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4 Models Provided in Regional Trial Method |
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1.
Test Model |
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Test Model for regional trial test partitions variation into
varieties, years and locations components, as well as their interaction. |
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2.
Treat model |
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Test Model for regional trial test with treatment partitions variation
into varieties, years, treatments and locations components, as well as their
interaction. |
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3.
TestR Model |
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TestR Model for regional trial test analyzes random effects. |
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4.
TreatR model |
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Test Model for regional trial test with treatment analyzes random
effect. |
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Format of input data |
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For Test model
and TestR model,
the format of data is shown in file CotTest.txt in Sample folder. The first four fixed columns in the
above CotTest.txt represent variety, year, environment and block. The minimum value 1 for all
the four fixed columns. The maximum value is the number of varieties for the
first column, number of years for the second column, number of locations for
the third column, and number of blocks within each year and location for the
fourth column. The code should be continuous integers and arranged in order.
Each missing trait value is
denoted by a dot("."). For TestR
model and TreatR model, the format
of data is shown in file CotTreat.txt in the Sample folder. The first five fixed
columns in the above CotTreat.txt represent variety, year, location,
treatment and block. The
minimum value 1 for all the four fixed columns. The maximum value is the
number of varieties for the first column, number of years for the second
column, number of locations for the third column, number of treatments for
the fourth column, and number of blocks within each year and location for the
fourth column. The code should be continuous integers and arranged in order.
Each missing trait value is
denoted by a dot("."). |
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Test and Treat models
within Regional Trial method
share the same Coefficient-setting Box, which will pop out
automatically when you selecte Test and Treat model from Regional Trial menu. |
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1. CK Transformation |
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Transform the observed data as proportion to the
check variety. |
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2. Contrast Test Number |
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Contrast Test is used for
linear contrast for means of several varieties. Number of contrast test
identifies how many pairs of comparisons want to take. For CotTest.txt, there
are 3 varieties in the file. Considering the 1st variety the CK, we can
compare the means of variety 1 and 3, under the prompt, inputting "-1,
0, 1", and "-1, 1 ,0" for comparison means of variety 1 and 2.
If we want to compare the means of varieties 2 and 3 with variety 1, input
"-1, 1, 1" under prompt. Here, 3 linear contrast were taken, so the
Contrast Test Number is 3. |
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3. Language |
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Select the Language the output file in.
Both Chinese and English are available. |
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4. Singular trait |
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It will estimate the variance components of random effect
for each trait. The results will be automatically saved in file filename.var. |
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5. Multiple Traits |
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It will estimate the covariance of random effect
for each trait. The results will be automatically saved in file filename.cov(filename is the name of input file).
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6. Run |
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Run the data with selected model and model. |
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7. Cancel |
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Leave the Coefficient-setting Box, and
do nothing. |
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TestR and TreatR
models within Regional Trial method share the same
Coefficient-setting Box, which will pop out automatically when you
selecte TestR and TreatR model from Regional
Trial menu. |
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1. Genotype |
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Select genotype as fixed or random effect. |
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2.
Jackknife Number |
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If the Jackknife
Kind is Block, Jackknife Number resampling unit is default 1. If Jackknife
Kind is Cell, Jackknife Number of resampling unit can range form 1 to
9. |
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3. Random Effects Predict Method |
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AUP(adjusted unbiased prediction) or
LUP(linear unbiased prediction) can be used in predicting random effects. |
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4. Language |
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Select the Language the output file in.
Both Chinese and English are available. |
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5.
Var |
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If you
check this option, the program will estimate variance components and predict
random effects. The results are automatically saved in file
filename.var(filename is the name of input file). |
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6. Cov |
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If you
tick this option, the program will estimate covariance components and
correlation coefficients, but you should choose Var and/or Het option first.
The results are automatically saved in file filename.cov. |
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7. Run |
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Run the data with selected model and model. |
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8. Cancel |
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Leave the Coefficient-setting Box, and do nothing. |
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