createControl {Cyclops} | R Documentation |

## 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 |

`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 ( |

`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 |

`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 |

`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 |

`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*version 3.4.1 Index]