Cheating and Looting in Japanese Electoral Politics
cheating.RdExtracted from the "cheating-replication.dta" data file with permission by the authors, Benjamin Nyblade and Steven Reed. The Stata data file provided by the authors included many constructed variables that have been omitted. Within R, these can be easily re-contructed by users.
Usage
data(cheating)Details
Special thanks to NyBlade and Reed for permission to repackage this data. Also special thanks to them for creating an especially transparent variable naming scheme.
The data set includes many columns for variables that can easily be re-constructed from the columns that are provided here. While Stata users might need to manually create 'dummy variables' and interactions, R users generally do not do that manually.
These variables from the original data set were omitted:
Dummy variables for the year variable: c("yrd1", "yrd2", ..., "yrd17", "yrd18")
Dummy variables for the ku variable: c("ku1", "ku2", ..., "ku141", "ku142")
Constructed product variables: c("actualratiosq", "viabsq", "viab_candcamp_divm", "viab_candothercamp_divm", "viabsq_candcamp_divm", "viabsq_candothercamp_divm", "absviab_candcamp", "absviab_candothercamp", "absviab_candcamp_divm", "absviab_candothercamp_divm", "viabsq_candcamp", "viabsq_candothercamp", "viab_candcamp", "viab_candothercamp", "candothercamp_divm", "candcamp_divm", "candcampminusm", "candothercampminusm", "predratiosq", "absviab")
Mean centered variables: constr2 <- c("viab_candcampminusm", "viab_candothercampminusm", "viabsq_candothercampminusm", "viabsq_candcampminusm")
In the end, we are left with these variables:
[1] "ku" [2] "prefecture" [3] "dist" [4] "year" [5] "yr" [6] "cdnr" [7] "jiban" [8] "cheating" [9] "looting" [10] "actualratio" [11] "viab" [12] "inc" [13] "cons" [14] "ur" [15] "newcand" [16] "jwins" [17] "cons_cwins" [18] "oth_cwins" [19] "camp" [20] "fleader" [21] "incablast" [22] "predratio" [23] "m" [24] "candcamp" [25] "candothercamp" [26] "kunocheat" [27] "kunoloot"
References
Benjamin Nyblade and Steven Reed, "Who Cheats? Who Loots? Political Competition and Corruption in Japan, 1947-1993." American Journal of Political Science 52(4): 926-41. October 2008.
Author
Paul E. Johnson pauljohn@ku.edu, on behalf of Benjamin Nyblade and Steven Reed
Examples
require(rockchalk)
data(cheating)
table1model2 <- glm(cheating ~ viab + I(viab^2) + inc + cons + ur
+ newcand + jwins + cons_cwins + oth_cwins, family = binomial(link
= "logit"), data = cheating)
predictOMatic(table1model2)
#> $viab
#> viab inc cons ur newcand jwins cons_cwins
#> 1 -0.5000000 0.5990585 0.5272481 2.538259 0.1176095 3.643262 1.826139
#> 2 -0.2150552 0.5990585 0.5272481 2.538259 0.1176095 3.643262 1.826139
#> 3 0.1410797 0.5990585 0.5272481 2.538259 0.1176095 3.643262 1.826139
#> 4 0.3884473 0.5990585 0.5272481 2.538259 0.1176095 3.643262 1.826139
#> 5 0.5000000 0.5990585 0.5272481 2.538259 0.1176095 3.643262 1.826139
#> oth_cwins fit
#> 1 0.9028964 0.005269946
#> 2 0.9028964 0.009585624
#> 3 0.9028964 0.012889333
#> 4 0.9028964 0.011809048
#> 5 0.9028964 0.010492609
#>
#> $inc
#> viab inc cons ur newcand jwins cons_cwins oth_cwins
#> 1 0.07121562 1 0.5272481 2.538259 0.1176095 3.643262 1.826139 0.9028964
#> 2 0.07121562 0 0.5272481 2.538259 0.1176095 3.643262 1.826139 0.9028964
#> fit
#> 1 0.01077975
#> 2 0.01605494
#>
#> $cons
#> viab inc cons ur newcand jwins cons_cwins oth_cwins
#> 1 0.07121562 0.5990585 1 2.538259 0.1176095 3.643262 1.826139 0.9028964
#> 2 0.07121562 0.5990585 0 2.538259 0.1176095 3.643262 1.826139 0.9028964
#> fit
#> 1 0.02469779
#> 2 0.00595655
#>
#> $ur
#> viab inc cons ur newcand jwins cons_cwins oth_cwins
#> 1 0.07121562 0.5990585 0.5272481 2 0.1176095 3.643262 1.826139 0.9028964
#> 2 0.07121562 0.5990585 0.5272481 3 0.1176095 3.643262 1.826139 0.9028964
#> 3 0.07121562 0.5990585 0.5272481 4 0.1176095 3.643262 1.826139 0.9028964
#> 4 0.07121562 0.5990585 0.5272481 1 0.1176095 3.643262 1.826139 0.9028964
#> fit
#> 1 0.011436713
#> 2 0.013790875
#> 3 0.016621475
#> 4 0.009480554
#>
#> $newcand
#> viab inc cons ur newcand jwins cons_cwins oth_cwins
#> 1 0.07121562 0.5990585 0.5272481 2.538259 0 3.643262 1.826139 0.9028964
#> 2 0.07121562 0.5990585 0.5272481 2.538259 1 3.643262 1.826139 0.9028964
#> fit
#> 1 0.01290339
#> 2 0.01089724
#>
#> $jwins
#> viab inc cons ur newcand jwins cons_cwins oth_cwins
#> 1 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 0 1.826139 0.9028964
#> 2 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 1 1.826139 0.9028964
#> 3 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 2 1.826139 0.9028964
#> 4 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 6 1.826139 0.9028964
#> 5 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 17 1.826139 0.9028964
#> fit
#> 1 0.014222225
#> 2 0.013772359
#> 3 0.013336529
#> 4 0.011725246
#> 5 0.008221063
#>
#> $cons_cwins
#> viab inc cons ur newcand jwins cons_cwins
#> 1 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 3.643262 0
#> 2 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 3.643262 0
#> 3 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 3.643262 0
#> 4 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 3.643262 3
#> 5 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 3.643262 25
#> oth_cwins fit
#> 1 0.9028964 0.014468533
#> 2 0.9028964 0.014468533
#> 3 0.9028964 0.014468533
#> 4 0.9028964 0.011601730
#> 5 0.9028964 0.002270248
#>
#> $oth_cwins
#> viab inc cons ur newcand jwins cons_cwins
#> 1 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 3.643262 1.826139
#> 2 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 3.643262 1.826139
#> 3 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 3.643262 1.826139
#> 4 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 3.643262 1.826139
#> 5 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 3.643262 1.826139
#> oth_cwins fit
#> 1 0 0.0168034217
#> 2 0 0.0168034217
#> 3 0 0.0168034217
#> 4 1 0.0122684352
#> 5 14 0.0001959623
#>
predictOMatic(table1model2, interval = "confidence")
#> rockchalk:::predCI: model's predict method does not return an interval.
#> We will improvize with a Wald type approximation to the confidence interval
#> rockchalk:::predCI: model's predict method does not return an interval.
#> We will improvize with a Wald type approximation to the confidence interval
#> rockchalk:::predCI: model's predict method does not return an interval.
#> We will improvize with a Wald type approximation to the confidence interval
#> rockchalk:::predCI: model's predict method does not return an interval.
#> We will improvize with a Wald type approximation to the confidence interval
#> rockchalk:::predCI: model's predict method does not return an interval.
#> We will improvize with a Wald type approximation to the confidence interval
#> rockchalk:::predCI: model's predict method does not return an interval.
#> We will improvize with a Wald type approximation to the confidence interval
#> rockchalk:::predCI: model's predict method does not return an interval.
#> We will improvize with a Wald type approximation to the confidence interval
#> rockchalk:::predCI: model's predict method does not return an interval.
#> We will improvize with a Wald type approximation to the confidence interval
#> $viab
#> viab inc cons ur newcand jwins cons_cwins
#> 1 -0.5000000 0.5990585 0.5272481 2.538259 0.1176095 3.643262 1.826139
#> 2 -0.2150552 0.5990585 0.5272481 2.538259 0.1176095 3.643262 1.826139
#> 3 0.1410797 0.5990585 0.5272481 2.538259 0.1176095 3.643262 1.826139
#> 4 0.3884473 0.5990585 0.5272481 2.538259 0.1176095 3.643262 1.826139
#> 5 0.5000000 0.5990585 0.5272481 2.538259 0.1176095 3.643262 1.826139
#> oth_cwins fit lwr upr
#> 1 0.9028964 0.005269946 0.002959313 0.009367772
#> 2 0.9028964 0.009585624 0.006928724 0.013247754
#> 3 0.9028964 0.012889333 0.009824098 0.016894640
#> 4 0.9028964 0.011809048 0.008716433 0.015981242
#> 5 0.9028964 0.010492609 0.007034724 0.015623456
#>
#> $inc
#> viab inc cons ur newcand jwins cons_cwins oth_cwins
#> 1 0.07121562 1 0.5272481 2.538259 0.1176095 3.643262 1.826139 0.9028964
#> 2 0.07121562 0 0.5272481 2.538259 0.1176095 3.643262 1.826139 0.9028964
#> fit lwr upr
#> 1 0.01077975 0.007746345 0.01498307
#> 2 0.01605494 0.011015395 0.02334564
#>
#> $cons
#> viab inc cons ur newcand jwins cons_cwins oth_cwins
#> 1 0.07121562 0.5990585 1 2.538259 0.1176095 3.643262 1.826139 0.9028964
#> 2 0.07121562 0.5990585 0 2.538259 0.1176095 3.643262 1.826139 0.9028964
#> fit lwr upr
#> 1 0.02469779 0.018032061 0.033742888
#> 2 0.00595655 0.003816553 0.009285292
#>
#> $ur
#> viab inc cons ur newcand jwins cons_cwins oth_cwins
#> 1 0.07121562 0.5990585 0.5272481 2 0.1176095 3.643262 1.826139 0.9028964
#> 2 0.07121562 0.5990585 0.5272481 3 0.1176095 3.643262 1.826139 0.9028964
#> 3 0.07121562 0.5990585 0.5272481 4 0.1176095 3.643262 1.826139 0.9028964
#> 4 0.07121562 0.5990585 0.5272481 1 0.1176095 3.643262 1.826139 0.9028964
#> fit lwr upr
#> 1 0.011436713 0.008483905 0.01540128
#> 2 0.013790875 0.010439343 0.01819862
#> 3 0.016621475 0.012034408 0.02291641
#> 4 0.009480554 0.006523356 0.01375976
#>
#> $newcand
#> viab inc cons ur newcand jwins cons_cwins oth_cwins
#> 1 0.07121562 0.5990585 0.5272481 2.538259 0 3.643262 1.826139 0.9028964
#> 2 0.07121562 0.5990585 0.5272481 2.538259 1 3.643262 1.826139 0.9028964
#> fit lwr upr
#> 1 0.01290339 0.009712292 0.01712484
#> 2 0.01089724 0.006002356 0.01970474
#>
#> $jwins
#> viab inc cons ur newcand jwins cons_cwins oth_cwins
#> 1 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 0 1.826139 0.9028964
#> 2 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 1 1.826139 0.9028964
#> 3 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 2 1.826139 0.9028964
#> 4 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 6 1.826139 0.9028964
#> 5 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 17 1.826139 0.9028964
#> fit lwr upr
#> 1 0.014222225 0.010179367 0.01983857
#> 2 0.013772359 0.010113299 0.01873024
#> 3 0.013336529 0.009975115 0.01781030
#> 4 0.011725246 0.008614631 0.01594100
#> 5 0.008221063 0.003834457 0.01753759
#>
#> $cons_cwins
#> viab inc cons ur newcand jwins cons_cwins
#> 1 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 3.643262 0
#> 2 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 3.643262 0
#> 3 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 3.643262 0
#> 4 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 3.643262 3
#> 5 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 3.643262 25
#> oth_cwins fit lwr upr
#> 1 0.9028964 0.014468533 0.0107887224 0.01937886
#> 2 0.9028964 0.014468533 0.0107887224 0.01937886
#> 3 0.9028964 0.014468533 0.0107887224 0.01937886
#> 4 0.9028964 0.011601730 0.0085679384 0.01569274
#> 5 0.9028964 0.002270248 0.0003851442 0.01325968
#>
#> $oth_cwins
#> viab inc cons ur newcand jwins cons_cwins
#> 1 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 3.643262 1.826139
#> 2 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 3.643262 1.826139
#> 3 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 3.643262 1.826139
#> 4 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 3.643262 1.826139
#> 5 0.07121562 0.5990585 0.5272481 2.538259 0.1176095 3.643262 1.826139
#> oth_cwins fit lwr upr
#> 1 0 0.0168034217 1.240634e-02 0.02272307
#> 2 0 0.0168034217 1.240634e-02 0.02272307
#> 3 0 0.0168034217 1.240634e-02 0.02272307
#> 4 1 0.0122684352 9.180701e-03 0.01637750
#> 5 14 0.0001959623 4.493889e-06 0.00847606
#>
## The publication used "rare events logistic", which I'm not bothering
## with here because I don't want to invoke additional imported packages.
## But the ordinary logit results are proof of concept.