createPrior: Create a Cyclops prior object

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

Create a Cyclops prior object

Description

createPrior creates a Cyclops prior object for use with fitCyclopsModel.

Usage

createPrior(
  priorType,
  variance = 1,
  exclude = c(),
  graph = NULL,
  neighborhood = NULL,
  useCrossValidation = FALSE,
  forceIntercept = FALSE
)

Arguments

priorType	Character: specifies prior distribution. See below for options
variance	Numeric: prior distribution variance
exclude	A vector of numbers or covariateId names to exclude from prior
graph	Child-to-parent mapping for a hierarchical prior
neighborhood	A list of first-order neighborhoods for a partially fused prior
useCrossValidation	Logical: Perform cross-validation to determine prior variance.
forceIntercept	Logical: Force intercept coefficient into prior

Value

A Cyclops prior object of class inheriting from "cyclopsPrior" for use with fitCyclopsModel.

Prior types

We specify all priors in terms of their variance parameters. Similar fitting tools for regularized regression often parameterize the Laplace distribution in terms of a rate "lambda" per observation. See "glmnet", for example.

variance = 2 * / (nobs * lambda)^2 or lambda = sqrt(2 / variance) / nobs

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 25, 2025, 1:07 a.m.