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我目前正在对热带森林伐木的影响进行荟萃分析。

作为其中的一部分，我一直在测试关于影响是否因地区和使用的测井方法而异的假设。

我正在使用 R 中的metafor包完成所有这些工作。

我的数据如下所示：

structure(list(Method = structure(c(2L, 2L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), .Label = c("Conventional", "RIL"), class = "factor"), MU = c(192.96, 
252.41, 235.6, 258, 258, 399, 313, 409.8, 420.4, 333.47, 327.54, 
256, 228.1, 547.1, 453.3873094, 427.495, 346.8, 330.833333333333, 
343.3, 221.5, 194.8, 51.1, 276), SSU = c(3, 3, 30, 3, 3, 2, 5, 
17, 10, 4, 4, 4, 9, 15, 35, 10, 3, 3, 3, 3, 3, 3, 10), ML = c(157.03, 
171.97, 219.5, 198, 148, 191, 204, 315.3647059, 386.22, 135.8, 
211.78, 183.8, 159.9, 230.8, 97.00798294, 218.31, 279.933333333333, 
261.4, 249.733333333333, 118.6, 42.9, 18.7, 128.4), SSL = c(3, 
3, 10, 3, 3, 10, 5, 17, 10, 4, 4, 4, 9, 10, 131, 45, 3, 3, 3, 
3, 3, 3, 10), Region = structure(c(3L, 3L, 2L, 2L, 2L, 3L, 2L, 
2L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 3L, 3L
), .Label = c("Africa", "Americas", "Asia & Oceania"), class = "factor"), 
SDU = c(7.69030558560582, 12.1243556529821, 74.4902678207026, 
30, 30, 145, 107.33126291999, 64.9, 92.95, 40.73364703, 54.0371067, 
53.6, 98.1, 193.8, 16.13693527, 109.3250955, 28.21329474, 
30.91865671942, 32.220024829289, 37.065887281974, 96.4752299815865, 
37.4122974434878, 91.706052144883), SDL = c(8.46972844901181, 
7.81154914213564, 53.1262646908288, 18, 10, 324.8738217, 
84.970583144992, 44.90907399, 109.0794186, 20.75198304, 18.6400617, 
11.6, 88.2, 104.2, 4.008416039, 185.9464001, 29.85034897, 
28.7292533839639, 15.297494348204, 37.7587076050015, 32.5625551822949, 
7.44781847254617, 126.174878640718)), .Names = c("Method", 
"MU", "SSU", "ML", "SSL", "Region", "SDU", "SDL"), row.names = c(NA, 
23L), class = "data.frame")

然后，我使用它来计算我拥有数据的每个站点的效果大小和相关的 SE，如下所示：

require("metafor")
ROM <- escalc(data=AGB, measure="ROM", m2i=MU, sd2i=SDU,
              n2i=SSU, m1i=ML, sd1i=SDL, n1i=SSL, append=TRUE)

我的问题是我不知道如何从具有两个分类预测变量的模型中解释治疗对比。

我的“最佳”模型（AIC 最低的模型）如下所示：

ROM.ma1 <- rma(yi,vi,mods=~Method+Region,method="ML",data=ROM)

使用随机效应模型。

summary(ROM.ma1)

给我们这个：

Mixed-Effects Model (k = 23; tau^2 estimator: ML)

  logLik  deviance       AIC       BIC  
 -4.2852   65.8950   18.5705   24.2479  

tau^2 (estimated amount of residual heterogeneity):     0.0634 (SE = 0.0241)
tau (square root of estimated tau^2 value):             0.2519
I^2 (residual heterogeneity / unaccounted variability): 90.58%
H^2 (unaccounted variability / sampling variability):   10.62

Test for Residual Heterogeneity: 
QE(df = 19) = 616.2226, p-val < .0001

Test of Moderators (coefficient(s) 2,3,4): 
QM(df = 3) = 17.0683, p-val = 0.0007

Model Results:

                      estimate      se     zval    pval    ci.lb   ci.ub   
intrcpt                -0.4392  0.3150  -1.3944  0.1632  -1.0566  0.1781   
MethodRIL               0.3544  0.1513   2.3420  0.0192   0.0578  0.6510  *
RegionAmericas          0.1027  0.3201   0.3208  0.7484  -0.5247  0.7301   
RegionAsia & Oceania   -0.3487  0.3068  -1.1365  0.2557  -0.9500  0.2526   

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

现在我明白截距是第一级因子Method和的组合Region。

我想做的是计算每个组的系数估计值及其置信区间，以便我可以绘制该测试的结果。

有没有办法可以做到这一点？

我问了很多同事，他们都没有给我有用的答复。

提前致谢。

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