Observation Oriented Modeling
Software Questions

  1. How is it possible to obtain a c-value less than zero? I tested a model and the c-value was reported as :  "Model c-value :<  0.00"

    Version 2 of OOM clarifies this potential source of confusion. Chance values are now printed out in a format in which they are never reported as 0.00 or "< 0.00." If a randomization test fails to meet or exceed the PCC index, then the c-value is reported as a note. For instance, for 1000 trials, the note will appear as: "less than ( 1 / 1000); that is, < 0.001".

    Here's the answer to this question for Version 1:
    The c-value is in fact not less than zero. The c-value can also not, in principle, be exactly equal to zero. With more decimal precision the "< 0.00" text would be replaced with a value other than zero. For instance, consider setting the randomization test to 1000 trials. Further, suppose the results showed that not 1 in 1000 trials was the observed Percent Correct Classification (PCC) index equaled or exceeded by the randomized versions of the observations. In such an example, the c-value would be some value less than 1 in 1000, or less than .001 (1 / 1000). With only two decimals of precision, the result appears as "< 0.00", but with three decimals of precision it appears as "< 0.001."   When OOM encounters at least one PCC value from randomized observations, then the inequality is removed from the output; e.g., "Model c-value :  0.02."  The c-value is never reported as "Model c-value = 0.00" because the observed pattern of results and computed PCC index is a single event or ordered pattern out of many more possible patterns. The observed pattern may be one of  a trillion possible patterns and may thus rarely show up when the observations are randomized, but the c-value is still not exactly equal to zero.