copula_additive {LongDecompHE} | R Documentation |
Copula regression models with semiparametric additive hazards margins for bivariate interval censoring and semi-competing risks.
Description
Fits a copula model with semiparametric additive hazards marginsfor bivariate interval censoring and semi-competing risks.
Usage
copula_additive(
data,
var_list,
l1 = 0,
u1,
m1 = 3,
l2 = 0,
u2,
m2 = 3,
method = "Newton",
iter = 1000,
stepsize = 1e-05,
hes = TRUE,
control = list(),
eta_ini = NULL
)
Arguments
data |
a data frame; must have |
var_list |
the list of covariates to be fitted into the copula model. |
l1 |
Left boundary of event 1 time interval. |
u1 |
Right boundary of event 1 time interval. |
m1 |
Degree of Bernstein polynomial for event 1. |
l2 |
Left boundary of event 2 time interval. |
u2 |
Right boundary of event 2 time interval. |
m2 |
Degree of Bernstein polynomial for event 2. |
method |
optimization method (see |
iter |
number of iterations when |
stepsize |
size of optimization step when method is |
hes |
default is |
control |
a list of control parameters for methods other than |
eta_ini |
Optional initial values for copula parameters. |
Details
must have id
(subject id), visit
(the visit number for the subject),
visit_time
(the time for each visit in years), status
(the disability status at visit_time, 1 for disability, 0 for non-disability),
statusD
(the death status at visit_time, 1 for dead, 0 for alive),
and weight
sampling weight and each subject received the same weight across visits.
Optimization methods can be all methods (except "Brent"
) from optim
, such as
"Nelder-Mead"
, "BFGS"
, "CG"
, "L-BFGS-B"
, "SANN"
.
Users can also use "Newton"
(from nlm
).
Value
a LongDecompHE
object summarizing the model.
Can be used as an input to general S3
methods including
summary
, print
, plot
,
coef
, logLik
, AIC
.
Source
Tao Sun, Huiping Zheng, and Xiaojun Wang (2025+).
Decomposing Differences in Cohort Health Expectancy by Cause and Age with Longitudinal Data.
Under review.
Examples
# Fit a Copula2-Semiparametric additive hazards model
data(simulated_dataA)
u1 = u2 = max(simulated_dataA$visit_time)
var_list = c("Z1", "Z2", "Z3")
copula_additive_model <- copula_additive(data = simulated_dataA,
var_list = var_list,
l1=0, u1 = u1, m1 = 3,
l2=0, u2 = u2, m2 = 3,
method = "combined", iter=1000,
stepsize=1e-6,
hes = TRUE,
control = list(maxit = 10000))
summary(copula_additive_model)