> # Print results > print(lrtest) Likelihood ratio tests of cumulative link models: formula: link: threshold: m2 categorical_FOD_FODs ~ 0 + Condition_FODs:Gender_quantised_FODs - 1 logit flexible m1 categorical_FOD_FODs ~ 0 + Condition_FODs:Gender_FODs - 1 logit flexible no.par AIC logLik LR.stat df Pr(>Chisq) m2 6 5839.2 -2913.6 m1 13 5847.8 -2910.9 5.4581 7 0.6042 > print(AICs) df AIC m1 13 5847.790 m2 6 5839.248 > print(BICs) df BIC m1 13 5927.194 m2 6 5875.896 which test is ideal to conduct?

The test is used to compare two models: the null model, and the model with changes. The p-value is used to determine whether to keep the changes in the model.
A p-value of less than 0.05 indicates that the changes improve the model fit.
A p-value of more than 0.05 indicates that the changes do not improve the model fit.