Usage
frailty(x, distribution="gamma", ...)
frailty.gamma(x, sparse = (nclass > 5), theta, df, eps = 1e-05,
method = c("em","aic", "df", "fixed"), ...)
frailty.gaussian(x, sparse = (nclass > 5), theta, df,
method =c("reml","aic", "df", "fixed"), ...)
frailty.t(x, sparse = (nclass > 5), theta, df, eps = 1e-05, tdf = 5,
method = c("aic", "df", "fixed"), ...)Arguments
x
the variable to be entered as a random effect.
It is always treated as a factor.
distribution
either the gamma,
gaussian or t
distribution may be specified.
The routines frailty.gamma,
frailty.gaussian and
frailty.t do the actual work.
...
Arguments for specific distribution, including (but not
limited to)
sparse
cutoff for using a sparse coding of the data matrix.
If the total number of levels of x is larger
than this value, then a sparse matrix approximation is used.
The correct cutoff is still a matter of exploration: if the number of
theta
if specified, this fixes the variance of the random effect.
If not, the variance is a parameter, and a best solution is sought.
Specifying this implies method='fixed'.
df
if specified, this fixes the degrees of freedom for the random effect.
Specifying this implies method='df'.
Only one of theta or
df should be specified.
method
the method used to select a solution for theta, the variance of the
random effect.
The fixed corresponds to a user-specified
value, and no iteration is done.
The df selects the variance such that the
degrees
tdf
the degrees of freedom for the t-distribution.
eps
convergence critera for the iteration on theta.