twinData {OpenMx}R Documentation

Australian twin sample biometric data.

Description

Australian twin data with 3808 observations on the 12 variables including body mass index (BMI) assessed in both MZ and DZ twins.

Questionnaires were mailed to 5967 pairs age 18 years and over. These data consist of completed questionnaires returned by both members of 3808 (64 percent) pairs. There are two cohort blocks in the data: a younger group (zyg 1:5), and an older group (zyg 6:10)

It is a wide dataset, with two individuals per line. Data include zygosity (zyg), along with heights in metres, weights in kg, and the derived variables BMI in kg/m^2 (stored as “htwt1” and “htwt2”), as well as the log of this variable, stored here as bm1 and bm2. The logged values are more closely normally distributed.

fam is a family identifier. Age is entered only once, as the both twins in each pair share a common age.

fam

a numeric vector

age

a numeric vector

zyg

a numeric vector

part

a numeric vector

wt1

a numeric vector

wt2

a numeric vector

ht1

a numeric vector

ht2

a numeric vector

htwt1

a numeric vector

htwt2

a numeric vector

bmi1

a numeric vector

bmi2

a numeric vector

Usage

data(twinData)

Format

A data frame with 3808 observations on the following 12 variables.

fam

a numeric vector

of family IDs

age

a numeric vector

of ages (years)

zyg

a numeric vector

of zygosity (see below for important details)

part

a numeric vector

wt1

a numeric vector

of weights in kg (twin 1)

wt2

a numeric vector

of weights in kg (twin 2)

ht1

a numeric vector

of heights in kg (twin 1)

ht2

a numeric vector

of heights in kg (twin 2)

htwt1

a numeric vector

of kg/m^2 twin 1

htwt2

a numeric vector

of kg/m^2 twin 2

bmi1

a numeric vector

of log BMI for twin 1

bmi2

a numeric vector

of log BMI for twin 2

Details

Zygosity is coded as follows 1 == MZFF (i.e MZ females) 2 == MZMM (i.e MZ males) 3 == DZFF 4 == DZMM 5 == DZOS opposite sex pairs

Note: Zygosity 6:10 is the same, for an older cohort in the sample. So: 6 == MZFF (i.e MZ females) 7 == MZMM (i.e MZ males) 8 == DZFF 9 == DZMM 10 == DZOS opposite sex pairs

References

Martin, N. G. & Jardine, R. (1986). Eysenck's contribution to behavior genetics. In S. Modgil & C. Modgil (Eds.), Hans Eysenck: Consensus and Controversy. Falmer Press: Lewes, Sussex.

Martin, N. G., Eaves, L. J., Heath, A. C., Jardine, R., Feindgold, L. M., & Eysenck, H. J. (1986). Transmission of social attitudes. Proceedings of the National Academy of Science, 83, 4364-4368.

Examples

data(twinData)
str(twinData)
plot(wt1 ~ wt2, data = twinData)
mzData <- as.matrix(subset(myTwinData, zyg == 1, c(bmi1, bmi2)))
dzData <- as.matrix(subset(myTwinData, zyg == 3, c(bmi1, bmi2)))

[Package OpenMx version 2.2.6 Index]