# calfun: Estimating the Calibration Equation In VDSPCalibration: Statistical Methods for Designing and Analyzing a Calibration Study

## Description

Estimates the calibration equation based on CV information

## Usage

 `1` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```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 ```

VDSPCalibration documentation built on May 2, 2019, 3:32 p.m.