Tests whether the estimated intercept and slope jointly fall within a confidence region around specified ideal values (typically intercept=0 and slope=1 for method comparison studies).
Usage
joint_test(object, ...)
# S3 method for class 'simple_eiv'
joint_test(
object,
ideal_intercept = 0,
ideal_slope = 1,
conf.level = 0.95,
...
)Value
An object of class htest containing:
- statistic
The Mahalanobis distance (chi-squared distributed with df=2).
- parameter
Degrees of freedom (always 2).
- p.value
The p-value for the test.
- conf.int
The confidence level used.
- estimate
Named vector of estimated intercept and slope.
- null.value
Named vector of hypothesized intercept and slope.
- alternative
Description of the alternative hypothesis.
- method
Description of the test.
- data.name
Name of the input object.
Details
The test computes the Mahalanobis distance between the estimated coefficients and the hypothesized values using the variance-covariance matrix of the estimates. Under the null hypothesis, this distance follows a chi-squared distribution with 2 degrees of freedom.
For Deming regression, the variance-covariance matrix is computed via
jackknife. For Passing-Bablok regression, bootstrap resampling must have
been performed (i.e., boot_ci = TRUE in the original call).