Description Usage Format Details Source References Examples
Yamaguchi (1987) presented this three-way frequency table, cross-classifying occupational categories of sons and fathers in the United States, United Kingdom and Japan. This data set has become a classic for models comparing two-way mobility tables across layers corresponding to countries, groups or time (e.g., Goodman and Hout, 1998; Xie, 1992).
The US data were derived from the 1973 OCG-II survey; those for the UK from the 1972 Oxford Social Mobility Survey; those for Japan came from the 1975 Social Stratification and Mobility survey. They pertain to men aged 20-64.
1 |
A frequency data frame with 75 observations on the following 4 variables. The total sample size is 28887.
Son
a factor with levels UpNM
LoNM
UpM
LoM
Farm
Father
a factor with levels UpNM
LoNM
UpM
LoM
Farm
Country
a factor with levels US
UK
Japan
Freq
a numeric vector
Five status categories – upper and lower
nonmanuals (UpNM
, LoNM
),
upper and lower manuals (UpM
, LoM
),
and Farm
) are used for both fathers' occupations and
sons' occupations.
Upper nonmanuals are professionals, managers, and officials; lower nonmanuals are proprietors, sales workers, and clerical workers; upper manuals are skilled workers; lower manuals are semi-skilled and unskilled nonfarm workers; and farm workers are farmers and farm laborers.
Some of the models from Xie (1992), Table 1, are fit in demo(yamaguchi-xie)
.
Yamaguchi, K. (1987). Models for comparing mobility tables: toward parsimony and substance, American Sociological Review, vol. 52 (Aug.), 482-494, Table 1
Goodman, L. A. and Hout, M. (1998). Statistical Methods and Graphical Displays for Analyzing How the Association Between Two Qualitative Variables Differs Among Countries, Among Groups, Or Over Time: A Modified Regression-Type Approach. Sociological Methodology, 28 (1), 175-230.
Xie, Yu (1992). The log-multiplicative layer effect model for comparing mobility tables. American Sociological Review, 57 (June), 380-395.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | data(Yamaguchi87)
# reproduce Table 1
structable(~ Father + Son + Country, Yamaguchi87)
# create table form
Yama.tab <- xtabs(Freq ~ Son + Father + Country, data=Yamaguchi87)
# define mosaic labeling_args for convenient reuse in 3-way displays
largs <- list(rot_labels=c(right=0), offset_varnames = c(right = 0.6),
offset_labels = c(right = 0.2),
set_varnames = c(Son="Son's status", Father="Father's status")
)
###################################
# Fit some models & display mosaics
# Mutual independence
yama.indep <- glm(Freq ~ Son + Father + Country, data=Yamaguchi87, family=poisson)
anova(yama.indep)
mosaic(yama.indep, ~Son+Father, main="[S][F] ignoring country")
mosaic(yama.indep, ~Country + Son + Father, condvars="Country",
labeling_args=largs,
main='[S][F][C] Mutual independence')
# no association between S and F given country ('perfect mobility')
# asserts same associations for all countries
yama.noRC <- glm(Freq ~ (Son + Father) * Country, data=Yamaguchi87, family=poisson)
anova(yama.noRC)
mosaic(yama.noRC, ~~Country + Son + Father, condvars="Country",
labeling_args=largs,
main="[SC][FC] No [SF] (perfect mobility)")
# ignore diagonal cells
yama.quasi <- update(yama.noRC, ~ . + Diag(Son,Father):Country)
anova(yama.quasi)
mosaic(yama.quasi, ~Son+Father, main="Quasi [S][F]")
## see also:
# demo(yamaguchi-xie)
##
|
Loading required package: vcd
Loading required package: grid
Loading required package: gnm
Son UpNM LoNM UpM LoM Farm
Father Country
UpNM US 1275 364 274 272 17
UK 474 129 87 124 11
Japan 127 101 24 30 12
LoNM US 1055 597 394 443 31
UK 300 218 171 220 8
Japan 86 207 64 61 13
UpM US 1043 587 1045 951 47
UK 438 254 669 703 16
Japan 43 73 122 60 13
LoM US 1159 791 1323 2046 52
UK 601 388 932 1789 37
Japan 35 51 62 66 11
Farm US 666 496 1031 1632 646
UK 76 56 125 295 191
Japan 109 206 184 253 325
Analysis of Deviance Table
Model: poisson, link: log
Response: Freq
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev
NULL 74 34313
Son 4 7034.4 70 27279
Father 4 3859.2 66 23419
Country 2 14231.1 64 9188
Analysis of Deviance Table
Model: poisson, link: log
Response: Freq
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev
NULL 74 34313
Son 4 7034.4 70 27279
Father 4 3859.2 66 23419
Country 2 14231.1 64 9188
Son:Country 8 1062.9 56 8125
Father:Country 8 2533.8 48 5592
Analysis of Deviance Table
Model: poisson, link: log
Response: Freq
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev
NULL 74 34313
Son 4 7034.4 70 27279
Father 4 3859.2 66 23419
Country 2 14231.1 64 9188
Son:Country 8 1062.9 56 8125
Father:Country 8 2533.8 48 5592
Country:Diag(Son, Father) 15 4255.3 33 1336
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