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Model simplification proceeded by stepwise deletion of non-significant terms (P > 0.05).
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Model evaluation and simplification proceeded using Z-test and likelihood ratio tests (LRTs) [Bates et al., 2012].
Once the significance of the full model has been established, one may want to proceed with model simplification to narrow down the significant predictors.
We did not perform any model simplification and assessed model fit based on χ2-statistics.
Model simplification and LDA parameters estimation.
Model simplification was performed using a backward stepwise algorithm.
Model simplification produced lower AIC scores in all cases.
We used a backward model simplification procedure by first fitting saturated models.
Furthermore, ΔTC was the only term that was retained after model simplification (Table 1).
Model simplification was done in a stepwise procedure and non-significant terms were excluded [33].
These were used to guide model simplification.
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