svycoxme {svycoxme} | R Documentation |
Survey-weighted mixed-effects Cox models
Description
Fit a mixed-effect proportional hazards model to data from a complex design.
Usage
svycoxme(
formula,
design,
subset = NULL,
rescale = TRUE,
control = coxme::coxme.control(),
...
)
## S3 method for class 'DBIsvydesign'
svycoxme(
formula,
design,
subset = NULL,
rescale = TRUE,
control = coxme::coxme.control(),
...
)
## S3 method for class 'survey.design'
svycoxme(
formula,
design,
subset = NULL,
rescale = TRUE,
control = coxme::coxme.control(),
...
)
## S3 method for class 'svyrep.design'
svycoxme(
formula,
design,
subset = NULL,
rescale = TRUE,
control = coxme::coxme.control(),
multicore = getOption("survey.multicore"),
return.replicates = FALSE,
...
)
Arguments
formula |
Model formula. |
design |
|
subset |
Expression to select a subpopulation. |
rescale |
Rescale weights to improve numerical stability. |
control |
Optional list of |
... |
Other arguments passed to |
multicore |
For replicate weight designs. Should parallel processing be used? |
return.replicates |
For replicate weight designs. Should replicates be returned? |
Details
Parallel processing is done with future_lapply
. Future planning
is left to the user, e.g. using plan
before the call to svycoxme
.
Note that svycoxme.DBIsvydesign
has not been implemented yet.
Value
An object of class svycoxme
.
Examples
des <- survey::svydesign(ids = ~group_id, weights = ~weight, data = samp_srcs)
fit1 <- svycoxme(survival::Surv(stat_time, stat) ~ X1 + X2 + X3 + (1 | group_id),
design = des)
summary(fit1)
# with replicate weights (only 10 replicates are used to reduce CPU time)
repdes <- survey::as.svrepdesign(des, type = "bootstrap", replicates = 10)
fit2 <- svycoxme(survival::Surv(stat_time, stat) ~ X1 + X2 + X3 + (1 | group_id),
design = repdes)
summary(fit2)
# use multicore processing (`n_cores = 2` to comply with CRAN policy). Otherwise,
# something like, `floor(parallelly::availableCores() * 0.8)`, could be used.
n_cores = 2
future::plan("multicore", workers = n_cores)
fit3 <- svycoxme(survival::Surv(stat_time, stat) ~ X1 + X2 + X3 + (1 | group_id),
design = repdes, multicore = TRUE)
all.equal(coef(fit2), coef(fit3))
future::plan("sequential")
[Package svycoxme version 1.0.0 Index]