gmvjoint (version 0.4.0)

cond.ranefs: Obtain conditional distribution of the random effects

Description

Obtain the conditional distribution of the random effects of a joint model fit. This is achieved by a Metropolis scheme. Approximate normality across random effects is expected, and could be useful in diagnosing potential issues surrounding model fits.

Usage

cond.ranefs(fit, burnin = 500L, N = 3500L, tune = 2)

Value

A list of class cond.b.joint containing:

walks

A list of length n containing the history of \(b_i\) post burn-in.

acceptance

A numeric vector containing the acceptance rate for each sampled subject.

M

The ModelInfo list from joint. Used by S3 methods for class cond.b.joint.

bhats

Posterior estimates at MLEs for the random effects. Same as ranef(joint).

Sigmahats

The covariances of bhats.

D

The MLE estimate for the variance-covariance matrix of random effects from fit.

q

Dimension of random effects.

K

Number of responses.

qnames

The names of the random effects as determined by call to joint.

burnin

The amount of burn-in used.

N

Number of MC iterations.

tune

tuning parameter used

nobs

The number of observations for each subject for each response.

elapsed.time

Time taken for cond.ranefs to complete.

Arguments

fit

a joint model fit by the joint function.

burnin

Number of burn-in iterations to discard, defaults to 500.

N

Number of MC iterations to carry out post burn-in, defaults to 3500.

tune

Tuning parameter, problem-specific, defaults to 2.

See Also

ranef.joint plot.cond.b.joint

Examples

# \donttest{
dat <- simData()$data
long.formulas <- list(Y.1 ~ time + cont + bin + (1 + time|id), 
                      Y.2 ~ time + cont + bin + (1 + time|id))
surv.formula <- Surv(survtime, status) ~ bin
fit <- joint(long.formulas, surv.formula, dat, list("gaussian","gaussian"))
cond.b <- cond.ranefs(fit, burnin = 50L, N = 1000, tune = 2)
cond.b
plot(cond.b) # Overall 
plot(cond.b, id = 1) # Plot the first subject (see plot.cond.b.joint).
# }