> m2 <- clm(categorical_FOD_FODs ~ 0 + Condition_FODs:Gender_quantised_FODs - 1, + random = ~1|subject_FODs, data = indvar_FODs) > summary(m2) formula: categorical_FOD_FODs ~ 0 + Condition_FODs:Gender_quantised_FODs - 1 data: indvar_FODs link threshold nobs logLik AIC niter max.grad cond.H logit flexible 3321 -2913.62 5839.25 6(0) 8.61e-13 4.7e+01 Coefficients: Estimate Std. Error z value Pr(>|z|) Condition_FODsA:Gender_quantised_FODs 0.12910 0.06843 1.887 0.0592 . Condition_FODsB:Gender_quantised_FODs -0.06120 0.06805 -0.899 0.3685 Condition_FODsC:Gender_quantised_FODs -0.04323 0.06766 -0.639 0.5229 Condition_FODsD:Gender_quantised_FODs 0.06104 0.06855 0.890 0.3732 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Threshold coefficients: Estimate Std. Error z value ascending|descending 0.05659 0.09969 0.568 descending|identity 2.75330 0.11826 23.282 (199 observations deleted due to missingness) what should be a following-up analysis according to these results? can you please write the code for this analysis?

library(ordinal)
m2 <- clm(categorical_FOD_FODs ~ 0 + Condition_FODs:Gender_quantised_FODs - 1, 
           random = ~1|subject_FODs, data = indvar_FODs)
summary(m2)