Cyclops (version 3.5.0)

createPrior: 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
)

Value

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

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

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))