Quantal Response Analysis in the Absence of a Zone of Mixed Results Using Data Augmentation

摘要

Quantal response analysis is used to estimate the probability of a dichotomous response, e.g., complete penetration, as a function of a stimulus variable. Using maximum likelihood and the DiDonato-Janagin algorithm, estimates of the normal distribution parameters that underlie threshold stimulus levels are obtainable if a zone of mixed results is observed in the test data and if the average success-producing stimulus exceeds the average failure-producing stimulus. In the absence of a zone of mixed results, a method is proposed that utilizes data augmentation to estimate some parameter of interest, e.g., the probability of success at a specific stimulus level. This method generates artificial copies of the original data set of stimuli and responses and then adds a random noise component to each of the stimuli.