boot.joint  R Documentation 
joint
objectUse an existing model fit by joint
along with the data object originally
used and obtain a mean estimate, standard errors and 95% confidence interval using the
bootstrap. The original data is resampled by subject, not by observation.
boot.joint(
fit,
data,
boot.size = NULL,
nboot = 100L,
replace = TRUE,
progress = TRUE,
use.MLEs = TRUE,
control = list()
)
fit 
a joint model fit by the 
data 
the original data used to fit the above joint model. 
boot.size 
integer, specifies the number of subjects to resample in the bootstrapping
approach. The default value is 
nboot 
integer, specifies the number of bootstrap samples, default value is

replace 
logical, should sampling be done with replacement? Defaults to

progress 
logical, should a text progress bar showing overall progress be shown
and updated after each successful bootstrapped model fit? Defaults to 
use.MLEs 
logical, should the MLEs of the 
control 
a list of control arguments, with same possible arguments as shown in

A list of class boot.joint
which contains the MLEs from supplied joint
object, as well as the bootstrapped summaries and some model/computation information.
James Murray (j.murray7@ncl.ac.uk).
joint
vcov.joint
# Bivariate fit on PBC data 
data(PBC)
# Subset data and remove NAs
PBC < subset(PBC, select = c('id', 'survtime', 'status', 'drug', 'time',
'albumin', 'platelets'))
PBC < na.omit(PBC)
# Specify bivariate fit
long.formulas < list(
albumin ~ time*drug + (1 + timeid),
platelets ~ time * drug + (1 + timeid)
)
surv.formula < Surv(survtime, status) ~ drug
fit < joint(long.formulas, surv.formula, PBC, family = list('gaussian', 'poisson'))
# Set 50 bootstraps, with lower absolute tolerance and convergence of 'either'.
BOOT < boot.joint(fit, PBC, nboot = 50L, control = list(tol.abs = 5e3, conv = 'either'),
use.MLEs = TRUE)
BOOT # Print to console via S3 method
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