createControl: Create a Cyclops control object

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

Create a Cyclops control object

Description

createControl creates a Cyclops control object for use with fitCyclopsModel.

Usage

createControl(
  maxIterations = 1000,
  tolerance = 1e-06,
  convergenceType = "gradient",
  cvType = "auto",
  fold = 10,
  lowerLimit = 0.01,
  upperLimit = 20,
  gridSteps = 10,
  cvRepetitions = 1,
  minCVData = 100,
  noiseLevel = "silent",
  threads = 1,
  seed = NULL,
  resetCoefficients = FALSE,
  startingVariance = -1,
  useKKTSwindle = FALSE,
  tuneSwindle = 10,
  selectorType = "auto",
  initialBound = 2,
  maxBoundCount = 5,
  algorithm = "ccd",
  doItAll = TRUE,
  syncCV = FALSE
)

Arguments

maxIterations	Integer: maximum iterations of Cyclops to attempt before returning a failed-to-converge error
tolerance	Numeric: maximum relative change in convergence criterion from successive iterations to achieve convergence
convergenceType	String: name of convergence criterion to employ (described in more detail below)
cvType	String: name of cross validation search. Option "auto" selects an auto-search following BBR. Option "grid" selects a grid-search cross validation
fold	Numeric: Number of random folds to employ in cross validation
lowerLimit	Numeric: Lower prior variance limit for grid-search
upperLimit	Numeric: Upper prior variance limit for grid-search
gridSteps	Numeric: Number of steps in grid-search
cvRepetitions	Numeric: Number of repetitions of X-fold cross validation
minCVData	Numeric: Minimum number of data for cross validation
noiseLevel	String: level of Cyclops screen output ("silent", "quiet", "noisy")
threads	Numeric: Specify number of CPU threads to employ in cross-validation; default = 1 (auto = -1)
seed	Numeric: Specify random number generator seed. A null value sets seed via Sys.time.
resetCoefficients	Logical: Reset all coefficients to 0 between model fits under cross-validation
startingVariance	Numeric: Starting variance for auto-search cross-validation; default = -1 (use estimate based on data)
useKKTSwindle	Logical: Use the Karush-Kuhn-Tucker conditions to limit search
tuneSwindle	Numeric: Size multiplier for active set
selectorType	String: name of exchangeable sampling unit. Option "byPid" selects entire strata. Option "byRow" selects single rows. If set to "auto", "byRow" will be used for all models except conditional models where the average number of rows per stratum is smaller than the number of strata.
initialBound	Numeric: Starting trust-region size
maxBoundCount	Numeric: Maximum number of tries to decrease initial trust-region size
algorithm	String: name of fitting algorithm to employ; default is ccd
doItAll	Currently unused
syncCV	Currently unused Todo: Describe convegence types

Value

A Cyclops control object of class inheriting from "cyclopsControl" for use with fitCyclopsModel.

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.