[R-meta] Fixed Intercept in mixed effect models Interpretation
Graf, Benedikt
@1begr@| @end|ng |rom un|-tr|er@de
Sat Apr 13 12:42:18 CEST 2019
Hello,
In a seminar a few months ago my interest in meta-analysis was raised. Since then I have been working on a meta-analysis, which I calculate with metafor.
I have a question for you regarding categorical moderator analysis with metafor. For my core analysis I calculated random effect models. These show a high heritability, which is why I conclude on moderators (which I also coded). Examples would be the type of survey or methodological aspects. I use the following syntax for moderator analyses (example "context" as categorial variable and "gender" as interval/ratio variable):
mod.context <- rma(yi, vi, weighted=TRUE, method="HE", mods = ~ factor(Context)-1, data=dataset) mod.context
confint(mod.context)
mod.sex <- rma(yi, vi, weighted=TRUE, method="HE", mods = ~ Sex_female-1, data=dataset) mod.sex
confint(mod.sex)
I partially adopted this syntax from the seminar. I work with Fishers z correlations and a small number of studies. My question refers to the fixation of the intercept and what it means. I want to determine the influence of the categorical factor "context" on my overall effect - do I have to fix the intercept or not? The same applies to gender - assuming I have an effect of x on y, how do I interpret the influence of a fixed or non-fixed intercept on this relationship? (I don't get R� displayed with fixed intercept - is it because the effect x to y is fixed?). I'm new on this field and want to do things right - therefore I would be very grateful for your help.
Best regards,
Bene
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