Clarification of data transformations

# Clarification of data transformations

Hi,
I would like to recode a variable age at measurement such that the last level
corresponds to ages 5+ to fit as a factor a model.  Is this possible in ASREML
without creating a special data set?

I have been trying to use MIN transformations on page 21 of the Manual (dated
February 8, 2000) says that the !MIN v takes minimum of v and the data value.
However, page 58 Chapter 4 say:
!MIN v is the minimum of the field and the value v (MAX(<field>,v))

My understanding is that ASREML recodes the levels based on the first value
found and then applied these transformations.  I.E. the first age of dam
values in order found in the data set are:
4, 3, 5, 6 and 2
So ASREML creates the vector with values    [4 3 5 6 2],
!MIN  3 then creates the vector with values [4 3 5 5 5].

Thus, v refers to the index of the vector and not the value.  MAX seems to do
the same but I have yet to understand the SET transformation.

Thanks in advance for any suggestions,
Bruce

The simple data I have been using:
1 3 4 12
1 4 3 12
1 4 5 13
1 4 6 14
2 2 2 13
2 3 3 12
2 4 4 14
2 4 5 12
2 4 6 11
3 2 2 11
3 3 3 12
3 4 4 12
3 4 5 13
3 4 6 15

The .as file:

Test of coding
animal
tc    !I
code   !I !MIN 3
y
test.dat
y ~ mu code

The .asr file:

ASREML [ 8 Feb 2000]  Test of coding

Thu Feb 10 10:16:00 2000   8.00 Mbyte  Unix   test
Reading test.dat  FREE FORMAT skipping  0  lines
Univariate analysis of y
Using       14 records [of      14 read from      14 lines of test.dat
]
Model term      Size Type    COL   Minimum    Mean      Maximum   #zero #miss
1 animal          1 Covariat  1  1.000      2.071      3.000         0     0
2 tc              3 Factor    2      1     1.9286          3         0     0
3 code            3 Factor    3      1     2.3571          3         0     0
4 y               1 Variate   4  11.00      12.57      15.00         0     0
5 mu              1 Constant Term
Forming  4  equations:  4  dense
Initial updates will be shrunk by factor    0.548
NOTICE:     1 (more) singularities,
LogL=-9.75614     S2=  1.4697         11 df    1.000
Final parameter values                        1.0000

Source                Model  terms     Gamma     Component    Comp/SE   % C
Variance                 14     11   1.00000       1.46970       2.35   0 P

Analysis of Variance       DF     F-incr      F-adj  StndErrDiff
5 mu                      1    1505.46     327.51
3 code                    2       0.43       0.43  0.8807

Solution       Standard Error    T-value     T-prev
3 code
3  -0.666667       0.989847         -0.67
5   0.833333E-01   0.820738          0.10      0.91
5 mu
4    12.6667       0.699928         18.10
Finished: Thu Feb 10 10:16:02 2000   LogL Converged

The .sln file:

code                            4           0.000       0.000
code                            3         -0.6667      0.9898
code                            5          0.8333E-01  0.8207
mu                              1           12.67      0.6999
Residual            1         -0.6667       12.67
Residual            2          0.1776E-14   12.00
Residual            3          0.2500       12.75
Residual            4           1.250       12.75
Residual            5          0.2500       12.75
Residual            6          0.1776E-14   12.00
Residual            7           1.333       12.67
Residual            8         -0.7500       12.75
Residual            9          -1.750       12.75
Residual           10          -1.750       12.75
Residual           11          0.1776E-14   12.00
Residual           12         -0.6667       12.67
Residual           13          0.2500       12.75
Residual           14           2.250       12.75

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