confint.cyclopsFit: Confidence intervals for Cyclops model parameters

In Cyclops: Cyclic Coordinate Descent for Logistic, Poisson and Survival Analysis

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))

Cyclops documentation built on June 22, 2024, 10:24 a.m.