Glance at a(n) mjoint object
Glance accepts a model object and returns a tibble::tibble()
with exactly one row of model summaries. The summaries are typically
goodness of fit measures, p-values for hypothesis tests on residuals,
or model convergence information.
Glance never returns information from the original call to the modeling function. This includes the name of the modeling function or any arguments passed to the modeling function.
Glance does not calculate summary measures. Rather, it farms out these
computations to appropriate methods and gathers the results together.
Sometimes a goodness of fit measure will be undefined. In these cases
the measure will be reported as NA.
Glance returns the same number of columns regardless of whether the
model matrix is rank-deficient or not. If so, entries in columns
that no longer have a well-defined value are filled in with an NA
of the appropriate type.
## S3 method for class 'mjoint' glance(x, ...)
x |
An |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
A tibble::tibble() with exactly one row and columns:
AIC |
Akaike's Information Criterion for the model. |
BIC |
Bayesian Information Criterion for the model. |
logLik |
The log-likelihood of the model. [stats::logLik()] may be a useful reference. |
sigma2_j |
The square root of the estimated residual variance for the j-th longitudinal process |
Other mjoint tidiers:
tidy.mjoint()
if (requireNamespace("joineRML", quietly = TRUE)) {
## 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)
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