confint.cyclopsFit {Cyclops}

Confidence intervals for Cyclops model parameters

Description

confinit.cyclopsFit profiles the data likelihood to construct confidence intervals of arbitrary level. Usually it only makes sense to do this for variables that have not been regularized.

Usage

## S3 method for class 'cyclopsFit'
confint(
  object,
  parm,
  level = 0.95,
  overrideNoRegularization = FALSE,
  includePenalty = TRUE,
  rescale = FALSE,
  ...
)

Arguments

object

A fitted Cyclops model object

parm

A specification of which parameters require confidence intervals, either a vector of numbers of covariateId names

level

Numeric: confidence level required

overrideNoRegularization

Logical: Enable confidence interval estimation for regularized parameters

includePenalty

Logical: Include regularized covariate penalty in profile

rescale

Boolean: rescale coefficients for unnormalized covariate values

...

Additional argument(s) for methods

Value

A matrix with columns reporting lower and upper confidence limits for each parameter. These columns are labelled as (1-level) / 2 and 1 - (1 - level) / 2 in percent (by default 2.5 percent and 97.5 percent)

Examples

#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))