phmm | R Documentation |
phmm
is used to approximate a single proportional hazards mixed model
using a piecewise constant hazard mixed model approximation
via Monte Carlo Expectation Conditional Minimization (MCECM). No model selection is performed.
phmm(
formula,
data = NULL,
covar = NULL,
offset = NULL,
optim_options = optimControl(),
adapt_RW_options = adaptControl(),
trace = 0,
tuning_options = lambdaControl(),
survival_options = survivalControl(),
progress = TRUE,
...
)
formula |
a two-sided linear formula object describing both the fixed effects and
random effects part of the model, with the response on the left of a ~ operator and the terms,
separated by + operators, on the right.
The response must be a |
data |
an optional data frame containing the variables named in |
covar |
character string specifying whether the covariance matrix should be unstructured
("unstructured") or diagonal with no covariances between variables ("independent").
Default is set to |
offset |
This can be used to specify an a priori known component to be included in the
linear predictor during fitting. Default set to |
optim_options |
a structure of class "optimControl" created
from function |
adapt_RW_options |
a list of class "adaptControl" from function |
trace |
an integer specifying print output to include as function runs. Default value is 0. See Details for more information about output provided when trace = 0, 1, or 2. |
tuning_options |
a list of class "selectControl" or "lambdaControl" resulting from
|
survival_options |
a structure of class "survivalControl" created
from function |
progress |
a logical value indicating if additional output should be given showing the
progress of the fit procedure. If |
... |
additional arguments that could be passed into |
The phmm
function can be used to approximate a single proportional hazards
mixed model using a piecewise constant hazard mixed model.
While this approach is meant to be used in the case where the user knows which
covariates belong in the fixed and random effects and no penalization is required, one is
allowed to specify non-zero fixed and random effects penalties using lambdaControl
and the (...) arguments. The (...) allow for specification of penalty-related arguments; see
glmmPen
and glmmPen_FA
for details.
For a high dimensional situation, the user may want to fit a
full model using a small penalty for the fixed and random effects and save the posterior
draws from this full model for use in any BIC-ICQ calculations during selection within phmmPen
.
Specifying a file name in the 'BICq_posterior' argument will save the posterior draws from the
phmm
model into a big.matrix with this file name, see the Details section of
phmmPen
for additional details.
A reference class object of class pglmmObj
for which many methods are
available (e.g. methods(class = "pglmmObj")
)
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