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Ambiguity Resolution (AR) plays a vital role in precise GNSS positioning. Correctly-fixed integer ambiguities can significantly improve the positioning solution, while incorrectly-fixed integer ambiguities can bring large positioning errors and, therefore, should be avoided. The ratio test is an extensively used test to validate the fixed integer ambiguities. To choose proper critical values of the ratio test, the Fixed Failure-rate Ratio Test (FFRT) has been proposed, which generates critical values according to user-defined tolerable failure rates. This contribution provides easy-to-implement fitting functions to calculate the critical values. With a massive Monte Carlo simulation, the functions for many different tolerable failure rates are provided, which enriches the choices of critical values for users. Moreover, the fitting functions for the fix rate are also provided, which for the first time allows users to evaluate the conditional success rate, i.e., the success rate once the integer candidates are accepted by FFRT. The superiority of FFRT over the traditional ratio test regarding controlling the failure rate and preventing unnecessary false alarms is shown by a simulation and a real data experiment. In the real data experiment with a baseline of 182.7 km, FFRT achieved much higher fix rates (up to 30% higher) and the same level of positioning accuracy from fixed solutions as compared to the traditional critical value.

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