我正在尝试使用R在下面的（样本）数据集上执行广义估计方程（GEE），我希望获得一些指导。首先，我将描述我的数据集。如下所示，它包含5个变量。 Country_ID从1到7（从最左边到最右边）显示了政治人物Ideo_Ordinal的国家。然后，我们对三个特征进行了测量。我想根据国家和每个政客的政治信念（因变量）与这三个特征进行分析。 GEE模型应隐藏国家/地区并取所有国家/地区的平均值，从而得出一个简单的回归模型，但要考虑到我们拥有不同的国家/地区。我使用geepack只是为了创建一个最小的可重现示例，但是我不确定这对于我的情况是最好的，因此我想对可以更好地处理多元回归的软件包提出建议和解决方案。
library(geepack)
samplem<-coef(summary(geeglm(sample$Ideo_Ordinal ~Machiavellianism+Psychopathy+Narcissism ,data = sample, id = sample$Country_ID,
corstr = "independence")))
# multgee version
library(multgee)
fitord <- ordLORgee(Ideo_Ordinal~ Machiavellianism+Psychopathy+Narcissism, data=RightWomen,
id= Politician_ID,repeated=Country_ID)
summary(fitord)
#sample dataset
Country_ID Ideo_Ordinal Machiavellianism Narcissism Psychopathy
3 1 3 0.250895132 0.155238716 0.128683755
5 1 3 -0.117725000 -0.336256435 -0.203137879
7 1 3 0.269509029 -0.260728261 0.086819555
9 1 6 0.108873496 0.175528190 0.182884928
14 1 3 0.173129951 0.054468468 0.155030794
15 1 6 -0.312088872 -0.414358301 -0.212599946
17 1 3 -0.297647658 -0.096523143 -0.228533352
18 1 3 -0.020389157 -0.210180866 -0.046687695
20 1 3 -0.523432382 -0.125114982 -0.431070629
21 1 1 0.040304508 0.022743463 0.233657881
22 1 3 0.253695988 -0.330825166 0.101122320
23 1 3 -0.478673895 -0.421801231 -0.422894791
27 1 6 -0.040856419 -0.566728704 -0.136069484
28 1 3 0.240040249 -0.398404825 0.135603114
29 1 6 -0.207631653 -0.005347621 -0.294935155
30 1 3 0.458042533 0.462935386 0.586244831
31 1 3 -0.259850232 -0.233074787 -0.092249465
33 1 3 0.002164223 -0.637668706 -0.267158031
34 1 6 0.050991955 -0.098030021 -0.043826848
36 1 3 -0.338052871 -0.168894328 -0.230198200
38 1 3 0.174382347 0.023807812 0.192963609
41 2 3 -0.227322148 -0.010016330 -0.095576329
42 2 3 -0.267514920 0.066108837 -0.218979873
43 2 3 0.421277754 0.385223920 0.421274111
44 2 3 -0.399592341 -0.498154998 -0.320402699
45 2 1 0.162038344 0.328116118 0.104105963
47 2 3 -0.080755709 0.003080287 -0.043568723
48 2 3 0.059474124 -0.447305420 0.003988071
49 2 3 -0.219773040 -0.312902659 -0.239057883
51 2 3 0.438659431 0.364042111 0.393014172
52 2 3 -0.088560903 -0.490889275 -0.006041054
53 2 3 -0.122612591 0.074438944 0.103722836
54 2 3 -0.450586055 -0.304253061 -0.132365179
55 2 6 -0.710545197 -0.451329850 -0.764201786
56 2 3 0.330718447 0.335460128 0.429173481
57 2 3 0.442508023 0.297522144 0.407155726
60 2 3 0.060797815 -0.096516876 -0.012802977
61 2 3 -0.250757764 -0.113219864 -0.215345379
62 2 1 0.153654345 -0.089615287 0.118626045
65 2 3 0.042969508 -0.486999608 -0.080829636
66 3 3 0.158337022 0.208229002 0.241607154
67 3 3 0.220237408 0.397914524 0.262207709
69 3 3 0.200558577 0.244419633 0.301732113
71 3 3 0.690244689 0.772692418 0.625921098
72 3 3 0.189810070 0.377774321 0.293988340
73 3 3 -0.385724422 -0.262131032 -0.373159652
74 3 3 -0.124095769 -0.109816334 -0.127157915
75 3 1 0.173299879 0.453592671 0.325357383
76 3 3 -0.598215129 -0.643286651 -0.423824759
77 3 3 -0.420558406 -0.361763025 -0.465612116
78 3 3 -0.176788569 -0.305506924 -0.203730879
80 3 3 -0.114790731 0.262392918 0.061382073
81 3 3 -0.274904173 -0.342603918 -0.302761994
82 3 3 -0.146902101 -0.059558818 -0.120550957
84 3 3 0.038303792 -0.139833875 0.170005914
85 3 3 -0.220212221 -0.541399757 -0.555201764
87 3 3 0.255300386 0.179484246 0.421428096
88 3 6 -0.548823069 -0.405541620 -0.322935805