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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.


# S3 method for mjoint
glance(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:

  • tidy() methods will warn when supplied an exponentiate argument if it will be ignored.

  • augment() methods will warn when supplied a newdata argument if it will be ignored.

See also

glance(), joineRML::mjoint()

Other mjoint tidiers: tidy.mjoint()


A tibble::tibble() with exactly one row and columns:


Akaike's Information Criterion for the model.


Bayesian Information Criterion for the model.


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


The square root of the estimated residual variance 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!
if (FALSE) {

# load libraries for models and data

# fit a joint model with bivariate longitudinal outcomes

hvd <- heart.valve[!$log.grad) &
  !$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

# 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