election: German Parliament Election Data
In samplingbook: Survey Sampling Procedures
Description
Usage
Format
Details
Source
References
Examples
Description
Data frame with number of citizens eligible to vote and results of the elections in 2002 and 2005 for the German Bundestag, the first chamber of the German
parliament.
Usage
1
Format
A data frame with 299 observations (corresponding to constituencies) on the following 13 variables.
statefactor, the 16 German federal states
eligible_02number of citizens eligible to vote in 2002
SPD_02a numeric vector, percentage for the Social Democrats SPD in 2002
UNION_02a numeric vector, percentage for the conservative Christian Democrats CDU/CSU in 2002
GREEN_02a numeric vector, percentage for the Greens in 2002
FDP_02a numeric vector, percentage for the Liberal Party FDP in 2002
LEFT_02a numeric vector, percentage for the Left Party PDS in 2002
eligible_05number of citizens eligible to vote in 2005
SPD_05a numeric vector, percentage for the Social Democrats SPD in 2005
UNION_05a numeric vector, percentage for the conservative Christian Democrats CDU/CSU in 2005
GREEN_05a numeric vector, percentage for the Greens in 2005
FDP_05a numeric vector, percentage for the Liberal Party FDP in 2005
LEFT_05a numeric vector, percentage for the Left Party in 2005
Details
German Federal Elections
Half of the Members of the German Bundestag are elected directly from Germany's 299 constituencies,
the other half one on the parties' land lists. Accordingly, each voter has two votes in the elections
to the German Bundestag. The first vote, allowing voters to elect their local representatives to the
Bundestag, decides which candidates are sent to Parliament from the constituencies.
The second vote is cast for a party list. And it is this second vote that determines the relative
strengths of the parties represented in the Bundestag. At least 598 Members of the German Bundestag
are elected in this way. In addition to this, there are certain circumstances in which some
candidates win what are known as 'overhang mandates' when the seats are being distributed.
The data set provides the percentage of second votes for each party, which determines the number of
seats each party gets in parliament. These percentages are calculated by the number of votes for a party divided by number
of valid votes.
Source
The data is provided by the R package flexclust.
References
Kauermann, Goeran/Kuechenhoff, Helmut (2010): Stichproben. Methoden und praktische Umsetzung mit R. Springer.
Homepage of the Bundestag: http://www.bundestag.de.
Friedrich Leisch. A Toolbox for K-Centroids Cluster Analysis. Computational Statistics and Data Analysis, 51 (2), 526-544, 2006.
Examples
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data(election)
summary(election)
# 1) Draw a simple sample of size n=20
n <- 20
set.seed(67396)
index <- sample(1:nrow(election), size=n)
sample1 <- election[index,]
Smean(sample1$SPD_02, N=nrow(election))
# true mean
mean(election$SPD_02)
# 2) Estimate sample size to forecast proportion of SPD in election of 2005
sample.size.prop(e=0.01, P=mean(election$SPD_02), N=Inf)
# 3) Usage of previous knowledge by model based estimation
# draw sample of size n = 20
N <- nrow(election)
set.seed(67396)
sample <- election[sort(sample(1:N, size=20)),]
# secondary information SPD in 2002
X.mean <- mean(election$SPD_02)
# forecast proportion of SPD in election of 2005
mbes(SPD_05 ~ SPD_02, data=sample, aux=X.mean, N=N, method='all')
# true value
Y.mean <- mean(election$SPD_05)
Y.mean
# Use a second predictor variable
X.mean2 <- c(mean(election$SPD_02),mean(election$GREEN_02))
# forecast proportion of SPD in election of 2005 with two predictors
mbes(SPD_05 ~ SPD_02+GREEN_02, data=sample, aux=X.mean2, N=N, method= 'regr')
Example output
Loading required package: pps
Loading required package: sampling
Loading required package: survey
Loading required package: grid
Loading required package: Matrix
Loading required package: survival
Attaching package: ‘survival’
The following objects are masked from ‘package:sampling’:
cluster, strata
Attaching package: ‘survey’
The following object is masked from ‘package:graphics’:
dotchart
state eligible_02 SPD_02 UNION_02
Nordrhein-Westfalen:64 Min. :152670 Min. :0.1785 Min. :0.1285
Bayern :45 1st Qu.:190152 1st Qu.:0.3337 1st Qu.:0.3075
Baden-Wuerttemberg :37 Median :206148 Median :0.3845 Median :0.3628
Niedersachsen :29 Mean :205461 Mean :0.3861 Mean :0.3831
Hessen :21 3rd Qu.:219907 3rd Qu.:0.4459 3rd Qu.:0.4342
Sachsen :17 Max. :249388 Max. :0.6171 Max. :0.7282
(Other) :86
GREEN_02 FDP_02 LEFT_02 eligible_05
Min. :0.02251 Min. :0.02489 Min. :0.003352 Min. :154154
1st Qu.:0.05734 1st Qu.:0.06001 1st Qu.:0.008415 1st Qu.:191819
Median :0.07638 Median :0.07428 Median :0.011201 Median :206345
Mean :0.08484 Mean :0.07342 Mean :0.041898 Mean :206924
3rd Qu.:0.10650 3rd Qu.:0.08601 3rd Qu.:0.019522 3rd Qu.:220944
Max. :0.25029 Max. :0.12420 Max. :0.293128 Max. :254100
SPD_05 UNION_05 GREEN_05 FDP_05
Min. :0.1885 Min. :0.1104 Min. :0.02619 Min. :0.04565
1st Qu.:0.2959 1st Qu.:0.2881 1st Qu.:0.05664 1st Qu.:0.08095
Median :0.3361 Median :0.3432 Median :0.07195 Median :0.09679
Mean :0.3427 Mean :0.3507 Mean :0.08060 Mean :0.09769
3rd Qu.:0.3883 3rd Qu.:0.4045 3rd Qu.:0.09818 3rd Qu.:0.11241
Max. :0.5586 Max. :0.6048 Max. :0.22769 Max. :0.16630
LEFT_05
Min. :0.02275
1st Qu.:0.03866
Median :0.04888
Mean :0.08870
3rd Qu.:0.07176
Max. :0.35536
Smean object: Sample mean estimate
With finite population correction: N=299
Mean estimate: 0.3515
Standard error: 0.0165
95% confidence interval: [0.3192,0.3839]
[1] 0.3861344
sample.size.prop object: Sample size for proportion estimate
Without finite population correction: N=Inf, precision e=0.01 and expected proportion P=0.3861
Sample size needed: 9106
mbes object: Model Based Estimation of Population Mean
Population size N = 299, sample size n = 20
Values for auxiliary variable:
X.mean.1 = 0.3861, x.mean.1 = 0.3515
----------------------------------------------------------------
Simple Estimate
Mean estimate: 0.3009
Standard error: 0.0119
95% confidence interval [0.2775,0.3242]
----------------------------------------------------------------
Difference Estimate
Mean estimate: 0.3355
Standard error: 0.0088
95% confidence interval [0.3183,0.3526]
----------------------------------------------------------------
Ratio Estimate
Mean estimate: 0.3305
Standard error: 0.0072
95% confidence interval [0.3163,0.3447]
----------------------------------------------------------------
Linear Regression Estimate
Mean estimate: 0.3223
Standard error: 0.0063
95% confidence interval [0.31,0.3346]
----------------------------------------------------------------
Linear Regression Model:
Call:
lm(formula = formula, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.054727 -0.022938 -0.003066 0.027230 0.037138
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.08290 0.03137 2.643 0.0165 *
SPD_02 0.62004 0.08729 7.103 1.28e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.02908 on 18 degrees of freedom
Multiple R-squared: 0.737, Adjusted R-squared: 0.7224
F-statistic: 50.45 on 1 and 18 DF, p-value: 1.277e-06
[1] 0.3426949
mbes object: Model Based Estimation of Population Mean
Population size N = 299, sample size n = 20
Values for auxiliary variable:
X.mean.1 = 0.3861, x.mean.1 = 0.3515
X.mean.2 = 0.0848, x.mean.2 = 0.07
----------------------------------------------------------------
Linear Regression Estimate
Mean estimate: 0.3291
Standard error: 0.0051
95% confidence interval [0.3191,0.3391]
----------------------------------------------------------------
Linear Regression Model:
Call:
lm(formula = formula, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.037753 -0.016922 -0.004229 0.016320 0.048000
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.04326 0.02843 1.521 0.14652
SPD_02 0.66001 0.07223 9.138 5.71e-08 ***
GREEN_02 0.36537 0.11489 3.180 0.00547 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.0237 on 17 degrees of freedom
Multiple R-squared: 0.8351, Adjusted R-squared: 0.8157
F-statistic: 43.06 on 2 and 17 DF, p-value: 2.217e-07
samplingbook documentation built on April 3, 2021, 1:06 a.m.
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Description Usage Format Details Source References Examples
Description
Data frame with number of citizens eligible to vote and results of the elections in 2002 and 2005 for the German Bundestag, the first chamber of the German parliament.
Usage
1 |
Format
A data frame with 299 observations (corresponding to constituencies) on the following 13 variables.
statefactor, the 16 German federal states
eligible_02number of citizens eligible to vote in 2002
SPD_02a numeric vector, percentage for the Social Democrats SPD in 2002
UNION_02a numeric vector, percentage for the conservative Christian Democrats CDU/CSU in 2002
GREEN_02a numeric vector, percentage for the Greens in 2002
FDP_02a numeric vector, percentage for the Liberal Party FDP in 2002
LEFT_02a numeric vector, percentage for the Left Party PDS in 2002
eligible_05number of citizens eligible to vote in 2005
SPD_05a numeric vector, percentage for the Social Democrats SPD in 2005
UNION_05a numeric vector, percentage for the conservative Christian Democrats CDU/CSU in 2005
GREEN_05a numeric vector, percentage for the Greens in 2005
FDP_05a numeric vector, percentage for the Liberal Party FDP in 2005
LEFT_05a numeric vector, percentage for the Left Party in 2005
Details
German Federal Elections
Half of the Members of the German Bundestag are elected directly from Germany's 299 constituencies, the other half one on the parties' land lists. Accordingly, each voter has two votes in the elections to the German Bundestag. The first vote, allowing voters to elect their local representatives to the Bundestag, decides which candidates are sent to Parliament from the constituencies. The second vote is cast for a party list. And it is this second vote that determines the relative strengths of the parties represented in the Bundestag. At least 598 Members of the German Bundestag are elected in this way. In addition to this, there are certain circumstances in which some candidates win what are known as 'overhang mandates' when the seats are being distributed.
The data set provides the percentage of second votes for each party, which determines the number of seats each party gets in parliament. These percentages are calculated by the number of votes for a party divided by number of valid votes.
Source
The data is provided by the R package flexclust.
References
Kauermann, Goeran/Kuechenhoff, Helmut (2010): Stichproben. Methoden und praktische Umsetzung mit R. Springer.
Homepage of the Bundestag: http://www.bundestag.de.
Friedrich Leisch. A Toolbox for K-Centroids Cluster Analysis. Computational Statistics and Data Analysis, 51 (2), 526-544, 2006.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | data(election)
summary(election)
# 1) Draw a simple sample of size n=20
n <- 20
set.seed(67396)
index <- sample(1:nrow(election), size=n)
sample1 <- election[index,]
Smean(sample1$SPD_02, N=nrow(election))
# true mean
mean(election$SPD_02)
# 2) Estimate sample size to forecast proportion of SPD in election of 2005
sample.size.prop(e=0.01, P=mean(election$SPD_02), N=Inf)
# 3) Usage of previous knowledge by model based estimation
# draw sample of size n = 20
N <- nrow(election)
set.seed(67396)
sample <- election[sort(sample(1:N, size=20)),]
# secondary information SPD in 2002
X.mean <- mean(election$SPD_02)
# forecast proportion of SPD in election of 2005
mbes(SPD_05 ~ SPD_02, data=sample, aux=X.mean, N=N, method='all')
# true value
Y.mean <- mean(election$SPD_05)
Y.mean
# Use a second predictor variable
X.mean2 <- c(mean(election$SPD_02),mean(election$GREEN_02))
# forecast proportion of SPD in election of 2005 with two predictors
mbes(SPD_05 ~ SPD_02+GREEN_02, data=sample, aux=X.mean2, N=N, method= 'regr')
|
Example output
Loading required package: pps
Loading required package: sampling
Loading required package: survey
Loading required package: grid
Loading required package: Matrix
Loading required package: survival
Attaching package: ‘survival’
The following objects are masked from ‘package:sampling’:
cluster, strata
Attaching package: ‘survey’
The following object is masked from ‘package:graphics’:
dotchart
state eligible_02 SPD_02 UNION_02
Nordrhein-Westfalen:64 Min. :152670 Min. :0.1785 Min. :0.1285
Bayern :45 1st Qu.:190152 1st Qu.:0.3337 1st Qu.:0.3075
Baden-Wuerttemberg :37 Median :206148 Median :0.3845 Median :0.3628
Niedersachsen :29 Mean :205461 Mean :0.3861 Mean :0.3831
Hessen :21 3rd Qu.:219907 3rd Qu.:0.4459 3rd Qu.:0.4342
Sachsen :17 Max. :249388 Max. :0.6171 Max. :0.7282
(Other) :86
GREEN_02 FDP_02 LEFT_02 eligible_05
Min. :0.02251 Min. :0.02489 Min. :0.003352 Min. :154154
1st Qu.:0.05734 1st Qu.:0.06001 1st Qu.:0.008415 1st Qu.:191819
Median :0.07638 Median :0.07428 Median :0.011201 Median :206345
Mean :0.08484 Mean :0.07342 Mean :0.041898 Mean :206924
3rd Qu.:0.10650 3rd Qu.:0.08601 3rd Qu.:0.019522 3rd Qu.:220944
Max. :0.25029 Max. :0.12420 Max. :0.293128 Max. :254100
SPD_05 UNION_05 GREEN_05 FDP_05
Min. :0.1885 Min. :0.1104 Min. :0.02619 Min. :0.04565
1st Qu.:0.2959 1st Qu.:0.2881 1st Qu.:0.05664 1st Qu.:0.08095
Median :0.3361 Median :0.3432 Median :0.07195 Median :0.09679
Mean :0.3427 Mean :0.3507 Mean :0.08060 Mean :0.09769
3rd Qu.:0.3883 3rd Qu.:0.4045 3rd Qu.:0.09818 3rd Qu.:0.11241
Max. :0.5586 Max. :0.6048 Max. :0.22769 Max. :0.16630
LEFT_05
Min. :0.02275
1st Qu.:0.03866
Median :0.04888
Mean :0.08870
3rd Qu.:0.07176
Max. :0.35536
Smean object: Sample mean estimate
With finite population correction: N=299
Mean estimate: 0.3515
Standard error: 0.0165
95% confidence interval: [0.3192,0.3839]
[1] 0.3861344
sample.size.prop object: Sample size for proportion estimate
Without finite population correction: N=Inf, precision e=0.01 and expected proportion P=0.3861
Sample size needed: 9106
mbes object: Model Based Estimation of Population Mean
Population size N = 299, sample size n = 20
Values for auxiliary variable:
X.mean.1 = 0.3861, x.mean.1 = 0.3515
----------------------------------------------------------------
Simple Estimate
Mean estimate: 0.3009
Standard error: 0.0119
95% confidence interval [0.2775,0.3242]
----------------------------------------------------------------
Difference Estimate
Mean estimate: 0.3355
Standard error: 0.0088
95% confidence interval [0.3183,0.3526]
----------------------------------------------------------------
Ratio Estimate
Mean estimate: 0.3305
Standard error: 0.0072
95% confidence interval [0.3163,0.3447]
----------------------------------------------------------------
Linear Regression Estimate
Mean estimate: 0.3223
Standard error: 0.0063
95% confidence interval [0.31,0.3346]
----------------------------------------------------------------
Linear Regression Model:
Call:
lm(formula = formula, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.054727 -0.022938 -0.003066 0.027230 0.037138
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.08290 0.03137 2.643 0.0165 *
SPD_02 0.62004 0.08729 7.103 1.28e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.02908 on 18 degrees of freedom
Multiple R-squared: 0.737, Adjusted R-squared: 0.7224
F-statistic: 50.45 on 1 and 18 DF, p-value: 1.277e-06
[1] 0.3426949
mbes object: Model Based Estimation of Population Mean
Population size N = 299, sample size n = 20
Values for auxiliary variable:
X.mean.1 = 0.3861, x.mean.1 = 0.3515
X.mean.2 = 0.0848, x.mean.2 = 0.07
----------------------------------------------------------------
Linear Regression Estimate
Mean estimate: 0.3291
Standard error: 0.0051
95% confidence interval [0.3191,0.3391]
----------------------------------------------------------------
Linear Regression Model:
Call:
lm(formula = formula, data = data)
Residuals:
Min 1Q Median 3Q Max
-0.037753 -0.016922 -0.004229 0.016320 0.048000
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.04326 0.02843 1.521 0.14652
SPD_02 0.66001 0.07223 9.138 5.71e-08 ***
GREEN_02 0.36537 0.11489 3.180 0.00547 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.0237 on 17 degrees of freedom
Multiple R-squared: 0.8351, Adjusted R-squared: 0.8157
F-statistic: 43.06 on 2 and 17 DF, p-value: 2.217e-07
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