[R-meta] Asking for continuous moderating effects
Kim, Jaewoo
jk|m @end|ng |rom b@uer@uh@edu
Fri Sep 10 00:18:01 CEST 2021
Thank you for your heads up, Professor Viechtbauer. I will keep that in mind. I have attached the sample excel file and the sample code where the problem is to be reproduced. Could you please take a look at these files? As I have a problem with the result I below, I only included the sample data regarding result I. Please let me know if you need anything else to reproduce. Many thanks.
I am analyzing continuous moderating effects with metafor. When I ran the moderator analysis, I found that R^2 is NA%. Since my metafor is an older version, I thought NA appeared in the results. Though I updated my metafor (v 3.0-2), the results were still the same (please see the result I). However, when I ran another moderator analysis with metafor, R^2 appeared 0, not NA (please see result II). May I ask why this happens? In this case, can I view NA as 0%? Below are the results with R code.
Results I
# Moderator 1
res <- rma(rAB, vAB, mods = ~ M1, data=sample_moderator)
res
Mixed-Effects Model (k = 17; tau^2 estimator: REML)
tau^2 (estimated amount of residual heterogeneity): 0.0069 (SE = 0.0112)
tau (square root of estimated tau^2 value): 0.0830
I^2 (residual heterogeneity / unaccounted variability): 22.42%
H^2 (unaccounted variability / sampling variability): 1.29
R^2 (amount of heterogeneity accounted for): NA%
Test for Residual Heterogeneity:
QE(df = 15) = 16.9198, p-val = 0.3237
Test of Moderators (coefficient 2):
QM(df = 1) = 9.4482, p-val = 0.0021
Model Results:
estimate se zval pval ci.lb ci.ub
intrcpt -0.5843 0.2530 -2.3094 0.0209 -1.0802 -0.0884 *
M1 0.2672 0.0869 3.0738 0.0021 0.0968 0.4375 **
===================================================================
Results II
res <- rma(rAE, vAE, mods = ~ M1, data=sample_moderator)
res
Mixed-Effects Model (k = 10; tau^2 estimator: REML)
tau^2 (estimated amount of residual heterogeneity): 0.0291 (SE = 0.0194)
tau (square root of estimated tau^2 value): 0.1705
I^2 (residual heterogeneity / unaccounted variability): 77.99%
H^2 (unaccounted variability / sampling variability): 4.54
R^2 (amount of heterogeneity accounted for): 0.00%
Test for Residual Heterogeneity:
QE(df = 8) = 37.9082, p-val < .0001
Test of Moderators (coefficient 2):
QM(df = 1) = 0.9967, p-val = 0.3181
Model Results:
estimate se zval pval ci.lb ci.ub
intrcpt 0.4235 0.2087 2.0297 0.0424 0.0146 0.8325 *
M1 -0.0862 0.0863 -0.9984 0.3181 -0.2554 0.0830
========================================================================
From: Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer using maastrichtuniversity.nl>
Sent: Thursday, September 9, 2021 3:05 AM
To: Kim, Jaewoo <jkim using bauer.uh.edu>; r-sig-meta-analysis using r-project.org <r-sig-meta-analysis using r-project.org>
Subject: RE: [R-meta] Asking for continuous moderating effects
Dear Jaewoo,
Please provide a reproducible example for the case where R^2 is reported as NA%.
Also, please switch of line wrapping in your email client (or whatever is causing the results below to be so mangled up).
Best,
Wolfgang
>-----Original Message-----
>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On
>Behalf Of Kim, Jaewoo
>Sent: Thursday, 09 September, 2021 9:46
>To: r-sig-meta-analysis using r-project.org
>Subject: [R-meta] Asking for continuous moderating effects
>
>Hello,
>
>Hope that this email goes well. I am analyzing continuous moderating effects with
>metafor. When I ran the moderator analysis, I found that R^2 is NA%. Since my
>metafor is an older version, I thought NA appeared in the results. Though I
>updated my metafor (v 3.0-2), the results were the same (please see the result I).
>However, when I ran another moderator analysis with metafor, R^2 appeared 0, not
>NA (please see result II). May I ask why this happens? In this case, can I view NA
>as 0%? Below are the results with R code.
>
>Results I
>
>res <-
>rma(rAB, vAB, mods = ~ M1, data=en_moderator)
>res
>
>Mixed-Effects
>Model (k = 17; tau^2 estimator: REML)
>
>tau^2
>(estimated amount of residual heterogeneity): 0.0069 (SE = 0.0112)
>tau (square
>root of estimated tau^2 value):
>0.0830
>I^2 (residual
>heterogeneity / unaccounted variability): 22.42%
>H^2
>(unaccounted variability / sampling variability): 1.29
>R^2 (amount
>of heterogeneity accounted for):
>NA%
>
>Test for
>Residual Heterogeneity:
>QE(df = 15) =
>16.9198, p-val = 0.3237
>
>Test of
>Moderators (coefficient 2):
>QM(df = 1) =
>9.4482, p-val = 0.0021
>
>Model
>Results:
>
> estimate se
>zval pval ci.lb
>ci.ub
>intrcpt -0.5843
>0.2530 -2.3094 0.0209
>-1.0802 -0.0884 *
>M1 0.2672
>0.0869 3.0738 0.0021
>0.0968 0.4375 **
>
>===================================================================
>
>Results II
>
>res <-
>rma(rAE, vAE, mods = ~ M1, data=en_moderator)
>res
>
>Mixed-Effects
>Model (k = 10; tau^2 estimator: REML)
>
>tau^2
>(estimated amount of residual heterogeneity): 0.0291 (SE = 0.0194)
>tau (square
>root of estimated tau^2 value):
>0.1705
>I^2 (residual
>heterogeneity / unaccounted variability): 77.99%
>H^2
>(unaccounted variability / sampling variability): 4.54
>R^2 (amount
>of heterogeneity accounted for):
>0.00%
>
>Test for
>Residual Heterogeneity:
>QE(df = 8) =
>37.9082, p-val < .0001
>
>Test of
>Moderators (coefficient 2):
>QM(df = 1) =
>0.9967, p-val = 0.3181
>
>Model
>Results:
>
> estimate se
>zval pval ci.lb
>ci.ub
>intrcpt 0.4235
>0.2087 2.0297 0.0424
>0.0146 0.8325 *
>M1 -0.0862
>0.0863 -0.9984 0.3181
>-0.2554 0.0830
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