European MEP data, part 3

Wingfeet

8 years ago

[This article was first published on Wiekvoet, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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As final post on European MEPs voting I wanted to look at the individual MEP. The variables examined are how often present and how often present but not voted. The latter might be a marker of sign in and slope off. The analysis chosen is a hierarchical Bayesian analysis, which should push individual’s outcomes towards the population behavior.
As mentioned before, votewatch’s data describe how often MEPs voted what in the European Parliament. For each MEP the number of votes, percentages Yes, No, Abstain, number of elections and number of elections not voted. A description of the data can be found in this pdf.

Reading data

Data read as previously. All code shown at end of the post.

Modelling Data

Initially I wanted to use a beta binomial model as the beta distribution is the conjugate prior for the binomial and often seen in the text books. Then I realized the usual approach would be using the logit transformation. The wish to compare the two approaches lead me to adding the probit and finally a different way to calculate the beta binomial. Calculations are done in Jags. In text the models are displayed, end of text is full code. The number of samples is chosen 1000, which is more than needed for interpretation of the data, but with the comparison between methods a few extra samples seemed nice.

Model 1: beta binomial

The model is simple enough not to need much explanation. Priors on a and b are intended to be non-informative.

mbb1 <- function() {

for (i in 1:N) {

nchosen[i] ~ dbin(p[i],nvotes[i])

p[i] ~ dbeta(a,b)

}

a ~ dexp(.001)

b ~ dexp(.001)

}

Model 2: Beta binomial

The thought leading to this specification is that with beta distribution conjugate prior, it should be possible to calculate posteriour distributions for probabilities outside of Jags, thereby saving Jags a bit of time and saving a load of data transfer between Jags and R.

mbb2 <- function() {

for (i in 1:N) {

nchosen[i] ~ dbetabin(a,b,nvotes[i])

}

a ~ dexp(.001)

b ~ dexp(.001)

}

Model 3: Logit

The model is reasonable straigthforward. The awkward names for mean and sd, ap and asd respectively, are chosen thus so samples can be extracted similar to model 1.

mlogit <- function() {

for (i in 1:N) {

nchosen[i] ~ dbin(p[i],nvotes[i])

logit(p[i]) <- lp[i]

lp[i] ~ dnorm(ap,tau)

}

ap ~ dnorm(0,.001)

tau <- pow(asd,-2)

asd ~dunif(0,10)

}

Model 4: Probit

This is just replacing logit() by probit().

mprobit <- mlogit <- function() {

for (i in 1:N) {

nchosen[i] ~ dbin(p[i],nvotes[i])

logit(p[i]) <- lp[i]

lp[i] ~ dnorm(ap,tau)

}

ap ~ dnorm(0,.001)

tau <- pow(asd,-2)

asd ~dunif(0,10)

}

Results

Presence of MEPs

The beta distribution obtained shows that most of the MEPs are signed in. MEPs with more that 20% of the votes not singed in are rare.

The best performing MEPs are shown below for beta binomial 1, logit and probit model are shown below. The results show different MEPs for each calculation. This is an artifact due to using a sampler, these MEPs are way to close to each other for meaningful ordering

beta-binomial

Last.Name First.Name National.Party

463 STAVRAKAKIS Georgios Panhellenic Socialist Movement

218 JEGGLE Elisabeth Christlich Demokratische Union Deutschlands

80 AUDY Jean-Pierre Union pour un Mouvement Populaire

452 DESS Albert Christlich-Soziale Union in Bayern e.V.

273 LISEK Krzysztof Platforma Obywatelska

372 ZEMKE Janusz Władysław Sojusz Lewicy Demokratycznej

Mean p5 p95

463 0.001439957 4.612416e-05 0.004372737

218 0.001450050 3.465712e-05 0.004839997

80 0.001470705 4.752273e-05 0.004617277

452 0.001491458 3.185192e-05 0.005023073

273 0.001497189 4.672592e-05 0.004592358

372 0.001499812 5.112743e-05 0.004861630

logit

Last.Name First.Name Mean p5 p95

57 BACH Georges 0.002845830 0.0005561477 0.006789563

514 LUDVIGSSON Olle 0.002854100 0.0005746037 0.006441176

548 SCHALDEMOSE Christel 0.002882655 0.0006066471 0.006735655

372 ZEMKE Janusz Władysław 0.002925083 0.0006555610 0.006699789

85 SCHWAB Andreas 0.002927801 0.0006034058 0.007039026

273 LISEK Krzysztof 0.002940536 0.0006825662 0.007001949

Probit

Last.Name First.Name Mean p5 p95

687 EHRENHAUSER Martin 0.002929976 0.0006652270 0.006671044

57 BACH Georges 0.002935575 0.0006351852 0.006839898

98 MATULA Iosif 0.002947576 0.0006889951 0.006754586

449 CERCAS Alejandro 0.002953899 0.0006167549 0.007239345

564 BELDER Bastiaan 0.002961148 0.0006210927 0.007109177

184 LANGEN Werner 0.002967294 0.0006372111 0.006767979

The worst MEPs are quite different from each other, the calculations yield similar order

Beta-Binomial
Last.Name First.Name
116 TREMATERRA Gino
5 PETROVIĆ JAKOVINA Sandra
734 MORVAI Krisztina
27 VADIM TUDOR Corneliu
396 CROWLEY Brian
60 VITKAUSKAITE BERNARD Justina
National.Party Mean
116 Unione dei Democratici cristiani e dei Democratici di Centro 0.2961918
5 Socijaldemokratska partija Hrvatske 0.2974738
734 JOBBIK MAGYARORSZÁGÉRT MOZGALOM 0.3184573
27 Partidul România Mare 0.3411132
396 Fianna Fáil Party 0.5118451
60 Darbo partija 0.5233919
p5 p95
116 0.2507316 0.3439449
5 0.2031520 0.4045347
734 0.2862299 0.3524033
27 0.3084684 0.3742007
396 0.4744408 0.5481621
60 0.4469903 0.5995429
logit
Last.Name First.Name Mean p5 p95
116 TREMATERRA Gino 0.3010280 0.2535735 0.3496571
734 MORVAI Krisztina 0.3222543 0.2894436 0.3566998
5 PETROVIĆ JAKOVINA Sandra 0.3393291 0.2280846 0.4542476
27 VADIM TUDOR Corneliu 0.3447283 0.3108516 0.3791264
396 CROWLEY Brian 0.5189438 0.4840589 0.5560512
60 VITKAUSKAITE BERNARD Justina 0.5530630 0.4794193 0.6288764
probit
Last.Name First.Name Mean p5 p95
116 TREMATERRA Gino 0.3033747 0.2566566 0.3510163
734 MORVAI Krisztina 0.3217914 0.2889948 0.3545175
5 PETROVIĆ JAKOVINA Sandra 0.3389094 0.2317717 0.4564703
27 VADIM TUDOR Corneliu 0.3461575 0.3142402 0.3773828
396 CROWLEY Brian 0.5193148 0.4849846 0.5543466
60 VITKAUSKAITE BERNARD Justina 0.5556912 0.4837601 0.6299831

Second binomial
name mean q05 q95
[1,] “TREMATERRA” 0.2956197 0.2491403 0.3420113
[2,] “PETROVIĆ JAKOVINA” 0.2998581 0.2078026 0.4042624
[3,] “MORVAI” 0.3183077 0.2855259 0.3524003
[4,] “VADIM TUDOR” 0.3411089 0.3064908 0.3754585
[5,] “CROWLEY” 0.511809 0.4767657 0.5477082
[6,] “VITKAUSKAITE BERNARD” 0.5231093 0.4554704 0.5919841

It thus seems that beta binomial have slightly different outcomes than logit and probit, with beta-binomial giving slightly smaller parameter values. The shift for PETROVIĆ JAKOVINA is due to less data. It appears beta binomial had a stronger effect of the prior.
Last.Name TotalPossible nNotIn
5 PETROVIĆ JAKOVINA 40 15
27 VADIM TUDOR 514 179
734 MORVAI 514 167

Signed in, not voting

To save duplication, only logit and beta-binomial are shown. It would seem that signing in but not voting is structural, the typical MEP does this a few to 15% of the votes.

The best MEPs in this respect include an independent, which did surprise me. As a Dutch voter, having a member of SGP there does not surprise me, while strong Christianity is not my political flavor, they do have a trustworthy reputation many other politician can be jealous of. In terms of logit against beta-binomial, the logit now has lower numbers, which suggests that the beta-binomial again has again more pull toowards the population.

beta binomial

Last.Name First.Name National.Party

350 BŘEZINA Jan Independent

175 GROSSETÊTE Françoise Union pour un Mouvement Populaire

21 MAZEJ KUKOVIČ Zofija Slovenska demokratska stranka

333 BRATKOWSKI Arkadiusz Tomasz Polskie Stronnictwo Ludowe

564 BELDER Bastiaan Staatkundig Gereformeerde Partij

55 PAPANIKOLAOU Georgios Nea Demokratia

Mean p5 p95

350 0.004855719 0.0012133168 0.01027123

175 0.005019880 0.0011882656 0.01056993

21 0.006102050 0.0007614780 0.01547558

333 0.006392797 0.0007950631 0.01628781

564 0.006590296 0.0021129110 0.01337183

55 0.006678283 0.0022042544 0.01360759

logit

Last.Name First.Name Mean p5 p95

175 GROSSETÊTE Françoise 0.007681981 0.003254561 0.01387197

350 BŘEZINA Jan 0.007820303 0.003207069 0.01421726

55 PAPANIKOLAOU Georgios 0.008982877 0.003933873 0.01604022

452 DESS Albert 0.009006247 0.003923229 0.01594389

376 CUTAŞ George Sabin 0.009011560 0.003966942 0.01585792

237 CANCIAN Antonio 0.009144514 0.004149473 0.01669278

Finally, the worst politicians in this respect. Note that Jerzy BUZEK was president of the EP 2009-2012, Martin Schultz president of EP since 2012, which probably means they were there but did not vote in that role. Comparing beta-binomial to logit, the beta-binomial again pulls a bit stronger to the population.

binomial

Last.Name First.Name National.Party

686 ZIOBRO Zbigniew Solidarna Polska

492 DÉSIR Harlem Parti Socialiste

766 BLOOM Godfrey United Kingdom Independence Party

418 BAALEN Johannes Cornelis van Volkspartij voor Vrijheid en Democratie

542 SCHULZ Martin Sozialdemokratische Partei Deutschlands

123 BUZEK Jerzy Platforma Obywatelska

Mean p5 p95

686 0.3490508 0.3118425 0.3865028

492 0.3921216 0.3546858 0.4314310

766 0.3928973 0.3527089 0.4342107

418 0.4118334 0.3731467 0.4485519

542 0.4815201 0.4464745 0.5160744

123 0.5878925 0.5531420 0.6217835

logit

Last.Name First.Name Mean p5 p95

686 ZIOBRO Zbigniew 0.3567185 0.3177950 0.3941565

492 DÉSIR Harlem 0.4000572 0.3610226 0.4393352

766 BLOOM Godfrey 0.4013182 0.3633043 0.4407786

418 BAALEN Johannes Cornelis van 0.4211575 0.3836522 0.4590419

542 SCHULZ Martin 0.4903926 0.4539334 0.5266999

123 BUZEK Jerzy 0.6003081 0.5642402 0.6344741

Code

library(gdata)

r1 <- read.xls(‘votewatch-europe-yes-no-votes-data-11-december-2013.preprocessed.xls’,

stringsAsFactors=TRUE)

# adapted in source file:

# headers

# number of decimals

# “

# fix a double

r1$National.Party <- as.character(r1$National.Party)

(uu <- unique(r1$National.Party[r1$Country==’Hungary’ &

grepl(‘ri Sz’,r1$National.Party)]))

r1$National.Party[r1$National.Party %in% uu] <- uu[1]

r1$National.Party <- factor(r1$National.Party)

r1$Date <- as.Date(r1$Sdate)

r1$Group <- relevel(r1$Group,’S&D’)

r1$nYES=round(r1$pYES*r1$TotalDone/100)

r1$nNO=round(r1$pNO*r1$TotalDone/100)

r1$nAbstain=round(r1$pAbstain*r1$TotalDone/100)

# 3 vars before sum to TotalDone

r1$nNoVote=round(r1$pNoVote*r1$TotalPossible/100)

r1$nNotIn <- r1$TotalPossible-r1$nNoVote-r1$TotalDone

# 5 vars before sum to TotalPossible

n.iter=1000

library(R2jags)

datain <- list(nchosen=r1$nNotIn,

nvotes=r1$TotalPossible,

N=nrow(r1))

mbb1 <- function() {

for (i in 1:N) {

nchosen[i] ~ dbin(p[i],nvotes[i])

p[i] ~ dbeta(a,b)

}

a ~ dexp(.001)

b ~ dexp(.001)

}

params <- c(‘a’,’b’,’p’)

inits <- function() {

list(a=runif(1,0,5),

b=runif(1,0,5),

p=runif(datain$N,0,1))

}

jfbb1 <- jags(datain,

model=mbb1,

inits=inits,

parameters=params,

progress.bar=”gui”,

n.iter=n.iter)

pd <- seq(0,1,by=.01)

png(‘plotbb1.png’)

plot(dbeta(pd,

mean(jfbb1$BUGSoutput$sims.matrix[,1]),

mean(jfbb1$BUGSoutput$sims.matrix[,2])),

x=pd,

type=’l’,

main=’MEPs signing in’,

xlab=’Proportion not signed in’,

ylab=’MEP Density’)

dev.off()

##############

mbb2 <- function() {

for (i in 1:N) {

nchosen[i] ~ dbetabin(a,b,nvotes[i])

}

a ~ dexp(.001)

b ~ dexp(.001)

}

params2 <- c(‘a’,’b’)

inits2 <- function() {

list(a=runif(1,0,5),

b=runif(1,0,5))

}

jfbb2 <- jags(datain,

model=mbb2,

inits=inits2,

parameters=params2,

progress.bar=”gui”,

n.iter=n.iter)

res2 <- subset(r1,,c(

Last.Name,

First.Name,

Country,

Group,

National.Party,

nNotIn,

TotalPossible))

res2 <- merge(res2,

data.frame(a=jfbb2$BUGSoutput$sims.matrix[,1],

b=jfbb2$BUGSoutput$sims.matrix[,2]))

res2$p <- rbeta(

nrow(res2),

res2$a+res2$nNotIn,

res2$b+res2$TotalPossible-res2$nNotIn)

##############

mlogit <- function() {

for (i in 1:N) {

nchosen[i] ~ dbin(p[i],nvotes[i])

logit(p[i]) <- lp[i]

lp[i] ~ dnorm(ap,tau)

}

ap ~ dnorm(0,.001)

tau <- pow(asd,-2)

asd ~dunif(0,10)

}

paramslp <- c(‘ap’,’asd’,’p’)

initslp <- function() {

list(ap=rnorm(1,0),

asd=runif(1,0,10),

lp=rnorm(datain$N,0,1))

}

jflogit <- jags(datain,

model=mlogit,

inits=initslp,

parameters=paramslp,

progress.bar=”gui”,

n.iter=n.iter)

mprobit <- mlogit <- function() {

for (i in 1:N) {

nchosen[i] ~ dbin(p[i],nvotes[i])

logit(p[i]) <- lp[i]

lp[i] ~ dnorm(ap,tau)

}

ap ~ dnorm(0,.001)

tau <- pow(asd,-2)

asd ~dunif(0,10)

}

jfprobit <- jags(datain,

model=mprobit,

inits=initslp,

parameters=paramslp,

progress.bar=”gui”,

n.iter=n.iter)

# dispay results

makres <- function(bf) {

sm <- bf$BUGSoutput$sims.matrix[,-(1:3)]

res <- r1[,c(1,2,3,5)]

res$Mean <- apply(sm,2,mean)

res$p5 <- apply(sm,2,quantile,p=.05)

res$p95 <- apply(sm,2,quantile,p=.95)

res <- res[order(res$Mean),]

}

rbb1 <- makres(jfbb1)

rlogit <- makres(jflogit)

rprobit <- makres(jfprobit)

head(rbb1[,-3])

head(rlogit[,-(3:4)])

head(rprobit[,-(3:4)])

tail(rbb1[,-3])

tail(rlogit[,-(3:4)])

tail(rprobit[,-(3:4)])

t(sapply(rbb1$Last.Name[761:766],function(x) {

mm=mean(res2$p[res2$Last.Name==x])

qq=quantile(res2$p[res2$Last.Name==x],c(0.05,0.95))

list(name=as.character(x),mean=mm,q05=qq[1],q95=qq[2])

}

))

r1[r1$Last.Name %in% rbb1$Last.Name[762:764],c(1,11,18)]

####################

datain2 <- list(nchosen=r1$nNoVote,

nvotes=r1$TotalPossible-r1$nNotIn,

N=nrow(r1))

jfbbnv <- jags(datain2,

model=mbb1,

inits=inits,

parameters=params,

progress.bar=”gui”,

n.iter=n.iter)

jflogitnv <- jags(datain2,

model=mlogit,

inits=initslp,

parameters=paramslp,

progress.bar=”gui”,

n.iter=n.iter)

png(‘novote.png’)

plot(dbeta(pd,

mean(jfbbnv$BUGSoutput$sims.matrix[,1]),

mean(jfbbnv$BUGSoutput$sims.matrix[,2])),

x=pd,

type=’l’,

main=’MEPs not voting’,

xlab=’Proportion not voting’,

ylab=’MEP Density’)

dev.off()

rbbnv <- makres(jfbbnv)

rlogitnv <- makres(jflogitnv)

head(rbbnv[,-3])

head(rlogitnv[,-(3:4)])

tail(rbbnv[,-3])

tail(rlogitnv[,-(3:4)])

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