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, ...)



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

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


if (FALSE) { # Fit a joint model with bivariate longitudinal outcomes library(joineRML) data(heart.valve) 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 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) }