[R-meta] Fixed Intercept in mixed effect models Interpretation
Michael Dewey
||@t@ @end|ng |rom dewey@myzen@co@uk
Sat Apr 13 15:25:06 CEST 2019
Dear Benedikt
See comments in-line below
On 13/04/2019 11:42, Graf, Benedikt wrote:
> 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
You have told R to fit a model without intercept so you will get one
coefficient for each level of Context. Your Alternative would be to
remove the -1 and get the intercept and one fewer coefficient for
Context. This is not explicitly something to do with metafor, it is how
linear model functions like lm() and glm() work. Try it and see, you
will not break anything.
One thing to note is that if you test the moderator variable with the
two models you are testing two different hypotheses. The way you are
doing it tests whether the estimated effect is equal to zero, the other
way tests whether the estimated effect is the same for each level of
Context.
>
> confint(mod.context)
>
>
>
> mod.sex <- rma(yi, vi, weighted=TRUE, method="HE", mods = ~ Sex_female-1, data=dataset) mod.sex
>
> confint(mod.sex)
>
Have you actually coded Sex as a factor? It would be much better to do
so if not.
>
>
> 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
>
>
> [[alternative HTML version deleted]]
>
>
>
> ---
> This email has been checked for viruses by AVG.
> https://www.avg.com
>
>
> _______________________________________________
> R-sig-meta-analysis mailing list
> R-sig-meta-analysis using r-project.org
> https://stat.ethz.ch/mailman/listinfo/r-sig-meta-analysis
>
--
Michael
http://www.dewey.myzen.co.uk/home.html
More information about the R-sig-meta-analysis
mailing list