________________________________________
From: Viechtbauer Wolfgang (STAT)
[wolfgang.viechtbauer at maastrichtuniversity.nl]
Sent: 03 September 2014 10:28
To: Shona Smith; r-sig-mixed-models at r-project.org
Subject: RE: Post model fitting checks in Metafor (rma.mv)
Dear Shona,
The profile() function in metafor allows you to examine the profiled
(restricted) log-likelihood for a particular parameter. So, ideally, you
should do this for each variance component and correlation in the model
(in your case, sigma2, tau2 and rho). I am not sure what you mean by: "
how I know which value to specify for each?" After you have fitted your
model and stored the results in, let's say, 'res', then just do:
par(mfrow=c(3,1))
profile(res, sigma2=1)
profile(res, tau2=1)
profile(res, rho=1)
to get all three profile plots. And yes, the functions should peak at the
parameter estimates. If a function is flat, then this suggests that the
model is overparameterized.
You could also look at how quickly the log-likelihood drops off as you
move away from the parameter estimate. The bounds of a 95% profile
likelihood CI for a particular parameter would be those two values from
the x-axis where the log-likelihood has dropped by 3.84/2. You could add
abline(h = logLik(res) - qchisq(.95, df=1)/2, lty="dotted")
to the figures to see that cutoff. Depending on how much data you have,
you may find that those CIs are quite wide. You may have to increase the
x-axis range, in case the cutoff isn't reached within the range chosen by
default by the profile function (use the 'xlim' argument).
Indeed, you could also look at the (standardized) residuals. Use
rstandard(res)$z
to get those values. Or:
plot(fitted(res), rstandard(res)$z, pch=19)
to create a fitted values versus standardized residuals plot.
As for the interpretation of the results when you exclude the intercept
(i.e., mods = ~ Age + Treatment + Biomarker - 1), you will get the
estimated (average) effect for each level of 'Age', but the coefficients
for 'Treatment' and 'Biomarker' are still going to be contrasts that
indicate how much higher/lower the (average) effect is for the levels
indicated, relative to the reference level.
I hope this helps!
Best,
Wolfgang
-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-
models-bounces at r-project.org] On Behalf Of Shona Smith
Sent: Tuesday, September 02, 2014 18:37
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Post model fitting checks in Metafor (rma.mv)
Hi all,
I am currently conducting a meta-analysis using rma.mv in metafor. My
model uses Hedges' d (converted to g) and includes 3 moderators (age-3
levels; treatment-5 levels; biomarker-3 levels). I have included 3
random effects: species nested within taxonomic class (since I have
more
than one study for some species, and species are spread over 7
taxonomic
classes) and study separately. So my code is as follows:
rma.mv(yi, vi, mods = ~ Age + Treatment + Biomarker, random = list(~ 1
|
Study, ~ Species | Taxonomic.class), data=mydata)
I was wondering what the best method for post model fitting checks was
in
rma.mv? I know in the reference manual it mentions profile.rma to
create
a plot of the restricted log likelihood and I have done so. However, I
wondered if I need to plot all 3 variables (sigma2, tau2 and rho) and
also how I know which value to specify for each? Am I correct in that
I
should see a clear peak in each graph? Is there anything else I should
be looking for?
For post model fitting checks should I also look at residual normality
and residual against fitted values, as would be done for a typical
mixed
model? I think the standardised residuals are best for this - I can
get
them with rstandard.rma.mv, but it does not allow me to plot them.
Finally, when I include the intercept in the model, I can see if there
are significant differences among moderator levels. However, I was
wondering what the output includes when the intercept is not included:
is
this the overall effect size estimates for each moderator level?
Kind regards,
Shona
Shona Smith
PhD Student
Room 321
Institute of Biodiversity, Animal Health and Comparative Medicine
Graham Kerr Building
University of Glasgow
Glasgow G12 8QQ
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