control function

Auxiliary function for bhm fitting

Auxiliary function for bhm fitting. Typically only used internally by 'bhmFit', but may be used to construct a control argument to either function.

bhmControl(method = 'Bayes', interaction, biomarker.main, alpha, 
               B, R, thin, epsilon, c.n, beta0, sigma0)

Arguments

• method: choose either Bayes' for Bayes method with MCMC or profile' for profile likelihood method with Bootstrap. The default value is 'Bayes'
• interaction: an option of fitting model with interaction term When interaction = TRUE, a predictive biomarker model will be fitted When interaction = FALSE, a prognostic biomarker model will be fitted The default value is interaction = TRUE.
• biomarker.main: include biomarker main effect, default is TRUE
• B: number of burn in
• R: number of replications for Bayes meothd or number of Bootstrap for profile likelihood method
• thin: thinning parameter for Gibbs samples, default is 2
• epsilon: biomarker (transformed) step length for profile likelihood method, default is 0.01
• alpha: significance level (e.g. alpha=0.05)
• c.n: number of threshold (i.e. the cut point), default is 1
• beta0: initial value for mean of the prior distribution of beta, default is 0
• sigma0: initial value for variance of the prior distribution of beta, default is 10000

Details

Control is used in model fitting of "bhm".

Returns

This function checks the internal consisitency and returns a list of value as inputed to control model fit of bhm.

Author(s)

Bingshu E. Chen

Note

Based on code from Tian Fang.

See Also

bhm

Examples

## To fit a prognostic model for biomarker with two cut-points, 
## 500 burn-in samples and 10000 Gibbs samples,

ctl = bhmControl(interaction = FALSE, B = 500, R = 10000, c.n = 2)

##
## then fit the following model
##
#  fit = bhmFit(x, y, family = 'surv', control = ctl)
##