model1 <- lme(categorical_FOD_FODs~Condition_FODs*Gender_FODs,random=~1|ID/Ch,na.action=na.omit, method = "ML", data=long1.RSPL,control = lmeControl(opt = "optim"))

The model1 variable is the output of applying the linear mixed-effect model analysis to the data in the variable long1.RSPL using the linear mixed-effect model analysis package lme4. The linear mixed-effect model analysis takes as input the formula categorical_FOD_FODs~Condition_FODs*Gender_FODs, (the dependent variable categorical_FOD_FODs is predicted by the predictor variables Condition_FODs and Gender_FODs and their interaction Condition_FODs*Gender_FODs), random=~1|ID/Ch, (the random effects are the intercepts nested in the variables ID and Ch), na.action=na.omit (missing data points are removed), method = "ML" (maximum likelihood estimation is used), data=long1.RSPL (the data is the data in the variable long1.RSPL), control = lmeControl(opt = "optim") (the control is the optimization process).