calfun {CVcalibration} R Documentation

## Estimating the Calibration Equation

### Description

Estimating the calibration equation “y=a+b*x” with error-in observations assuming that the coefficients of the variation of the measurements are constants.

### Usage

```calfun(x, y, CVx, CVy, lambda0)
```

### Arguments

 `x` The observed \$x\$ values `y` The observed \$y\$ values `CVx` The underlying coefficient of variation of measurement \$x\$ `CVy` The underlying coefficient of variation of measurement \$y\$ `lambda0` The ratio, \$CV_y^2/CV_x^2\$

### Value

 `result` The estimated regression coefficients, standard error and confidence intervals based on (1) CVx only; (2) CVy only; (3) both CVx and CVy; and (4) the ratio of CVy^2/CVx^2.

Lu Tian, He Qi

### 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
```

[Package CVcalibration version 1.0-1 Index]