mmcif_start_values {mmcif} | R Documentation |
Fast heuristic for finding starting values for the mixed cumulative incidence functions model.
mmcif_start_values(object, n_threads = 1L, vcov_start = NULL)
object |
an object from |
n_threads |
the number of threads to use. |
vcov_start |
starting value for the covariance matrix of the random
effects. |
A list with
an element called "full"
with the starting value where the
last components are the covariance matrix.
an element called "upper"
the staring values where the
covariance matrix is stored as a log Cholesky decomposition. This is used
e.g. for optimization with mmcif_fit
.
if(require(mets)){
# prepare the data
data(prt)
# truncate the time
max_time <- 90
prt <- within(prt, {
status[time >= max_time] <- 0
time <- pmin(time, max_time)
})
# select the DZ twins and re-code the status
prt_use <- subset(prt, zyg == "DZ") |>
transform(status = ifelse(status == 0, 3L, status))
# randomly sub-sample
set.seed(1)
prt_use <- subset(
prt_use, id %in% sample(unique(id), length(unique(id)) %/% 10L))
n_threads <- 2L
mmcif_obj <- mmcif_data(
~ country - 1, prt_use, status, time, id, max_time,
2L, strata = country)
# get the staring values
start_vals <- mmcif_start_values(mmcif_obj, n_threads = n_threads)
# the starting values
print(start_vals)
}