electors mclogit 0.6.1

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)