For our model, the AIC was calculated as AIC = N × ln (RSS/N) + 2k, where N = number of fitting points, k = number of parameters in the model, and RSS is the residual sum of squares, which we equated to the best fitting score.
Using an ad hoc approach, we compared fitting scores with repeated automations of the same model but different parameter sets, to arrive at the optimal set of values for each model; once a model's scores no longer improved, we assigned the model a best fitting score.
Kshirsagar et al[ 5] conducted a study on a community-based cohorts with ages of 45 years or older in order to create the best fitting and simplified scores for predicting the incidence of CKD (GFR < 60 ml/min/1.73 m).
The best fitting model for BMI z-score was BMIZ = ß0 + ß1* ln)age + ß2*√age.
The best fitting model for each peak is then determined based on fitting scores, that is, the maximum R value.
This study set out to establish the best fitting model for the GHQ-12 for three scoring methods (Likert, GHQ and C-GHQ) and to calculate the degree of measurement error under these more realistic assumptions.
The best fitting lag structures for PM10, NO2, and O3 for the overall depression score were lag 0 2 days, lag 0 7 days, and lag 0 2 days, respectively.
Then, a set of confirmatory factor analyses (CFAs) based on the exploratory result and on the scientific literature were performed on a new data set (second assessment scores) to find the best fitting factor structure for the instrument.
