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, ... )

x | An |
---|---|

component | Character specifying whether to tidy the survival or
the longitudinal component of the model. Must be either |

conf.int | Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |

conf.level | The confidence level to use for the confidence interval
if |

boot_se | Optionally a |

... | Additional arguments. Not used. Needed to match generic
signature only. |

`tidy()`

, `joineRML::mjoint()`

, `joineRML::bootSE()`

Other mjoint tidiers:
`glance.mjoint()`

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.

if (FALSE) { # 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) }