augment.bage_mod {bage} | R Documentation |
Extract Data and Modelled Values
Description
Extract data and rates, probabilities, or means from a model object. The return value consists of the original data and one or more columns of modelled values.
Usage
## S3 method for class 'bage_mod'
augment(x, quiet = FALSE, ...)
Arguments
x |
Object of class |
quiet |
Whether to suppress messages.
Default is |
... |
Unused. Included for generic consistency only. |
Value
A tibble, with the original data plus one or more of the following columns:
-
.<outcome>
Corrected or extended version of the outcome variable, in applications where the outcome variable has missing values, or a data model is being used. -
.observed
'Direct' estimates of rates or probabilities, ie counts divided by exposure or size (in Poisson and binomial models.) -
.fitted
Draws of rates, probabilities, or means. -
.expected
Draws of expected values for rates or probabilities (in Poisson that include exposure, or in binomial models.)
Uncertain quantities are represented using rvecs.
Fitted vs unfitted models
augment()
is typically called on a fitted
model. In this case, the modelled values are
draws from the joint posterior distribution for rates,
probabilities, or means.
augment()
can, however, be called on an
unfitted model. In this case, the modelled values
are draws from the joint prior distribution.
In other words, the modelled values are informed by
model priors, and by values for exposure
, size
, or weights
,
but not by observed outcomes.
Imputed values for outcome variable
augment()
automatically imputes any missing
values for the outcome variable. If outcome variable
var
has one or more NA
s, then augment
creates a variable .var
holding original and imputed values.
Data model for outcome variable
If the overall model includes a data model
for the outcome variable var
,
then augment()
creates a new variable .var
containing
estimates of the true value for the outcome.
See Also
-
components()
Extract values for hyper-parameters -
tidy()
Short summary of a model -
mod_pois()
Specify a Poisson model -
mod_binom()
Specify a binomial model -
mod_norm()
Specify a normal model -
fit()
Fit a model -
is_fitted()
See if a model has been fitted -
unfit()
Reset a model -
datamods Overview of data models implemented in bage
Examples
set.seed(0)
## specify model
mod <- mod_pois(divorces ~ age + sex + time,
data = nzl_divorces,
exposure = population) |>
set_n_draw(n_draw = 100) ## smaller sample, so 'augment' faster
## fit model
mod <- mod |>
fit()
## draw from the posterior distribution
mod |>
augment()
## insert a missing value into outcome variable
divorces_missing <- nzl_divorces
divorces_missing$divorces[1] <- NA
## fitting model and calling 'augument'
## creates a new variable called '.divorces'
## holding observed and imputed values
mod_pois(divorces ~ age + sex + time,
data = divorces_missing,
exposure = population) |>
fit() |>
augment()
## specifying a data model for the
## original data also leads to a new
## variable called '.divorces'
mod_pois(divorces ~ age + sex + time,
data = nzl_divorces,
exposure = population) |>
set_datamod_outcome_rr3() |>
fit() |>
augment()