summary.survregVB {survregVB} | R Documentation |
Summary for Variational Bayes log-logistic AFT models.
Description
Produces a summary of a fitted Variational Bayes Parametric Survival Regression Model for a Log-Logistic AFT Model
Usage
## S3 method for class 'survregVB'
summary(object, ci = 0.95, ...)
Arguments
object |
The result of a |
ci |
The significance level for the credible intervals. (Default:0.95). |
... |
For future arguments. |
Value
An object of class summary.survregVB
with components:
-
ELBO
: The final value of the Evidence Lower Bound (ELBO) at the last iteration. -
alpha
: The shape parameter\alpha^*
ofq^*(b)
. -
omega
: The scale parameter\omega^*
ofq^*(b)
. -
mu
: Parameter\mu^*
ofq^*(\beta)
, a vector of means. -
Sigma
: Parameter\Sigma^*
ofq^*(\beta)
, a covariance matrix. -
na.action
: A missing-data filter function, applied to themodel.frame
, after any subset argument has been used. -
iterations
: The number of iterations performed by the VB algorithm: before converging or reachingmax_iteration
. -
n
: The number of observations. -
call
: The function call used to invoke thesurvregVB
method. -
not_converged
: A boolean indicating if the algorithm converged.-
TRUE
: If the algorithm did not converge prior to achievingmax_iteration
. -
NULL
: If the algorithm converged successfully.
-
-
estimates
: A matrix with one row for each regression coefficient, and one row for the scale parameter. The columns contain:-
Value
: The estimated value based on the posterior distribution mean. -
Lower CI
: The lower bound of the credible interval. -
Upper CI
: The upper bound of the credible interval.
-
If called with shared frailty, the object also contains components:
-
lambda
: The shape parameter\lambda^*
ofq^*(\sigma^2_\gamma)
. -
eta
: The scale parameter\eta^*
ofq^*(\sigma^2_\gamma)
. -
tau
: Parameter\tau^*_i
ofq^*(\gamma_i)
, a vector of means. -
sigma
: Parameter\sigma^{2*}_i
ofq^*(\gamma_i)
, a vector of variance.
The estimates
component will contain an additional row for the
frailty, the estimated variance based on the posterior mean for the
random intercepts.