electors
mclogit
0.8.6.2
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))))
Iteration 1 - deviance = 1054.511 - criterion = 0.1598497
Iteration 2 - deviance = 923.1626 - criterion = 0.02666473
Iteration 3 - deviance = 890.3113 - criterion = 0.006530011
Iteration 4 - deviance = 883.0567 - criterion = 0.0005723444
Iteration 5 - deviance = 881.4344 - criterion = 1.387329e-05
Iteration 6 - deviance = 881.2041 - criterion = 1.394381e-07
Iteration 7 - deviance = 881.1809 - criterion = 1.04388e-09
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 -0.12603 0.18576 -0.678 0.497
welfare 2.01955 0.29136 6.932 4.16e-12 ***
auth 0.11409 0.15908 0.717 0.473
econ.left:classnew.middle -1.81703 0.09859 -18.430 < 2e-16 ***
econ.left:classold.middle -3.13862 0.15780 -19.890 < 2e-16 ***
classnew.middle:welfare -0.89578 0.06573 -13.628 < 2e-16 ***
classold.middle:welfare -1.47904 0.13451 -10.996 < 2e-16 ***
classnew.middle:auth -1.43391 0.04855 -29.535 < 2e-16 ***
classold.middle:auth 1.44109 0.05883 24.494 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Co-)Variances:
Grouping level: 1
Estimate Std.Err.
(Intercept) 3.066 0.9423
Null Deviance: 80580
Residual Deviance: 881.2
Number of Fisher Scoring iterations: 7
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
})))
Iteration 1 - deviance = 1055.013 - criterion = 0.1599458
Iteration 2 - deviance = 923.0042 - criterion = 0.02667377
Iteration 3 - deviance = 890.0156 - criterion = 0.006569445
Iteration 4 - deviance = 882.7442 - criterion = 0.0005787939
Iteration 5 - deviance = 881.116 - criterion = 1.407264e-05
Iteration 6 - deviance = 880.8845 - criterion = 1.415608e-07
Iteration 7 - deviance = 880.8611 - criterion = 1.059383e-09
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 -0.08426 0.28175 -0.299 0.765
welfare 2.01978 0.29137 6.932 4.15e-12 ***
auth 0.11424 0.15909 0.718 0.473
econ.left:classnew.middle -1.84956 0.12678 -14.588 < 2e-16 ***
econ.left:classold.middle -3.21621 0.21256 -15.131 < 2e-16 ***
econ.left:time -0.08023 0.40734 -0.197 0.844
classnew.middle:welfare -0.89582 0.06573 -13.628 < 2e-16 ***
classold.middle:welfare -1.47946 0.13451 -10.999 < 2e-16 ***
classnew.middle:auth -1.43387 0.04855 -29.533 < 2e-16 ***
classold.middle:auth 1.44101 0.05883 24.495 < 2e-16 ***
econ.left:classnew.middle:time 0.06208 0.15046 0.413 0.680
econ.left:classold.middle:time 0.14858 0.26950 0.551 0.581
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Co-)Variances:
Grouping level: 1
Estimate Std.Err.
(Intercept) 3.066 0.9423
Null Deviance: 80580
Residual Deviance: 880.9
Number of Fisher Scoring iterations: 7
Number of observations: 37500
## End(Do not test)
