> model1 <- lme(FOD_FODs ~ 0 + Condition_FODs*Age_FODs, random=~1|subject_FODs, + na.action=na.omit, + method = "ML", data=indvar_FODs,control = lmeControl(opt = "optim")) > ano <- anova(model1) > round(ano, 3) numDF denDF F-value p-value Condition_FODs 4 3139 0.347 0.846 Age_FODs 15 3139 0.084 1.000 Condition_FODs:Age_FODs 45 3139 0.046 1.000 > eta_squared(anova(model1), partial=TRUE) # Effect Size for ANOVA Parameter | Eta2 (partial) | 95% CI ------------------------------------------------------- Condition_FODs | 4.42e-04 | [0.00, 1.00] Age_FODs | 4.02e-04 | [0.00, 1.00] Condition_FODs:Age_FODs | 6.52e-04 | [0.00, 1.00] - One-sided CIs: upper bound fixed at [1.00]. > library(MuMIn) > r.squaredGLMM(model1) R2m R2c [1,] 0.001171854 0.001178491 Can you please report these results in APA style?

The results of the analysis are presented in table 1. According to the table, the effect of condition and age on FODs was not significant. In addition, there was also no significant interaction between condition and age. The partial eta-square indicated that condition and age have a trivial effect on FODs (eta-squared < 0.01) and the R2GLMM indicated that condition and age explained less than 1% of the variance of the data.

Table 1. Results of ANOVA