[R-meta] Asking for continuous moderating effects

[Kim, Jaewoo]

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