fitSccsModel | R Documentation |
Fit the SCCS model
fitSccsModel(
sccsIntervalData,
prior = createPrior("laplace", useCrossValidation = TRUE),
control = createControl(cvType = "auto", selectorType = "byPid", startingVariance =
0.1, seed = 1, resetCoefficients = TRUE, noiseLevel = "quiet"),
profileGrid = NULL,
profileBounds = c(log(0.1), log(10))
)
sccsIntervalData |
An object of type SccsIntervalData as created using the createSccsIntervalData function. |
prior |
The prior used to fit the model. See Cyclops::createPrior for details. |
control |
The control object used to control the cross-validation used to determine the hyperparameters of the prior (if applicable). See Cyclops::createControl for details. |
profileGrid |
A one-dimensional grid of points on the log(relative risk) scale where the likelihood for coefficient of variables is sampled. See details. |
profileBounds |
The bounds (on the log relative risk scale) for the adaptive sampling of the likelihood function. |
Fits the SCCS model as a conditional Poisson regression. When allowed, coefficients for some or all covariates can be regularized.
Likelihood profiling is only done for variables for which profileLikelihood
is set to TRUE
when
calling createEraCovariateSettings()
. Either specify the profileGrid
for a completely user-
defined grid, or profileBounds
for an adaptive grid. Both should be defined on the log IRR scale.
When both profileGrid
and profileGrid
are NULL
likelihood profiling is disabled.
An object of type SccsModel
. Generic functions print
, coef
, and
confint
are available.
Suchard, M.A., Simpson, S.E., Zorych, I., Ryan, P., and Madigan, D. (2013). Massive parallelization of serial inference algorithms for complex generalized linear models. ACM Transactions on Modeling and Computer Simulation 23, 10
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