bc.binorm {bimixt} | R Documentation |
Implementation of binormal model. The binormal model estimates a single unimodal component for the cases and a single unimodal component for the controls.
bc.binorm(case, control, lambda.bounds = c(-5, 5))
case |
a numeric vector of case values |
control |
a numeric vector of control values |
lambda.bounds |
numeric vector of bounds: |
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. |
Michelle Winerip, Garrick Wallstrom, Joshua LaBaer
bc.twocomp
bc.fourcomp
em.twocomp.m1
em.twocomp.m2
em.twocomp.m3