Augment accepts a model object and a dataset and adds
information about each observation in the dataset. Most commonly, this
includes predicted values in the
.fitted column, residuals in the
.resid column, and standard errors for the fitted values in a
column. New columns always begin with a
. prefix to avoid overwriting
columns in the original dataset.
Users may pass data to augment via either the
data argument or the
newdata argument. If the user passes data to the
it must be exactly the data that was used to fit the model
object. Pass datasets to
newdata to augment data that was not used
during model fitting. This still requires that at least all predictor
variable columns used to fit the model are present. If the original outcome
variable used to fit the model is not included in
newdata, then no
.resid column will be included in the output.
Augment will often behave differently depending on whether
newdata is given. This is because there is often information
associated with training observations (such as influences or related)
measures that is not meaningfully defined for new observations.
For convenience, many augment methods provide default
augment(fit) will return the augmented training data. In these
cases, augment tries to reconstruct the original data based on the model
object with varying degrees of success.
The augmented dataset is always returned as a tibble::tibble with the
same number of rows as the passed dataset. This means that the passed
data must be coercible to a tibble. If a predictor enters the model as part
of a matrix of covariates, such as when the model formula uses
survival::Surv(), it is represented
as a matrix column.
We are in the process of defining behaviors for models fit with various
na.action arguments, but make no guarantees about behavior when data is
missing at this time.
## S3 method for class 'mjoint' augment(x, data = x$data, ...)
A base::data.frame or
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
more information on the difference between population-level and
individual-level fitted values and residuals.
If fitting a joint model with a single longitudinal process,
make sure you are using a named
list to define the formula
for the fixed and random effects of the longitudinal submodel.
tibble::tibble() with one row for each original observation
with addition columns:
population-level fitted values for the j-th longitudinal process
individuals-level fitted values for the j-th longitudinal process
population-level residuals for the j-th longitudinal process
individual-level residuals for the j-th longitudinal process
# broom only skips running these examples because the example models take a # while to generate—they should run just fine, though! ## Not run: # load libraries for models and data library(joineRML) # fit a joint model with bivariate longitudinal outcomes data(heart.valve) hvd <- heart.valve[!is.na(heart.valve$log.grad) & !is.na(heart.valve$log.lvmi) & heart.valve$num <= 50, ] fit <- mjoint( formLongFixed = list( "grad" = log.grad ~ time + sex + hs, "lvmi" = log.lvmi ~ time + sex ), formLongRandom = list( "grad" = ~ 1 | num, "lvmi" = ~ time | num ), formSurv = Surv(fuyrs, status) ~ age, data = hvd, inits = list("gamma" = c(0.11, 1.51, 0.80)), timeVar = "time" ) # extract the survival fixed effects tidy(fit) # extract the longitudinal fixed effects tidy(fit, component = "longitudinal") # extract the survival fixed effects with confidence intervals tidy(fit, ci = TRUE) # extract the survival fixed effects with confidence intervals based # on bootstrapped standard errors bSE <- bootSE(fit, nboot = 5, safe.boot = TRUE) tidy(fit, boot_se = bSE, ci = TRUE) # augment original data with fitted longitudinal values and residuals hvd2 <- augment(fit) # extract model statistics glance(fit) ## End(Not run)
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