Tidy a(n) mjoint object

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
tidy(
  x,
  component = "survival",
  conf.int = FALSE,
  conf.level = 0.95,
  boot_se = NULL,
  ...
)

Arguments

x: An mjoint object returned from joineRML::mjoint().
component: Character specifying whether to tidy the survival or the longitudinal component of the model. Must be either "survival" or "longitudinal". Defaults to "survival".
conf.int: Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.
conf.level: The confidence level to use for the confidence interval if conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.
boot_se: 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. 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.

Value

A tibble::tibble() with columns:

conf.high: Upper bound on the confidence interval for the estimate.
conf.low: Lower bound on the confidence interval for the estimate.
estimate: The estimated value of the regression term.
p.value: The two-sided p-value associated with the observed statistic.
statistic: The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
std.error: The standard error of the regression term.
term: The name of the regression term.

Examples


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

# feel free to ignore the following line—it allows {broom} to supply 
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("joineRML", quietly = TRUE)) {


# 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)
}

}

Arguments

See also

Value

Examples