Add error to the calibration coefficients

Description

Bromaghin et al (2016) studied the performance of QFASA estimators when predator diets were estimated using calibration coefficients that incorporated a degree of error. add_cc_err implements their method of adding error to a set of calibration coefficients.

Usage

1
add_cc_err(cc_true, err_bound)

Arguments

cc_true

A vector of calibration coefficients, intended to be the object cc returned by the function prep_fa.

err_bound

A proportion strictly greater than 0 and less than 1 used to control the lower and upper bounds of calibration coefficient error.

Value

A list containing the following elements:

cc

A numeric vector of calibration coefficients with error incorporated.

err

The mean relative absolute error in the calibration coefficients.

err_code

An integer error code (0 if no error is detected).

err_message

A string contains a brief summary of the execution.

Details

One of the major assumptions of QFASA is that the calibration coefficients are known perfectly. Bromaghin et al. (2016) investigated the robustness of diet estimators to violations of this assumption. The function add_cc_err uses the methods of Bromaghin et al. (2016) to add error to a set of calibration coefficients.

The argument err_bound is used to compute box constraints for the calibration coefficients: lower bound equals (1 - err_bound)*cc_true and upper bound equals (1 + err_bound)*cc_true. A uniformly distributed random number is generated between the bounds for each calibration coefficient and the vector of coefficients is scaled so that their sum equals the sum of the true calibration coefficients. Because only the relative magnitudes of the calibration coefficients are important in diet estimation, scaling the coefficients to have a common sum ensures comparability between multiple sets of coefficients.

The mean relative absolute difference between the true and error-added calibration coefficients is computed as a measure of error for the entire vector.

References

Bromaghin, J.F., S.M. Budge, G.W. Thiemann, and K.D. Rode. 2016. Assessing the robustness of quantitative fatty acid signature analysis to assumption violations. Methods in Ecology and Evolution 7:51-59.

Examples

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add_cc_err(cc_true = c(0.75, 1.00, 1.50, 1.15),
           err_bound = 0.25)

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