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,
test_method = c("F", "asymptotic"),
...
)Arguments
- object
- ...
Additional arguments (currently unused).
- ideal_intercept
The hypothesized intercept value (default: 0).
- ideal_slope
The hypothesized slope value (default: 1).
- conf.level
Confidence level for the test (default: 0.95).
- test_method
Reference distribution for the test.
"F"(default) uses the finite-sample F(2, n-2) distribution per Sadler (2010)."asymptotic"uses the chi-squared(2) distribution, which is the large-sample approximation. The two converge for large n.
Value
An object of class htest containing:
- statistic
The Mahalanobis distance.
- parameter
Degrees of freedom. For the F-based test,
df1anddf2; for the asymptotic test,df(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. By default, the finite-sample F(2, n-2) reference distribution is used (Sadler, 2010), which is more conservative than the asymptotic chi-squared(2) approximation for small samples.
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).