bhm (version 1.18)

pIndexControl: Auxiliary function for pIndex fitting

Description

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

Usage

pIndexControl(method = c("Efron", "Elc", "Elw", "Pic"), 
                model = c("default", "local", "threshold"), 
	        ci = c("Bootstrap", "Jackknife"), weights = NULL, 
		kernel = NULL, h = 0.1, w  = seq(0.05, 0.95, 0.05), 
		alpha = 0.05, B = 0, pct = 0.5, tau=NULL)

Value

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

Arguments

method

choose either `Efron' for Efron method, `Elc' for conditional empirical likelihood, `Elw' for weighted empirical likelihood method, and `Pic' for piecewise exponential distribution. The default value is `Efron'

model

`default' for default pIndex model, `local' for kernel method, `threshold' for threshold method

ci

Method to construct confidence interval, `Bootstrap' for Bootstrap method and `Jackknife' for Jackknife method

weights

case weight

kernel

kernel funtion types, including "gaussian", "epanechnikov", "rectangular", "triangular", "biweiht", "cosine", "optcosine". The default value is `gaussian'

h

bandwidth, defaul is 0.1

w

percentile of biomarker value for local fit

B

number of Bootstrap sample

alpha

significance level (e.g. alpha=0.05)

pct

Percentile of threshold (i.e. the cut point), default is 0.5

tau

maximum time tau to be used for pIndex

Author

Bingshu E. Chen

Details

Control is used in model fitting of `pIndex'.

See Also

bhm, pIndex

Examples

## To calculate the probability index for a biomarker with conditional empirical likelihood method, 
## and the corresponding 90 percent CI using Bootstrap method with 10000 bootstrap sample

ctl = pIndexControl(method = 'Elc', ci = 'Bootstrap', B = 10000, alpha = 0.1)

##
## then fit the following model
##
#  fit = pIndex(y~x1 + x2, family = 'surv', control = ctl)
##