Significant birthdays in the weekend

I am a listener to BBC's podcast More or Less. In the program Tim Harford looks at data with both humour and determination to find what the numbers mean. Last week he handled a listener question. Does everybody get a significant birthday (20, 30 years etc.) in a weekend. His back of the envelope answer was, yes, we think so. Given the regularity by which days shift over the years, it should happen by 60 latest. If anybody has this at 70, please contact us. This blog post tries to answer by which youngest age anybody born in the previous century has a significant birthday in the weekend and concludes that indeed by 60 latest it should have happened. Not surprising, 20 years has 2/7=28% of days. Somewhat surprising, 30, 40 and 50 are approximately equally probable (3/14=21%).
Additional note. I was not the only one who did this calculation. This weekend he mentioned some other listeners who emailed equivalent results to him.

Code

# days contains all days as month-day in a year.
days <- format(as.Date('1900-01-01',format='%Y-%m-%d')+0:364,'%m-%d')
tail(days)
[1] "12-26" "12-27" "12-28" "12-29" "12-30" "12-31"

# does a year day combination fall in weekend,
# format makes Sunday to '0', Saturday to '6'
isspecday <- function(y,d) {
  i <- paste(y,d,sep='-')
  format(as.Date(i,format='%Y-%m-%d'),'%w') %in% c('0','6')
}

# which years have a weekend, for a given day (month day combination)
sourcedata <- function(dayno) {
  year <- 1900:2100
  days <- isspecday(year,days[dayno])
  data.frame(year=year,sunday=days)
}

# example - use fifth day
sd1 <- sourcedata(5)
# reorganize in matrix so 20 , 30.. years after get next to each other
sd2 <- sapply(seq(20,80,10),function(x) sd1$sunday[(1:100)+x])
# make columns to ages
# other years get 1000, so min() can extract minimum age
sd3 <- sd2 * rep(1,100) %o% seq(20,80,10)
sd3[sd3==0] <- 1000
tail(sd3)
       [,1] [,2] [,3] [,4] [,5] [,6] [,7]
[95,]    20 1000 1000 1000 1000   70 1000
[96,]  1000   30 1000 1000 1000 1000   80
[97,]  1000 1000   40 1000 1000 1000   80
[98,]  1000 1000 1000   50 1000 1000 1000
[99,]  1000 1000 1000   50   60 1000 1000
[100,]   20 1000 1000 1000   60   70 1000

# result, ages
table(apply(sd3,1,min))
20 30 40 50 60 
28 22 21 22  7 

# make a wrapper, i to become day
goodyear <- function(i) {
  sd1 <- sourcedata(i)
  sd2 <- sapply(seq(20,80,10),function(x) sd1$sunday[(1:100)+x])
  sd3 <- sd2 * rep(1,100) %o% seq(20,80,10)
  sd3[sd3==0] <- 1000
  table(apply(sd3,1,min))
}
# and apply
sa <- sapply(1:365,goodyear)
# for brievety, only show last days
tail(t(sa))
       20 30 40 50 60
[360,] 28 22 22 21  7
[361,] 28 22 21 22  7
[362,] 28 21 22 21  8
[363,] 29 21 21 22  7
[364,] 29 21 21 22  7
[365,] 29 22 21 21  7
# overall
colSums(t(sa))/365
       20        30        40        50        60 
28.572603 21.430137 21.424658 21.430137  7.142466 

European MEP data, part 3

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)])

European MEP Data, Part 2

Following last week's short examination, I now wanted to drill down a bit more in the voting behaviour as given in data from votewatch.eu on voting of MEPs.
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.

Data

Compared to last week there is a slight adaptation in reading data. The same preprocessed spreadsheet is used, but next processing has been adapted.

library(gdata)

library(ggplot2)

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

    stringsAsFactors=TRUE)

# adapted in souce 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)]))

[1] "Fidesz-Magyar Polgári Szövetség-Keresztény Demokrata Néppárt"      

[2] "Fidesz-Magyar Polgári Szövetség-Keresztény Demokrata Néppárt"

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

Group data

In this section data per group is analyzed. The analysis results are mean estimates, their dispersion is a function of the group size. The analysis result has three stages

1. proportion present, 
2. proportion voted conditional on being present, 
3. proportion votes Yes, No, Abstain, conditional on voting. 

This means the proportions do not add to 1. There is an ANOVA calculation in the analysis. However, since it is highly significant for all responses, they are not displayed.
The script itself consists of a wrapper around a glm() call. This was the most easy approach when I did think displaying ANOVA would be a good idea. It is retained because it makes the dependent variable used clearly readable. 

test1 <- function(response) {

  g1 <- glm(response ~ Group,

      data=r1,family=binomial)

  g0 <- glm(response ~ 1,

      data=r1,family=binomial)

  print(anova(g1,g0,test='Chisq'))

  g1

}

g1 <- test1(with(r1,

        cbind(

            nNotIn,

            nIn=TotalPossible-nNotIn)))

h1 <- test1(with(r1,

        cbind(

            nNoVote,

            nVote=TotalPossible-nNotIn-nNoVote)))

i1 <- test1(with(r1,

        cbind(

            nYES,

            nNotYES=TotalDone-nYES)))

j1 <- test1(with(r1,

        cbind(

            nNO,

            nNotNO=TotalDone-nNO)))

k1 <- test1(with(r1,

        cbind(

            nAbstain,

            nNotAb=TotalDone-nAbstain)))

It would be nice to display the results graphically. For this a prediction variable is made. In addition, the results are merged with decent labels

ug <- unique(subset(r1,,Group))

lpreds <- lapply(list(g1,h1,i1,j1,k1),

    function(x) {

      predsg <- 

          as.data.frame(predict.glm(x,ug,type='response',se.fit=TRUE)[-3])

      predsg$Group <- ug$Group

      predsg$Q <- dimnames(x$model[[1]])[[2]][1]

      predsg 

    })

preds <- do.call(rbind,lpreds)

rel <- data.frame(

    Q=c("nNotIn",   "nNoVote",  "nYES",     "nNO",      "nAbstain"),

    Vote=factor(1:5,labels=c(

            'Not Present',

            'Not Voted',

            'Yes','No','Abstain')))

preds <- merge(preds,rel)

limits <- aes(ymax = fit + 2*se.fit, ymin=fit- 2* se.fit)

p1 <- ggplot(preds, aes(y=fit, x=Group,col=Vote))

p1 + geom_point(stat="identity") +

    geom_errorbar(limits,  width=0.25) +

    ylab('Proportion') +

    coord_flip()

One can see that each of the groups in general votes sooner Yes than No.

As I found the result a bit disappointing, I made a mosaic plot where distribution of results are next to each other. 

ag <- aggregate(subset(r1,,c(nYES,nNO,nAbstain,nNoVote,nNotIn)),

    by=list(Group=r1$Group),

    sum)

lag <- reshape(ag,direction='long',

    idvar='Group',

    times=names(ag[-1]),

    timevar='Q',

    v.names='Counts',

    varying=list(names(ag[-1])))

lag <- merge(lag,rel)

mosaicplot(xtabs(Counts ~ Group + Vote ,data=lag),

    color=rainbow(nlevels(lag$Group)),

    main='Votes',

    las=2,cex=.7)

By Party

At this point, I decided that the first plot would be interesting if displayed by party. However, there are far too many parties. So, to get a bit of order, the focus is on 'interesting' parties. Interesting in this case is anything not S&D, ADLE/ADLE and EPP because these are far too predictable. This leaves still quite some parties, so they have to be ordered by country too. Since there are a number of parties (e.g. independent) which are present in several countries, these to be labelled by country too. 

First a National.PartyF, which is ordered by country.

tween <- unique(subset(r1,,c(Country,National.Party)))

tween$National.PartyC <- as.character(tween$National.Party)

selection <- tween$National.Party %in% c('Independent','Labour Party','Parti Socialiste','Partido Popular')

tween$National.PartyC[selection] <- 

    paste(tween$National.PartyC[selection],tween$Country[selection])

tween <- tween[order(tween$Country,tween$National.Party),]

tween$National.PartyF <- factor(tween$National.PartyC,

    levels=tween$National.PartyC,

    labels=tween$National.PartyC)

r2 <- merge(r1,tween)

The analysis, stripped down to minimum.

test2 <- function(response) {

  glm(response ~ National.PartyF,

      data=r2,family=binomial)

}

g2 <- test2(with(r2,

        cbind(

            nNotIn,

            nIn=TotalPossible-nNotIn)))

h2 <- test2(with(r2,

        cbind(

            nNoVote,

            nVote=TotalPossible-nNotIn-nNoVote)))

i2 <- test2(with(r2,

        cbind(

            nYES,

            nNotYES=TotalDone-nYES)))

j2 <- test2(with(r2,

        cbind(

            nNO,

            nNotNO=TotalDone-nNO)))

k2 <- test2(with(r2,

        cbind(

            nAbstain,

            nNotAb=TotalDone-nAbstain)))

Predicting

ug2 <- unique(subset(r2,,National.PartyF))

lpreds2 <- lapply(list(g2,h2,i2,j2,k2),

    function(x) {

      predsg <- 

          as.data.frame(predict.glm(x,ug2,type='response',se.fit=TRUE)[-3])

      predsg$National.PartyF <- ug2$National.PartyF

      predsg$Q <- dimnames(x$model[[1]])[[2]][1]

      predsg 

    })

preds2 <- do.call(rbind,lpreds2)

preds2 <- merge(preds2,rel)

Adding supporting variables.  Shortening a very long name.

preds2 <- merge(preds2,rel)

preds2 <- merge(preds2,unique(subset(r2,,c(National.PartyF,Country,Group))))

levels(preds2$National.PartyF)[

    levels(preds2$National.PartyF)==

        "People for Real, Open and United Democracy / Conservative Party for Democracy and Success"

    ]  <- "People for Real, Open and United Democracy / ..."

The plot is still a bit large. In the top the UK has a number of parties which do know where the No button is. As do Dutch 'Partij voor de Vrijheid' and French 'Mouvement pour la France'. In contrast, 'Front National' is probably better described as Abstained. Do not think this is a right wing thing, the 'Communist Party of Greece' likes the No button too, though not as much as the parties mentioned earlier.

p1 <- ggplot(preds2[!(preds2$Group %in% c('S&D','EPP','ALDE/ADLE')),], 

    aes(y=fit, x=National.PartyF,col=Vote))

png('long2.png',width=800,height=800)

p1 + geom_point(stat="identity") +

    geom_errorbar(limits,  width=0.25) +

    ylab('Proportion') + 

    coord_flip() +

    xlab('National Party') +

    theme(legend.position="bottom")

Other noticeable

Anything over 0.3 which was neither Yes nor No.

preds2[preds2$fit>.3 & !(preds2$Vote %in% c('Yes','No')),c(1,3,5,6,7)]

                     National.PartyF       fit        Vote   Country     Group

136                    Darbo partija 0.5702479 Not Present Lithuania ALDE/ADLE

138                    Darbo partija 0.3653846   Not Voted Lithuania ALDE/ADLE

246 Freiheitliche Partei Österreichs 0.3248408     Abstain   Austria        NI

257                   Front national 0.3598585     Abstain    France        NI

396  JOBBIK MAGYARORSZÁGÉRT MOZGALOM 0.3164147     Abstain   Hungary        NI

522         Mouvement pour la France 0.3060686   Not Voted    France       EFD

To note: 

Darbo partija is the Labour Party

Jobbik is Radical Nationalist

FPÖ and FN got often enough in the newspapers, so don't need a link. MPF is clearly anti EU just from the name.