Class, Party Position, and Electoral Choice

Description

This is an artificial data set on electoral choice as influenced by class and party positions.

Usage

data(electors)

Examples

data(electors)
summary(mclogit(
 cbind(Freq,interaction(time,class))~econ.left+welfare+auth,
 data=electors))
Iteration 1 - Deviance = 85051.49
Iteration 2 - Deviance = 76759.94
Iteration 3 - Deviance = 74896.56
Iteration 4 - Deviance = 74890.9
Iteration 5 - Deviance = 74890.9
converged

Call:
mclogit(formula = cbind(Freq, interaction(time, class)) ~ econ.left +
    welfare + auth, data = electors)

           Estimate Std. Error z value Pr(>|z|)
econ.left -0.507265   0.007495 -67.679  < 2e-16 ***
welfare    0.564650   0.010700  52.769  < 2e-16 ***
auth       0.030305   0.005749   5.271 1.36e-07 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Null Deviance:     80580
Residual Deviance: 74890
Number of Fisher Scoring iterations:  5
Number of observations:  37500
summary(mclogit(
 cbind(Freq,interaction(time,class))~econ.left/class+welfare/class+auth/class,
 data=electors))
Iteration 1 - Deviance = 7377.939
Iteration 2 - Deviance = 4589.544
Iteration 3 - Deviance = 4293.485
Iteration 4 - Deviance = 4277.887
Iteration 5 - Deviance = 4277.808
Iteration 6 - Deviance = 4277.808
converged

Call:
mclogit(formula = cbind(Freq, interaction(time, class)) ~ econ.left/class +
    welfare/class + auth/class, data = electors)

                          Estimate Std. Error z value Pr(>|z|)
econ.left                 -0.77851    0.02312 -33.671  < 2e-16 ***
welfare                    3.43776    0.03170 108.431  < 2e-16 ***
auth                      -0.13740    0.03608  -3.808  0.00014 ***
econ.left:classnew.middle  0.44546    0.02588  17.212  < 2e-16 ***
econ.left:classold.middle -0.44082    0.10387  -4.244  2.2e-05 ***
classnew.middle:welfare   -3.12917    0.03696 -84.659  < 2e-16 ***
classold.middle:welfare   -5.27438    0.07286 -72.393  < 2e-16 ***
classnew.middle:auth      -0.86676    0.03947 -21.957  < 2e-16 ***
classold.middle:auth       1.39435    0.05615  24.831  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Null Deviance:     80580
Residual Deviance: 4278
Number of Fisher Scoring iterations:  6
Number of observations:  37500
summary(mclogit(
 cbind(Freq,interaction(time,class))~econ.left/class+welfare/class+auth/class,
 random=~1|party.time,
 data=within(electors,party.time<-interaction(party,time))))
Fitting plain conditional logit to obtain starting values
Iteration 1 - Deviance = 7377.939
Iteration 2 - Deviance = 4589.544
Iteration 3 - Deviance = 4293.485
Iteration 4 - Deviance = 4277.887
Iteration 5 - Deviance = 4277.808
Iteration 6 - Deviance = 4277.808
converged
Fitting random effects/random coefficients model
Iteration 1 - Deviance = 1876.788
Iteration 2 - Deviance = 1212.004
Iteration 3 - Deviance = 1009.8
Iteration 4 - Deviance = 958.7431
Iteration 5 - Deviance = 949.4332
Iteration 6 - Deviance = 948.1453
Iteration 7 - Deviance = 947.9013
Iteration 8 - Deviance = 947.8442
Iteration 9 - Deviance = 947.8329
Iteration 10 - Deviance = 947.8308
Iteration 11 - Deviance = 947.8305
Iteration 12 - Deviance = 947.8304
Iteration 13 - Deviance = 947.8304
Iteration 14 - Deviance = 947.8304
converged

Call:
mclogit(formula = cbind(Freq, interaction(time, class)) ~ econ.left/class +
welfare/class + auth/class, data = within(electors, party.time <-
  interaction(party,
    time)), random = ~1 | party.time)

Coefficents:
                          Estimate Std. Error z value Pr(>|z|)
econ.left                 -0.17223    0.13800  -1.248    0.212
welfare                    2.05402    0.21441   9.580   <2e-16 ***
auth                       0.08170    0.11820   0.691    0.489
econ.left:classnew.middle -1.66937    0.08804 -18.961   <2e-16 ***
econ.left:classold.middle -2.97243    0.14941 -19.894   <2e-16 ***
classnew.middle:welfare   -0.98925    0.06088 -16.248   <2e-16 ***
classold.middle:welfare   -1.61549    0.12869 -12.553   <2e-16 ***
classnew.middle:auth      -1.39210    0.04679 -29.752   <2e-16 ***
classold.middle:auth       1.45677    0.05817  25.044   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Co-)Variances:

Grouping level: party.time
            Estimate    Std. Error
            (Intercept) (Intercept)
(Intercept)      1.6343      0.1484

Null Deviance:     80580
Residual Deviance: 947.8
Number of Fisher Scoring iterations:  14
Number of observations:  37500
## Do not test:
summary(mclogit(
 cbind(Freq,interaction(time,class))~econ.left/(class*time)+welfare/class+auth/class,
 random=~1|party.time,
 data=within(electors,{
       party.time <-interaction(party,time)
       econ.left.sq <- (econ.left-mean(econ.left))^2
       })))
Fitting plain conditional logit to obtain starting values
Iteration 1 - Deviance = 7376.76
Iteration 2 - Deviance = 4587.134
Iteration 3 - Deviance = 4290.9
Iteration 4 - Deviance = 4275.276
Iteration 5 - Deviance = 4275.197
Iteration 6 - Deviance = 4275.197
converged
Fitting random effects/random coefficients model
Iteration 1 - Deviance = 1875.499
Iteration 2 - Deviance = 1212.373
Iteration 3 - Deviance = 1009.689
Iteration 4 - Deviance = 958.456
Iteration 5 - Deviance = 949.1018
Iteration 6 - Deviance = 947.8076
Iteration 7 - Deviance = 947.5627
Iteration 8 - Deviance = 947.5055
Iteration 9 - Deviance = 947.4941
Iteration 10 - Deviance = 947.492
Iteration 11 - Deviance = 947.4916
Iteration 12 - Deviance = 947.4916
Iteration 13 - Deviance = 947.4916
Iteration 14 - Deviance = 947.4916
converged

Call:
mclogit(formula = cbind(Freq, interaction(time, class)) ~ econ.left/(class *
    time) + welfare/class + auth/class, data = within(electors,
    {
        party.time <- interaction(party, time)
        econ.left.sq <- (econ.left - mean(econ.left))^2
    }), random = ~1 | party.time)

Coefficents:
                               Estimate Std. Error z value Pr(>|z|)
econ.left                      -0.13174    0.21015  -0.627    0.531
welfare                         2.05428    0.21437   9.583   <2e-16 ***
auth                            0.08182    0.11818   0.692    0.489
econ.left:classnew.middle      -1.70093    0.11664 -14.583   <2e-16 ***
econ.left:classold.middle      -3.04916    0.20380 -14.962   <2e-16 ***
econ.left:time                 -0.07790    0.30482  -0.256    0.798
classnew.middle:welfare        -0.98939    0.06088 -16.251   <2e-16 ***
classold.middle:welfare        -1.61605    0.12869 -12.558   <2e-16 ***
classnew.middle:auth           -1.39202    0.04679 -29.750   <2e-16 ***
classold.middle:auth            1.45670    0.05816  25.045   <2e-16 ***
econ.left:classnew.middle:time  0.06055    0.14470   0.418    0.676
econ.left:classold.middle:time  0.14727    0.26257   0.561    0.575
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Co-)Variances:

Grouping level: party.time
            Estimate    Std. Error
            (Intercept) (Intercept)
(Intercept)      1.6337      0.1484

Null Deviance:     80580
Residual Deviance: 947.5
Number of Fisher Scoring iterations:  14
Number of observations:  37500
## End(Do not test)