Hello, all,
I would like to use imputation, given the following scenario:
The data I possess are not independent as they quantify the degree of genetic
relatedness for PAIRS of individuals sampled from a population.
For example:
individual_1 individual_2 relatedness
1 1 1.00
1 2 0.34
1 3 0.00
1 4 0.79
2 2 1.00
2 3 ?
3 4 0.50
4 4 1.00
The data collected for the variables "individual_1" and "individual_2" are categorical
in nature and are complete, thus I do not need to impute these data. The data I need
to impute are the continuous genetic relatedness data; however, I am concerned about
the pairwise nature of these data.
Intuitively, if we know the degree of relatedness for individuals 1 and 2 as well as
for individuals 1 and 3, then we should be able to deduce the degree of relatedness
for individuals 2 and 3 (the missing datum in the above example). Conversely, knowing
the relatedness for individuals 1 and 4 do not help in deducing the relatedness
between individuals 2 and 3.
I have tried to use Joe Schafer's NORM software, declaring the variables
"individual_1" and "individual_2" as dummy variables. In doing so, am I using the
appropriate model? Alternatively, should I be stratifying the
categorical variables?
Help would be greatly appreciated.
Best regards,
Kelly
_______________________________
Kelly Gallagher
Institut des Sciences de l'Evolution (ISEM)
G�n�tique et Environnement CC065
Universit� de Montpellier 2
Place Eug�ne Bataillon
F-34095 Montpellier Cedex 05
France
tel: 33 (0) 4 67 14 47 18
fax: 33 (0) 4 67 14 36 22
e-mail: [EMAIL PROTECTED]
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