Glance accepts a model object and returns a `tibble::tibble()`

with exactly one row of model summaries. The summaries are typically
goodness of fit measures, p-values for hypothesis tests on residuals,
or model convergence information.

Glance never returns information from the original call to the modeling function. This includes the name of the modeling function or any arguments passed to the modeling function.

Glance does not calculate summary measures. Rather, it farms out these
computations to appropriate methods and gathers the results together.
Sometimes a goodness of fit measure will be undefined. In these cases
the measure will be reported as `NA`

.

Glance returns the same number of columns regardless of whether the
model matrix is rank-deficient or not. If so, entries in columns
that no longer have a well-defined value are filled in with an `NA`

of the appropriate type.

## Usage

```
# S3 method for mjoint
glance(x, ...)
```

## Arguments

- x
An

`mjoint`

object returned from`joineRML::mjoint()`

.- ...
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`

. Two exceptions here are:

## See also

Other mjoint tidiers:
`tidy.mjoint()`

## Value

A `tibble::tibble()`

with exactly one row and columns:

- AIC
Akaike's Information Criterion for the model.

- BIC
Bayesian Information Criterion for the model.

- logLik
The log-likelihood of the model. [stats::logLik()] may be a useful reference.

- sigma2_j
The square root of the estimated residual variance for the j-th longitudinal process

## Examples

```
# broom only skips running these examples because the example models take a
# while to generate—they should run just fine, though!
if (FALSE) {
# 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)
}
```