Create setting for lasso logistic regression

```
setLassoLogisticRegression(
variance = 0.01,
seed = NULL,
includeCovariateIds = c(),
noShrinkage = c(0),
threads = -1,
forceIntercept = F,
upperLimit = 20,
lowerLimit = 0.01,
tolerance = 2e-06,
maxIterations = 3000,
priorCoefs = NULL
)
```

## Arguments

- variance
Numeric: prior distribution starting variance

- seed
An option to add a seed when training the model

- includeCovariateIds
a set of covariate IDS to limit the analysis to

- noShrinkage
a set of covariates whcih are to be forced to be included in the final model. default is the intercept

- threads
An option to set number of threads when training model

- forceIntercept
Logical: Force intercept coefficient into prior

- upperLimit
Numeric: Upper prior variance limit for grid-search

- lowerLimit
Numeric: Lower prior variance limit for grid-search

- tolerance
Numeric: maximum relative change in convergence criterion from successive iterations to achieve convergence

- maxIterations
Integer: maximum iterations of Cyclops to attempt before returning a failed-to-converge error

- priorCoefs
Use coefficients from a previous model as starting points for model fit (transfer learning)

## Examples

```
model.lr <- setLassoLogisticRegression()
```