# NOT RUN {
#Generate some simulated data:
sim <- simulateCyclopsData(nstrata = 1, nrows = 1000, ncovars = 2, eCovarsPerRow = 0.5,
model = "poisson")
cyclopsData <- convertToCyclopsData(sim$outcomes, sim$covariates, modelType = "pr",
addIntercept = TRUE)
#Define the prior and control objects to use cross-validation for finding the
#optimal hyperparameter:
prior <- createPrior("laplace", exclude = 0, useCrossValidation = TRUE)
control <- createControl(cvType = "auto", noiseLevel = "quiet")
#Fit the model
fit <- fitCyclopsModel(cyclopsData,prior = prior, control = control)
#Find out what the optimal hyperparameter was:
getHyperParameter(fit)
#Extract the current log-likelihood, and coefficients
logLik(fit)
coef(fit)
#We can only retrieve the confidence interval for unregularized coefficients:
confint(fit, c(0))
# }
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