bc.twocomp

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Description

Implementation of two component models. In the two component unconstrained model, the components of the control and case mixtures are the same; however the mixture probabilities may differ for cases and controls. In the two component constrained model, all controls are distributed according to one of the two components while cases follow a mixture distribution of the two components.

Usage

1
2
3
bc.twocomp(x.cases, x.controls, constrained = T, lambda.bounds = c(-5, 5),

control.comp = 1, start.vals=NULL)

Arguments

x.cases

a numeric vector of case values

x.controls

a numeric vector of control values

constrained

Boolean indicating whether the two component constrained model should be used (default T) or the two component unconstrained model should be used (F)

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).

control.comp

indicator of which component contains the controls (1 or 2)

start.vals

starting values for the EM algorithm. If NA, the starting values are estimated from the data.

Value

lambda

Box-Cox transformation parameter

type

model type ( "2cc" or "2cu")

mu.cases

means of the Box-Cox transformed case components

sig.cases

standard deviations of the Box-Cox transformed case components

pi.cases

proportion of cases in each case component

mu.controls

means of the Box-Cox transformed control components

sig.controls

standard deviations of the Box-Cox transformed control components

pi.controls

proportion of controls in each control component (always equal to 1 for 2cc since all controls are forced into one component)

max.loglike

the maximum log likelihood value for the model

mu.cases.unt

an estimate of the untransformed means of the case components. Based on Monte Carlo simulations. Values will differ by computer seed.

sig.cases.unt

an estimate of the untransformed standard deviations of the case components. Based on Monte Carlo simulations. Values will differ by computer seed.

mu.controls.unt

an estimate of the untransformed means of the control components. Based on Monte Carlo simulations. Values will differ by computer seed.

sig.controls.unt

an estimate of the untransformed standard deviations of the control components. Based on Monte Carlo simulations. Values will differ by computer seed.

case

original case values

control

original control values

time

running time for the model fit

Author(s)

Michelle Winerip, Garrick Wallstrom, Joshua LaBaer

See Also

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