tidy.mjoint {broom} | R Documentation

## 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 class '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)

    ## End(Not run)

[Package broom version 0.5.2 Index]