voting: Data on Social Mobility and the Labour Vote

Description Usage Format Source Examples

Description

Voting data from the 1987 British general election, cross-classified by the class of the head of household and the class of their father.

Usage

1

Format

A data frame with 25 observations on the following 4 variables.

percentage

the percentage of the cell voting Labour.

total

the cell count.

origin

a factor describing the father's class with levels 1:5.

destination

a factor describing the head of household's class with levels 1:5.

Source

Clifford, P. and Heath, A. F. (1993) The Political Consequences of Social Mobility. J. Roy. Stat. Soc. A, 156(1), 51-61.

Examples

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### Examples from Clifford and Heath paper
### (Results differ slightly - possible transcription error in
### published data?)
set.seed(1)

## reconstruct counts voting Labour/non-Labour
count <- with(voting, percentage/100 * total)
yvar <- cbind(count, voting$total - count)

## fit diagonal reference model with constant weights
classMobility <- gnm(yvar ~ -1 + Dref(origin, destination), 
                     family = binomial, data = voting)
DrefWeights(classMobility)

## create factors indicating movement in and out of salariat (class 1)
upward <- with(voting, origin != 1 & destination == 1)
downward <- with(voting, origin == 1 & destination != 1)

## fit separate weights for the "socially mobile" groups
socialMobility <- gnm(yvar ~ -1 + Dref(origin, destination,
                                       delta = ~ 1 + downward + upward),
                      family = binomial, data = voting)
DrefWeights(socialMobility)

## fit separate weights for downwardly mobile groups only
downwardMobility <- gnm(yvar ~ -1 + Dref(origin, destination,
                                         delta = ~ 1 + downward),
                        family = binomial, data = voting)
DrefWeights(downwardMobility)

Example output

Initialising
Running main iterations........
Done
Refitting with parameters of first Dref weight constrained to zero
$origin
    weight         se 
0.43724694 0.03996404 

$destination
    weight         se 
0.56275306 0.03996404 

Initialising
Running main iterations.........
Done
Refitting with parameters of first Dref weight constrained to zero
$origin
  downward upward    weight         se
1    FALSE  FALSE 0.4044959 0.05918141
2     TRUE  FALSE 0.6044393 0.12371032
3    FALSE   TRUE 0.3900792 0.08134359

$destination
  downward upward    weight         se
1    FALSE  FALSE 0.5955041 0.05918141
2     TRUE  FALSE 0.3955607 0.12371032
3    FALSE   TRUE 0.6099208 0.08134359

Initialising
Running main iterations........
Done
Refitting with parameters of first Dref weight constrained to zero
$origin
  downward    weight         se
1    FALSE 0.3992031 0.04750643
2     TRUE 0.5991570 0.11951340

$destination
  downward    weight         se
1    FALSE 0.6007969 0.04750643
2     TRUE 0.4008430 0.11951340

gnm documentation built on May 29, 2017, 2:22 p.m.