Description
Implementation of binormal model. The binormal model estimates a single unimodal component for the cases and a single unimodal component for the controls.
Usage
bc.binorm(case, control, lambda.bounds = c(-5, 5))
Arguments
case
a numeric vector of case values
control
a numeric vector of control values
lambda.bounds
numeric vector of bounds: c(upper bound, lower bound). Specifies the range for optim to search for the optimization of lambda. Default: c(-5,5).
Value
- lambda
- Box-Cox transformation parameter
- type
- model type ("binorm")
- mu.cases
- mean of the Box-Cox transformed case component
- sig.cases
- standard deviation of the Box-Cox transformed case component
- pi.cases
- proportion of cases in each case component (always equal to 1 for binorm since all cases are forced into one component)
- mu.controls
- mean value of the Box-Cox transformed control component
- sig.controls
- standard deviation of the Box-Cox transformed control component
- pi.controls
- proportion of controls in each control component (always equal to 1 for binorm since all controls are forced into one component)
- max.loglike
- the maximum log likelihood value for the model
- case
- original case values
- control
- original control values
- mu.cases.unt
- an estimate of the untransformed mean of the case component. Based on Monte Carlo simulations. Values will differ by computer seed.
- sig.cases.unt
- an estimate of the untransformed standard deviation of the case component. Based on Monte Carlo simulations. Values will differ by computer seed.
- mu.controls.unt
- an estimate of the untransformed mean of the control component. Based on Monte Carlo simulations. Values will differ by computer seed.
- sig.controls.unt
- an estimate of the untransformed standard deviation of the control component. Based on Monte Carlo simulations. Values will differ by computer seed.