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

Duncan Mackay
Mon, 06 Aug 2018 04:29:07 -0000

Hi

Please read the geepack manual carefully.
GEE ordinal regression is not simple.
You need to format your data and do not use sample as a storage name. It is
the name of a function

dta is storage
dta$Ideo_Ordinal <- ordered(factor(dta$Ideo_Ordinal))

m0 <-
ordgee(Ideo_Ordinal ~ Machiavellianism+Psychopathy+Narcissism ,data = dta,
id = Country_ID,
       corstr = "independence")

You need to see if the model is appropriate first and whether the sandwich
errors are right before you go further

If this is your data you may not get credible results.
You need to read up on the requirements of GEEs and  ordinal GEEs in
particular
There are a number of packages with different data requirements and methods
If you have repeated measurements   repolr; ?multgee (just from memory)
Small sample sizes are a problem there are a number of packages dealing with
this but you will have to see which is best for you
Many do not offer a method for ordinal or multinomial GEE.
One further question to ask  population specific or subject specific  ie to
GEE or not to GEE


Regards

Duncan

Duncan Mackay
Department of Agronomy and Soil Science
University of New England
Armidale NSW 2350



-----Original Message-----
From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of euthymios
kasvikis
Sent: Saturday, 4 August 2018 07:30
To: r-help at r-project.org
Subject: [R] Perform GEE regression in R with multiple dependent variables

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

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