[R-meta] Metafor modeling question

Tue, Mar 28, 2023 1:36 AM

I would start by running:

par(mfrow=c(3,2))
profile(mm)

which will indicate that none of the var-cor components are identifiable.

The only study with any variability in SES is study 18 (all other studies have SES = 1), which makes analyzing the association between the effect sizes and SES a difficult endeavor.

Not sure how I would approach this. Possibly, I might consider just looking at study 18 by itself to examine this relationship:

plot(yi ~ SES, data=dat, subset=study==18, pch=21, bg="gray", cex=0.5/sqrt(vi))

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On
Behalf Of Yuhang Hu via R-sig-meta-analysis
Sent: Tuesday, 28 March, 2023 6:35
To: R meta
Cc: Yuhang Hu
Subject: Re: [R-meta] Metafor modeling question

Dear Wolfgang,

Thanks you so much for your response. Regarding the fitted vs residuals
plot, if we see a violation (e.g., heteroskedasticity and/or non-random
pattern in residuals' distribution around the regression line) as in the
case below, what should we do?

I would imagine that if I was working with an rma.uni() model, I could
model the heteroskedasticity using the 'scale=' argument?

Thanks,
Yuhang

mm <- rma.mv(yi ~ SES, vi, random = list(~SES|study, ~SES|outcome),
            struct = c("GEN","GEN"), data = dat)

plot(fitted(mm), resid(mm))

dat <- structure(list(study = c(1L, 1L, 2L, 2L, 3L, 3L, 4L, 5L, 6L,
7L, 7L, 8L, 8L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 11L, 12L,
13L, 13L, 14L, 14L, 15L, 15L, 16L, 16L, 17L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 19L, 19L, 20L,
20L), group = c(1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,
2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1), outcome = c("A",
"A", "B", "B", "B", "B", "B", "A", "A", "A", "B", "A", "A", "A",
"B", "A", "B", "A", "B", "A", "B", "B", "A", "A", "B", "B", "B",
"A", "A", "B", "B", "A", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "A", "B", "A", "B"), SES = c(1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 7, 13, 10, 8, 16, 3, 2, 18, 1,
5, 4, 20, 11, 9, 14, 17, 15, 6, 12, 19, 7, 13, 10, 8, 16, 3,
2, 18, 1, 5, 4, 20, 11, 9, 14, 17, 15, 6, 12, 19, 1, 1, 1, 1),
   yi = c(0.661693, 0.594366, 1.199301, 1.626664, 1.216272,
   -0.751534, -1.170659, 0.959518, 0.353214, 1.595024, 1.689419,
   0.462459, 1.324527, 0.61639, 0.615806, 0.616544, 0.617199,
   1.420133, 1.542125, 1.338082, 1.523729, 0.569801, 1.041618,
   6.796397, 3.241257, 0.97541, 1.369298, 1.706266, 1.964219,
   1.554362, 1.693888, 0.939487, 0.349941, 0.364232, 0.41826,
   0.37037, 0.378151, 0.265958, 0.250613, 0.32379, 0.318541,
   0.331738, 0.373367, 0.242215, 0.420524, 0.153225, 0.287845,
   0.43818, 0.315411, 0.264576, 0.33054, 0.35668, 0.393478,
   0.468176, 0.342097, 0.39018, 0.332325, 0.414179, 0.303209,
   0.290351, 0.398231, 0.283192, 0.290464, 0.352266, 0.323642,
   0.357534, 0.18504, 0.349599, 0.322425, 0.330017, 0.273626,
   0.484511, 3.087989, -0.427958, 1.133235, 1.073414), vi = c(0.030703,
   0.355305, 0.190678, 0.525746, 0.257342, 0.371465, 0.65578,
   0.970636, 0.605049, 0.584372, 0.185532, 0.140877, 0.618397,
   0.880055, 0.29737, 0.186943, 0.376905, 0.327622, 0.232547,
   0.288472, 0.88999, 0.203553, 0.202477, 0.94609, 0.694122,
   0.79155, 0.260697, 0.689051, 0.332882, 0.883863, 0.265871,
   0.443643, 0.71795, 0.325523, 0.720786, 0.905436, 0.777423,
   0.424495, 0.449885, 0.436236, 0.551392, 0.412329, 0.841006,
   0.03359, 0.592244, 0.159267, 0.430026, 0.044465, 0.341106,
   0.163984, 0.037114, 0.9678, 0.215364, 0.111536, 0.292115,
   0.548628, 0.25953, 0.217442, 0.615006, 0.108288, 0.80669,
   0.789254, 0.312538, 0.996466, 0.277414, 0.975438, 0.710994,
   0.028242, 0.488088, 0.921909, 0.622881, 0.145062, 0.240907,
   0.461285, 0.881521, 0.974101)), row.names = c(NA, -76L), class =
"data.frame")

On Mon, Mar 27, 2023 at 12:49?AM Viechtbauer, Wolfgang (NP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:

Dear Yuhang,

Please see below for my answers.

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:

r-sig-meta-analysis-bounces at r-project.org] On

Behalf Of Yuhang Hu via R-sig-meta-analysis
Sent: Sunday, 26 March, 2023 6:28
To: R meta
Cc: Yuhang Hu
Subject: [R-meta] Metafor modeling question

Dear All,

I have two questions about the following model:

mm <- rma.mv(y ~ SES, V, random = list(~SES|study, ~SES|outcome), struct

c("GEN","GEN"))

fit to a dataset structurally similar to:

study outcome SES
1     A       1.5
1     A       1.7
1     B       1.5
1     B       1.7
2     A       2.1
3     A       1.1
3     A       2.3
3     B       1.1
3     B       2.3

Question 1: I know, if I used: "random = list(~1 | study, ~1| outcome)",
then 'study' and 'outcome' would be crossed, random effects. But are
'study' and 'outcome' in model 'mm' still crossed,
random effects? (In other words, can crossed random-effects have varying
coefficients in them)

Yes, they are crossed in this example.

Question 2: Is it possible (or useful) to plot residuals vs. fitted values
for model 'mm'? If yes, how can we do that in metafor?

fitted(mm) gives you the fitted values and resid(mm) gives you the
residuals, so you can just plot them against each other with
plot(fitted(mm), resid(mm)) or you can use the standardized residuals on
the y-axis with plot(fitted(mm), rstandard(mm)$z), since the residuals
themselves are heteroscedastic, but the standardized residuals are ...
well, standardized!

Whether such a plot is useful is difficult to say in general, but I
suppose it can be used in the same manner as it is used with primary data.

Many thanks for your help,
Yuhang

[R-meta] Metafor modeling question

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