Category mean-square fit statistics |
Computation of Category Mean-Square fit statistics:
For all observations in the data:
Xni is the observed value
Eni is the expected value of Xni
Wni is the model variance of the observation around its expectation
Pnik is the probability of observing Xni=k
Category Outfit Mean-Square statistic for category k =
[sum ((k-Eni)²/Wni) for all Xni=k] / [sum (Pnik * (k-Eni)²/Wni) for all Xni]
Category Infit statistic for category k =
[sum ((k-Eni)²) for all Xni=k] / [sum (Pnik * (k-Eni)²) for all Xni]
The expected values of the category mean-squares are 1.0.
For dichotomies, with categories 0 and 1:
For category 0: k = 0, Eni = Pni = 1 - Pni
Category Outfit Mean-Square statistic for category 0
= [sum (Pni/(1-Pni)) for all Xni=0] / [sum (Pni) for all Xni]
Category Infit statistic for category 0
= [sum (Pni²) for all Xni=0] / [sum ((1-Pni)*Pni²) for all Xni]
For category 1: k = 1
Category Outfit Mean-Square statistic for category 0
= [sum ((1-Pni)/Pni) for all Xni=0] / [sum (1-Pni) for all Xni]
Category Infit statistic for category 0
= [sum ((1-Pni)²) for all Xni=1] / [sum (Pni*(1-Pni)²) for all Xni]
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