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MENU LIST |
Description/Comment
(if not self-evident) |
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Data |
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This group of functions
saves a lot of time and makes GGEbiplot many times more efficient.
Everyone who values time should select this module. |
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Entry/Tester Switch Roles |
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This is a very useful
function. It means many more additional models for the same set of data.
It is particularly useful when the dataset has two many columns but
relatively fewer rows (e.g., genetic mapping data can have too many
markers while Microsoft spreadsheets have only 255 columns. Such data will
have to be prepared as few columns and many rows. After read by GGEbiplot,
this function is essential to achieve the research purpose. |
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Fill Missing Cells By... |
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Weighed from Similar Entries |
This function chooses
five rows that are most similar to the row that has missing values.
Weights are assigned to the rows according to the degrees of similarity,
and the missing cells are estimated and filled. |
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Entry Means |
Fill the missing cell
with the entry mean. |
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Tester Means |
Fill the missing cell
with the tester mean. |
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Entry and Tester Means |
Fill the missing cell
with the average of the entry mean and the tester mean. |
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Derived variable |
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A new variable can be
derived and added from existing variables |
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Run
BALANCED Subset by... |
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This allows the
generation of balanced subset of the data that is read. This functions
save a lot of the researcher’s time and hassle. |
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Deleting Testers |
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Deleting Entries |
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Run
Partially Balanced Subset by... |
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Similar to above but
allows the user to specify a degree of tolerance on the unbalancedness of
the data. |
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Deleting Testers |
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Deleting Entries |
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Run
ANY subset by... |
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This allows generation
of a biplot based on ANY possible subset of the original data. Extremely
useful. |
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Deleting Testers |
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Deleting Entries |
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Entry Stratification |
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This group of functions
allows subset of the entries be selected based on their biplot position. |
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Vertex Entries only |
Only the entries that
are on the vertex hulls are selected. |
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Entries outside a sector |
Entries within a
specified sector are excluded from the biplot |
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Entries within a sector |
Entries within a
specified sector are included in the biplot |
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High mean entries |
Entries with higher
mean values across testers are selected based on a criterion specified by
the user. This allows a better appreciation of the entry by tester
(genotype by environment) interactions and the identification of
specifically adapted genotypes. |
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Low
mean entries |
Entries with lower mean
values across testers are selected based on a criterion specified by the
user. |
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Possible winners only |
Only entries that are a
winner for one more of the testers are selected. |
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Long Vector Entries |
Entries that have
vectors longer than a specified value are included. These entries
(genotypes) are more responsive to the testers (environments). |
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Short Vector Entries |
Entries that have
vectors shorter than a specified value are included. These entries
(genotypes) are less responsive to the testers (environments). |
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Tester Stratification |
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Testers outside a sector |
Testers within a
specified sector are excluded from the biplot |
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Testers within a sector |
Testers within a
specified sector are included in the biplot for more detailed
investigation |
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Long vector testers |
Testers that have
vectors longer than a specified value are included. These entries
(genotypes) are more discriminating of the entries |
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Short vector testers |
Testers that have
vectors shorter than a specified value are included. These testers are
less well represented in the original biplot or they have little
associations with those that have longer vectors in the original biplot |
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Back to Previous Subset |
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This generates a biplot
based on a previous subset of the original data |
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Back to Original Data |
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This generates a biplot
based on the full data as it was first read from the data file or
generated by GGEbiplot from 3-way or 4-way or multi-way data |
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Reverse the Sign of... |
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One
Tester |
Sometimes a trait is so
measured that a smaller value means more desirable. In such case, this
function will be useful to identify the desirable genotypes. |
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All
Testers |
Sometimes smaller
values mean more desirable. In such case, this function will be useful to
identify the desirable genotypes. |
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For
Diallel Data Analysis... |
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This group of functions
are useful for Diallel cross data only. |
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Complete the Two-way Table |
Dataset from Diallel
crosses without reciprocals contains data only half of the 2-way table.
This function can automatically fill the other half so that biplot
analysis can be completed. |
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Remove Reciprocal Effect |
Dataset from Diallel
crosses with reciprocals contains data consisting of two unsymmetrical
triangles. This function can automatically averages the reciprocals and
makes the two-way table symmetrical, whereby the reciprocal effects are
removed. |
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Exclude Parents Per se |
Values of the parents
per se are removed and treated as missing values. |
