_linpred_draws(newdata, model, value = ".value", ..., n = NULL,
  seed = NULL, re_formula = NULL, category = ".category",
  dpar = FALSE, scale = c("response", "linear"))
linpred_draws(model, newdata, value = ".value", ..., n = NULL,
  seed = NULL, re_formula = NULL, category = ".category",
  dpar = FALSE, scale = c("response", "linear"))
add_predicted_draws(newdata, model, prediction = ".prediction", ...,
  n = NULL, seed = NULL, re_formula = NULL, category = ".category")
predicted_draws(model, newdata, prediction = ".prediction", ...,
  n = NULL, seed = NULL, re_formula = NULL, category = ".category")
# S3 method for default
predicted_draws(model, newdata, ...)
# S3 method for stanreg
predicted_draws(model, newdata,
  prediction = ".prediction", ..., n = NULL, seed = NULL,
  re_formula = NULL, category = ".category")
# S3 method for brmsfit
predicted_draws(model, newdata,
  prediction = ".prediction", ..., n = NULL, seed = NULL,
  re_formula = NULL, category = ".category")
add_residual_draws(newdata, model, residual = ".residual", ...,
  n = NULL, seed = NULL, re_formula = NULL, category = ".category")
residual_draws(model, newdata, residual = ".residual", ..., n = NULL,
  seed = NULL, re_formula = NULL, category = ".category")
# S3 method for default
residual_draws(model, newdata, ...)
# S3 method for brmsfit
residual_draws(model, newdata, residual = ".residual",
  ..., n = NULL, seed = NULL, re_formula = NULL,
  category = ".category")

Arguments

newdata

Data frame to generate predictions from. If omitted, most model types will generate predictions from the data used to fit the model.

model

A supported Bayesian model fit that can provide fits and predictions. Supported models are listed in the second section of tidybayes-models: Models Supporting Prediction. While other functions in this package (like spread_draws) support a wider range of models, to work with add_fitted_draws and add_predicted_draws a model must provide an interface for generating predictions, thus more generic Bayesian modeling interfaces like runjags and rstan are not directly supported for these functions (only wrappers around those languages that provide predictions, like rstanarm and brm, are supported here).

value

The name of the output column for fitted_draws; default ".value".

...

Additional arguments passed to the underlying prediction method for the type of model given.

The number of draws per prediction / fit to return, or NULL to return all draws.

seed

A seed to use when subsampling draws (i.e. when n is not NULL).

re_formula

formula containing group-level effects to be considered in the prediction. If NULL (default), include all group-level effects; if NA, include no group-level effects. Some model types (such as brm and stanreg-objects) allow marginalizing over grouping factors by specifying new levels of a factor in newdata. In the case of brm, you must also pass allow_new_levels = TRUE here to include new levels (see predict.brmsfit).

category

For some ordinal, multinomial, and multivariate models (notably, brm models but not stan_polr models), multiple sets of rows will be returned per input row for fitted_draws or predicted_draws, depending on the model type. For ordinal/multinomial models, these rows correspond to different categories of the response variable. For multivariate models, these correspond to different response variables. The category argument specifies the name of the column to put the category names (or variable names) into in the resulting data frame. The default name of this column (".category") reflects the fact that this functionality was originally used only for ordinal models and has been re-used for multivariate models. The fact that multiple rows per response are returned only for some model types reflects the fact that tidybayes takes the approach of tidying whatever output is given to us, and the output from different modeling functions differs on this point. See vignette("tidy-brms") and vignette("tidy-rstanarm") for examples of dealing with output from ordinal models using both approaches.

dpar

For fitted_draws and add_fitted_draws: Should distributional regression parameters be included in the output? Valid only for models that support distributional regression parameters, such as submodels for variance parameters (as in brm). If TRUE, distributional regression parameters are included in the output as additional columns named after each parameter (alternative names can be provided using a list or named vector, e.g. c(sigma.hat = "sigma") would output the "sigma" parameter from a model as a column named "sigma.hat"). If FALSE (the default), distributional regression parameters are not included.

scale

Either "response" or "linear". If "response", results are returned on the scale of the response variable. If "linear", fitted values are returned on the scale of the linear predictor.

prediction

The name of the output column for predicted_draws; default ".prediction".

residual

The name of the output column for residual_draws; default ".residual".

Value

A data frame (actually, a tibble) with a .row column (a factor grouping rows from the input newdata), .chain column (the chain each draw came from, or NA if the model does not provide chain information), .iteration column (the iteration the draw came from, or NA if the model does not provide iteration information), and a .draw column (a unique index corresponding to each draw from the distribution). In addition, fitted_draws includes a column with its name specified by the value argument (default is .value) containing draws from the (transformed) linear predictor, and predicted_draws contains a .prediction column containing draws from the posterior predictive distribution. For convenience, the resulting data frame comes grouped by the original input rows.

Details

add_fitted_draws adds draws from (possibly transformed) posterior linear predictors (or "link-level" predictors) to the data. It corresponds to posterior_linpred in rstanarm or fitted.brmsfit in brms.

add_predicted_draws adds draws from posterior predictions to the data. It corresponds to posterior_predict in rstanarm or predict.brmsfit in brms.

add_fitted_draws and fitted_draws are alternate spellings of the same function with opposite order of the first two arguments to facilitate use in data processing pipelines that start either with a data frame or a model. Similarly, add_predicted_draws and predicted_draws are alternate spellings.

Given equal choice between the two, add_fitted_draws and add_predicted_draws are the preferred spellings.

add_linpred_draws and linpred_draws are alternative spellings of fitted_draws and add_fitted_draws for consistency with rstanarm terminology (specifically posterior_linpred).

Examples

Run this code

# NOT RUN {
library(ggplot2)
library(dplyr)

if (
  require("rstanarm", quietly = TRUE) &&
  require("modelr", quietly = TRUE)
) {

  theme_set(theme_light())

  m_mpg = stan_glm(mpg ~ hp * cyl, data = mtcars,
    # 1 chain / few iterations just so example runs quickly
    # do not use in practice
    chains = 1, iter = 500)

  # draw 100 fit lines from the posterior and overplot them
  mtcars %>%
    group_by(cyl) %>%
    data_grid(hp = seq_range(hp, n = 101)) %>%
    add_fitted_draws(m_mpg, n = 100) %>%
    ggplot(aes(x = hp, y = mpg, color = ordered(cyl))) +
    geom_line(aes(y = .value, group = paste(cyl, .draw)), alpha = 0.25) +
    geom_point(data = mtcars)

  # plot posterior predictive intervals
  mtcars %>%
    group_by(cyl) %>%
    data_grid(hp = seq_range(hp, n = 101)) %>%
    add_predicted_draws(m_mpg) %>%
    ggplot(aes(x = hp, y = mpg, color = ordered(cyl))) +
    stat_lineribbon(aes(y = .prediction), .width = c(.99, .95, .8, .5), alpha = 0.25) +
    geom_point(data = mtcars) +
    scale_fill_brewer(palette = "Greys")
}
# }