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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
  component = "survival", = 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 = 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. 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


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) {

# 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