twinData {OpenMx} | R Documentation |
Australian data on body mass index (BMI) assessed in both MZ and DZ twins, and saved in the text file twinData.txt.
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.
data(twinData)
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
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
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.
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)))