Hello,

I am running a model in SEM to look at predictors of weight velocity (using SEM for the mlmv feature) and I am trying to understand a statistically significant interaction between two continuous variables (care=main effect 1; morbidity=main effect 2, and morbcar=the interaction between these two main effects). I am trying to better understand what this interaction means (and I think I can do so in lincom), but I am having trouble setting the command up.

sem (wslopemean14 <- bsex birthweight morbidity care morbcare) if wslopemean14~=., nocapslatent method (mlmv)

Could anyone help me with how I might set up a post-estimation in lincom to compare different values of morbidity and care while holding other co-variates (birthweight and bsex) at their mean values?

Thank you!

Emily

I am running a model in SEM to look at predictors of weight velocity (using SEM for the mlmv feature) and I am trying to understand a statistically significant interaction between two continuous variables (care=main effect 1; morbidity=main effect 2, and morbcar=the interaction between these two main effects). I am trying to better understand what this interaction means (and I think I can do so in lincom), but I am having trouble setting the command up.

sem (wslopemean14 <- bsex birthweight morbidity care morbcare) if wslopemean14~=., nocapslatent method (mlmv)

Could anyone help me with how I might set up a post-estimation in lincom to compare different values of morbidity and care while holding other co-variates (birthweight and bsex) at their mean values?

Thank you!

Emily

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