Cyclops (version 3.5.0)

simulateCyclopsData: Simulation Cyclops dataset

Description

simulateCyclopsData generates a simulated large, sparse data set for use by fitCyclopsSimulation.

Usage

simulateCyclopsData(
  nstrata = 200,
  nrows = 10000,
  ncovars = 20,
  effectSizeSd = 1,
  zeroEffectSizeProp = 0.9,
  eCovarsPerRow = ncovars/100,
  model = "survival"
)

Value

A simulated data set

Arguments

nstrata

Numeric: Number of strata

nrows

Numeric: Number of observation rows

ncovars

Numeric: Number of covariates

effectSizeSd

Numeric: Standard derivation of the non-zero simulated regression coefficients

zeroEffectSizeProp

Numeric: Expected proportion of zero effect size

eCovarsPerRow

Number: Effective number of non-zero covariates per data row

model

String: Simulation model. Choices are: logistic, poisson or survival

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))