joint_ms_va_par {VAJointSurv} | R Documentation |
Extracts the Variational Parameters
Description
Computes the estimated variational parameters for each individual.
Usage
joint_ms_va_par(object, par = object$start_val)
Arguments
object |
a joint_ms object from |
par |
parameter vector to be formatted. |
Value
A list with one list for each individual with the estimated mean and covariance matrix.
Examples
# load in the data
library(survival)
data(pbc, package = "survival")
# re-scale by year
pbcseq <- transform(pbcseq, day_use = day / 365.25)
pbc <- transform(pbc, time_use = time / 365.25)
# create the marker terms
m1 <- marker_term(
log(bili) ~ 1, id = id, data = pbcseq,
time_fixef = bs_term(day_use, df = 5L),
time_rng = poly_term(day_use, degree = 1L, raw = TRUE, intercept = TRUE))
m2 <- marker_term(
albumin ~ 1, id = id, data = pbcseq,
time_fixef = bs_term(day_use, df = 5L),
time_rng = poly_term(day_use, degree = 1L, raw = TRUE, intercept = TRUE))
# base knots on observed event times
bs_term_knots <-
with(pbc, quantile(time_use[status == 2], probs = seq(0, 1, by = .2)))
boundary <- c(bs_term_knots[ c(1, length(bs_term_knots))])
interior <- c(bs_term_knots[-c(1, length(bs_term_knots))])
# create the survival term
s_term <- surv_term(
Surv(time_use, status == 2) ~ 1, id = id, data = pbc,
time_fixef = bs_term(time_use, Boundary.knots = boundary, knots = interior))
# create the C++ object to do the fitting
model_ptr <- joint_ms_ptr(
markers = list(m1, m2), survival_terms = s_term,
max_threads = 2L, ders = list(0L, c(0L, -1L)))
# find the starting values
start_vals <- joint_ms_start_val(model_ptr)
# extract variational parameters for each individual
VA_pars <- joint_ms_va_par(object = model_ptr,par = start_vals)
# number of sets of variational parameters is equal to the number of subjects
length(VA_pars)
length(unique(pbc$id))
# mean and var-covar matrix for 1st individual
VA_pars[[1]]
[Package VAJointSurv version 0.1.0 Index]