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 modelling
function. This includes the name of the modelling function or any
arguments passed to the modelling 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`

.

# 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` . 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. |

## See also

## Value

A `tibble::tibble()`

with exactly one row and columns:

AICAkaike's Information Criterion for the model.

BICBayesian Information Criterion for the model.

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

sigma2_jThe square root of the estimated residual variance for
the j-th longitudinal process

## Examples

# NOT RUN {
# Fit a joint model with bivariate longitudinal outcomes
library(joineRML)
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)
# }