bc.binorm

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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

1
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.

Author(s)

Michelle Winerip, Garrick Wallstrom, Joshua LaBaer

See Also

bc.twocomp bc.fourcomp em.twocomp.m1 em.twocomp.m2 em.twocomp.m3