[R] Perform GEE regression in R with multiple dependent variables

euthymios kasvikis
Fri, 03 Aug 2018 21:29:46 -0000

Im trying to perform generalized estimating equation (GEE) on the (sample)
dataset below with R and I would like some little guidance. First of all I
will describe my dataset. As you can see below it includes 5 variables.
Country_ID shows the country of the politician, Ideo_Ordinal his poltical
belief from 1 to 7 (far left to far right). Then we have measurements
regarding three characteristics. I would like to run an analysis based on
the country and the political beliefs of every politician (dependent
variables) in relation with the 3 characteristics. I have used the geepack
package using:

library(geepack)

        samplem<-coef(summary(geeglm(sample$Ideo_Ordinal
~Machiavellianism+Psychopathy+Narcissism ,data = sample, id sample$Ideo_Ordinal,
                                       corstr = "independence")))
%>%
          rownames_to_column() %>%
          mutate(lowerWald = Estimate-1.96*Std.err, # Lower Wald CI
                 upperWald=Estimate+1.96*Std.err,   # Upper Wald CI
                 df=1,
                 ExpBeta = exp(Estimate)) %>%       # Transformed estimate
          mutate(lWald=exp(lowerWald),              # Upper transformed
                 uWald=exp(upperWald))              # Lower transformed
        samplem

I would like to know if it is valid to add in this method the Country_ID
simultaneously with Ideo_Ordinal and how to do it.

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