createControl creates a Cyclops control object for use with fitCyclopsModel.
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
)A Cyclops control object of class inheriting from "cyclopsControl" for use with fitCyclopsModel.
Integer: maximum iterations of Cyclops to attempt before returning a failed-to-converge error
Numeric: maximum relative change in convergence criterion from successive iterations to achieve convergence
String: name of convergence criterion to employ (described in more detail below)
String: name of cross validation search.
Option "auto" selects an auto-search following BBR.
Option "grid" selects a grid-search cross validation
Numeric: Number of random folds to employ in cross validation
Numeric: Lower prior variance limit for grid-search
Numeric: Upper prior variance limit for grid-search
Numeric: Number of steps in grid-search
Numeric: Number of repetitions of X-fold cross validation
Numeric: Minimum number of data for cross validation
String: level of Cyclops screen output ("silent", "quiet", "noisy")
Numeric: Specify number of CPU threads to employ in cross-validation; default = 1 (auto = -1)
Numeric: Specify random number generator seed. A null value sets seed via Sys.time.
Logical: Reset all coefficients to 0 between model fits under cross-validation
Numeric: Starting variance for auto-search cross-validation; default = -1 (use estimate based on data)
Logical: Use the Karush-Kuhn-Tucker conditions to limit search
Numeric: Size multiplier for active set
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.
Numeric: Starting trust-region size
Numeric: Maximum number of tries to decrease initial trust-region size
String: name of fitting algorithm to employ; default is ccd
Currently unused
Currently unused
Todo: Describe convegence types
#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))
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