broom (version 0.5.0)

tidy.mjoint: Tidy a(n) mjoint object

Description

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 cross models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

Usage

# 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, calcalutes 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 one row for each term in the regression. The tibble has columns:

term

The name of the regression term.

estimate

The estimated value of the regression term.

std.error

The standard error of the regression term.

statistic

The value of a statistic, almost always a T-statistic, to use in a hypothesis that the regression term is non-zero.

p.value

The two-sided p-value associated with the observed statistic.

conf.low

The low end of a confidence interval for the regression term. Included only if conf.int = TRUE.

conf.high

The high end of a confidence interval for the regression term. Included only if conf.int = TRUE.

See Also

tidy(), joineRML::mjoint(), joineRML::bootSE()

Other mjoint tidiers: glance.mjoint

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)
# }
# NOT RUN {
# }