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)
Στις Δευ, 6 Αυγ 2018 στις 7:29 π.μ., ο/η Duncan Mackay <dulca...@bigpond.com>
έγραψε:
> 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-boun...@r-project.org] On Behalf Of euthymios
> kasvikis
> Sent: Saturday, 4 August 2018 07:30
> To: r-help@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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