calfun {VDSPCalibration}

Estimating the Calibration Equation

Description

Estimates the calibration equation based on CV information

Usage

calfun(x, y, CVx, CVy = CVx, lambda0 = 1)

Arguments

x

old VD measurements

y

reference (new) VD measurements

CVx

CV% of the old VD measurements

CVy

CV% of the new VD measurements

lambda0

the CV ratio of the new vs old measurements

Details

Estimation of the calibration equation. It covers 4 scenarios: Only CVx is known; only CVy is known; both CVx and CVy are known; and Only the ratio of CVy to CVx is known.

Value

coef

estimated coefficients of the linear function

se

standard errors of the estimated coefficients

lower CI

the lower end of the 95% CI of the regression coefficients

upper CI

the upper end of the 95% CI of the regression coefficients

Author(s)

Durazo-Arvizu, Ramon; Sempos, Chris; Tian, Lu

References

Tian L., Durazo-Arvizu R. A., Myers G., Brooks S., Sarafin K., and Sempos C. T. (2014), The estimation of calibration equations for variables with heteroscedastic measurement errors, Statist. Med., 33, pages 4420-4436

Examples

n=100
sigma0=10

beta0=5
beta1=1.2
CVx=0.15
CVy=0.07

lambda0=CVy^2/CVx^2

x0=runif(n, 20, 200)
y0=beta0+beta1*x0+rnorm(n)*sigma0
x=x0+x0*CVx*rnorm(n)
y=y0+y0*CVy*rnorm(n)

fit=calfun(x, y, CVx, CVy, lambda0)
fit