Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
# S3 method for mjoint
tidy(
x,
component = "survival",
conf.int = FALSE,
conf.level = 0.95,
boot_se = NULL,
...
)
An mjoint
object returned from joineRML::mjoint()
.
Character specifying whether to tidy the survival or
the longitudinal component of the model. Must be either "survival"
or
"longitudinal"
. Defaults to "survival"
.
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to FALSE
.
The confidence level to use for the confidence interval
if conf.int = TRUE
. Must be strictly greater than 0 and less than 1.
Defaults to 0.95, which corresponds to a 95 percent confidence interval.
Optionally a bootSE
object from joineRML::bootSE()
. If
specified, calculates confidence intervals via the bootstrap. Defaults to
NULL
, in which case standard errors are calculated from the
empirical information matrix.
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in ...
, where they will be ignored. If the misspelled
argument has a default value, the default value will be used.
For example, if you pass conf.lvel = 0.9
, all computation will
proceed using conf.level = 0.95
. Additionally, if you pass
newdata = my_tibble
to an augment()
method that does not
accept a newdata
argument, it will use the default value for
the data
argument.
tidy()
, joineRML::mjoint()
, joineRML::bootSE()
Other mjoint tidiers:
glance.mjoint()
A tibble::tibble()
with columns:
Upper bound on the confidence interval for the estimate.
Lower bound on the confidence interval for the estimate.
The estimated value of the regression term.
The two-sided p-value associated with the observed statistic.
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
The standard error of the regression term.
The name of the regression term.
# broom only skips running these examples because the example models take a
# while to generate—they should run just fine, though!
if (FALSE) {
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("joineRML", quietly = TRUE)) {
# 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)
}
}