fitCyclopsModel {Cyclops} | R Documentation |

## Fit a Cyclops model

### Description

`fitCyclopsModel`

fits a Cyclops model data object

### Usage

```
fitCyclopsModel(
cyclopsData,
prior = createPrior("none"),
control = createControl(),
weights = NULL,
forceNewObject = FALSE,
returnEstimates = TRUE,
startingCoefficients = NULL,
fixedCoefficients = NULL,
warnings = TRUE,
computeDevice = "native"
)
```

### Arguments

`cyclopsData` |
A Cyclops data object |

`prior` |
A prior object. More details are given below. |

`control` |
A |

`weights` |
Vector of 0/1 weights for each data row |

`forceNewObject` |
Logical, forces the construction of a new Cyclops model fit object |

`returnEstimates` |
Logical, return regression coefficient estimates in Cyclops model fit object |

`startingCoefficients` |
Vector of starting values for optimization |

`fixedCoefficients` |
Vector of booleans indicating if coefficient should be fix |

`warnings` |
Logical, report regularization warnings |

`computeDevice` |
String: Name of compute device to employ; defaults to |

### Details

This function performs numerical optimization to fit a Cyclops model data object.

### Value

A list that contains a Cyclops model fit object pointer and an operation duration

### Prior

Currently supported prior types are:

` "none"` | Useful for finding MLE |

` "laplace"` | L_1 regularization |

` "normal"` | L_2 regularization |

### References

Suchard MA, Simpson SE, Zorych I, Ryan P, Madigan D. Massive parallelization of serial inference algorithms for complex generalized linear models. ACM Transactions on Modeling and Computer Simulation, 23, 10, 2013.

Simpson SE, Madigan D, Zorych I, Schuemie M, Ryan PB, Suchard MA. Multiple self-controlled case series for large-scale longitudinal observational databases. Biometrics, 69, 893-902, 2013.

Mittal S, Madigan D, Burd RS, Suchard MA. High-dimensional, massive sample-size Cox proportional hazards regression for survival analysis. Biostatistics, 15, 207-221, 2014.

### Examples

```
## Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
cyclopsData <- createCyclopsData(counts ~ outcome + treatment, modelType = "pr")
cyclopsFit <- fitCyclopsModel(cyclopsData, prior = createPrior("none"))
coef(cyclopsFit)
confint(cyclopsFit, c("outcome2","treatment3"))
predict(cyclopsFit)
```

*Cyclops*version 3.4.1 Index]