This class of objects is returned by the survregVB function to represent
a fitted parametric log-logistic accelerated failure time (AFT) survival
model. Objects of this class have methods for the functions print
and summary.
For approximate posterior distributions:
\(q^*(\beta)\), a \(N_p(\mu^*,\Sigma^*)\) density function, and
\(q^*(b)\), an \(\text{Inverse-Gamma}(\alpha^*,\omega^*)\) density function,
the components of this class are:
ELBO: The final value of the Evidence Lower Bound (ELBO)
at the last iteration.
alpha: The shape parameter \(\alpha^*\) of \(q^*(b)\).
omega: The scale parameter \(\omega^*\) of \(q^*(b)\).
mu: Parameter \(\mu^*\) of \(q^*(\beta)\), a vector
of means.
Sigma: Parameter \(\Sigma^*\) of \(q^*(\beta)\), a
covariance matrix.
na.action: A missing-data filter function, applied to the
model.frame, after any subset argument has been used.
iterations: The number of iterations performed by the VB
algorithm: before converging or reaching max_iteration.
n: The number of observations.
call: The function call used to invoke the survregVB
method.
not_converged: A boolean indicating if the algorithm
converged.
TRUE: If the algorithm did not converge prior to
achieving max_iteration.
NULL: If the algorithm converged successfully.
If survregVB was called with shared frailty (with the cluster
argument), for approximate posterior distributions:
\(q^*(\sigma^2_\gamma)\), an \(\text{Inverse-Gamma}(\lambda^*,\eta^*)\) density function,
\(q^*(\gamma_i)\), a \(N(\tau^*_i,\sigma^{2*}_i)\) density function, for \(i=1,...,K\) clusters, and
the additional components are present:
lambda: The shape parameter \(\lambda^*\) of
\(q^*(\sigma^2_\gamma)\).
eta: The scale parameter \(\eta^*\) of
\(q^*(\sigma^2_\gamma)\).
tau: Parameter \(\tau^*_i\) of \(q^*(\gamma_i)\), a
vector of means.
sigma: Parameter \(\sigma^{2*}_i\) of \(q^*(\gamma_i)\),
a vector of variance.