I want to use a diallel analysis in R, for some of my own data.  I've been
through the primary literature and textbooks, and remain stumped as to how
to implment this in R.
I can illustrate the problem using a published example dataset: [Cockerham
and Weir (1977) Quadratic Analyses of Reciprocal Crosses. Biometrics, Vol.
33, No. 1 pp. 187-203]

In this study, 8 different individuals were crossed in a diallel design with
reciprocals but without self-crosses.  Two individuals were measured for
each combination of sire and dam.  The goal is to partition the phenotypic
variance based on comparisons among full and half-siblings, and draw
inferences about the additive and dominant components of variation.

The data look like this (complete dataset is at the end of this message)

>summary(df)
  val sire dam block Min.   : 9.00 1:14 1:14 1:56 1st Qu.:13.35 2:14 2:14
2:56 Median :15.35 3:14 3:14  Mean   :16.31 4:14 4:14  3rd Qu.:18.05 5:14
5:14  Max.   :31.80 6:14 6:14  (Other):28 (Other):28


The analysis used in that (and other studies) that I want to reproduce in R
identifies general combining ability (GCA, the average performance of a line
in hybrid combinations), and specific combining ability (SCA, cases where
certain hybrid combinations exceed expectations).  The output from this
analysis looks like this:
    Df MS Blocks 1 4.32 General 7 175.04 Specific 20 21.2 Reciprocal General
7 53.43 Reciprocal Specific 21 11.66 Error 55 3.62

Specifically, I am interested in using my data to ask what proportion of the
phenotypic variance is additive genetic variance?

The example dataset is pasted below.  I would greatly appreciate any ideas
for how to do this.

-- 
Eli Meyer
Postdoctoral Fellow
Department of Integrative Biology
University of Texas at Austin
Austin, TX 78712
office: (512) 475-6424
cell: (310) 618-4483
--

Cockerham-Weir.tab

    val sire dam block 1 14.4 1 2 1 2 16.2 1 2 2 3 27.2 1 3 1 4 30.8 1 3 2 5
17.2 1 4 1 6 27 1 4 2 7 18.3 1 5 1 8 20.2 1 5 2 9 16.2 1 6 1 10 16.8 1 6 2
11 18.6 1 7 1 12 14.4 1 7 2 13 16.4 1 8 1 14 16 1 8 2 15 15.4 2 1 1 16 16.5
2 1 2 17 14.8 2 3 1 18 14.6 2 3 2 19 18.6 2 4 1 20 18.6 2 4 2 21 15.2 2 5 1
22 15.3 2 5 2 23 17 2 6 1 24 15.2 2 6 2 25 14.4 2 7 1 26 14.8 2 7 2 27 10.8
2 8 1 28 13.2 2 8 2 29 31.8 3 1 1 30 30.4 3 1 2 31 21 3 2 1 32 23 3 2 2 33
24.6 3 4 1 34 25.4 3 4 2 35 19.2 3 5 1 36 20 3 5 2 37 29.8 3 6 1 38 28.4 3 6
2 39 12.8 3 7 1 40 14.2 3 7 2 41 13 3 8 1 42 14.4 3 8 2 43 16.2 4 1 1 44
17.8 4 1 2 45 11.4 4 2 1 46 13 4 2 2 47 16.8 4 3 1 48 16.3 4 3 2 49 12.4 4 5
1 50 14.2 4 5 2 51 16.8 4 6 1 52 14.8 4 6 2 53 12.6 4 7 1 54 12.2 4 7 2 55
9.6 4 8 1 56 11.2 4 8 2 57 14.6 5 1 1 58 18.8 5 1 2 59 12.2 5 2 1 60 13.6 5
2 2 61 15.2 5 3 1 62 15.4 5 3 2 63 15.2 5 4 1 64 13.8 5 4 2 65 18 5 6 1 66
16 5 6 2 67 10.4 5 7 1 68 12.2 5 7 2 69 13.4 5 8 1 70 20 5 8 2 71 20.2 6 1 1
72 23.4 6 1 2 73 14.2 6 2 1 74 14 6 2 2 75 18.6 6 3 1 76 14.8 6 3 2 77 22.2
6 4 1 78 17 6 4 2 79 14.3 6 5 1 80 17.3 6 5 2 81 9 6 7 1 82 10.2 6 7 2 83
11.8 6 8 1 84 12.8 6 8 2 85 14 7 1 1 86 16.6 7 1 2 87 12.2 7 2 1 88 9.2 7 2
2 89 13.6 7 3 1 90 16.2 7 3 2 91 13.8 7 4 1 92 14.4 7 4 2 93 15.6 7 5 1 94
15.6 7 5 2 95 15.6 7 6 1 96 11 7 6 2 97 13 7 8 1 98 9.8 7 8 2 99 15.2 8 1 1
100 17.2 8 1 2 101 10 8 2 1 102 11.6 8 2 2 103 17 8 3 1 104 18.2 8 3 2 105
20.8 8 4 1 106 20.8 8 4 2 107 20 8 5 1 108 17.4 8 5 2 109 17 8 6 1 110 12.6
8 6 2 111 13 8 7 1 112 9.8 8 7 2

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