joint_ms_set_vcov: Sets the Covariance Parameters

In VAJointSurv: Variational Approximation for Joint Survival and Marker Models

Sets the Covariance Parameters

Description

Sets the covariance matrices to the passed values. The function also sets covariance matrices for the variational distributions to the same values.

Usage

joint_ms_set_vcov(
  object,
  vcov_vary,
  vcov_surv,
  par = object$start_val,
  va_mean = NULL
)

Arguments

object	a joint_ms object from joint_ms_ptr.
vcov_vary	the covariance matrix for the time-varying effects.
vcov_surv	the covariance matrix for the frailties.
par	parameter vector to be formatted.
va_mean	a matrix with the number of rows equal to the number of random effects per observation and the number of columns is the number of observations. The order for the observations needs to be the same as the id element of object.

Value

Numeric vector with model parameters.

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)))

# compute var-covar matrices with the first set of starting values
joint_ms_format(object = model_ptr)$vcov
joint_ms_va_par(object = model_ptr)[[1]]

# altering var-covar matrices
alter_pars <- joint_ms_set_vcov(
  object = model_ptr,
  vcov_vary = diag(1:4),
  vcov_surv = matrix(0,0,0))

# altered var-covar matrices
joint_ms_format(object = model_ptr, par = alter_pars)$vcov
joint_ms_va_par(object = model_ptr, par = alter_pars)[[1]]

VAJointSurv documentation built on Aug. 14, 2022, 9:05 a.m.