View source: R/joinermltidiers.R
augment.mjoint  R Documentation 
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 .se.fit
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 data
argument,
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 data
or
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 data
arguments,
so that 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. At this time, tibbles do not
support matrixcolumns. This means you should not specify a matrix
of covariates in a model formula during the original model fitting
process, and that splines::ns()
, stats::poly()
and
survival::Surv()
objects are not supported in input data. If you
encounter errors, try explicitly passing a tibble, or fitting the original
model on data in a tibble.
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, ...)
x 
An 
data 
A base::data.frame or 
... 
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in

See joineRML::fitted.mjoint()
and joineRML::residuals.mjoint()
for
more information on the difference between populationlevel and
individuallevel 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.
A tibble::tibble()
with one row for each original observation
with addition columns:
.fitted_j_0 
populationlevel fitted values for the jth longitudinal process 
.fitted_j_1 
individualslevel fitted values for the jth longitudinal process 
.resid_j_0 
populationlevel residuals for the jth longitudinal process 
.resid_j_1 
individuallevel residuals for the jth longitudinal process 
# broom only skips running these examples because the example models take a # while to generateâ€”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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