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

euthymios kasvikis
Mon, 06 Aug 2018 15:00:30 -0000

First of all thanks for your advice. So suppose that I would like to use
the multgee package. The model would be like:
library(multgee)
fitord <- ordLORgee(Ideo_Ordinal~ Machiavellianism+Psychopathy+Narcissism,
data=RightWomen,
                    id= ordered(factor(Country_ID)))
summary(fitord)

???? ???, 6 ??? 2018 ???? 7:29 ?.?., ?/? Duncan Mackay <dulcalma at
bigpond.com>
??????:

> 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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>
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