several predict statements may be specified.
The first step is to specify the classify set of explanatory variables
after the predict directive.
Predict qualifiers
The prediction qualifiers are defined with the
following syntax:
f is an explanatory variable which is a factor,
t is a list of terms in the fitted model,
v is a list of explanatory variables.
Controlling formation of tables
!AVERAGE f [ weights]
is used to formally include a variable in
the averaging set and to explicitly set the weights for averaging.
Variables that only appear in random model terms are not included
in the averaging set unless included with this qualifier.
The default for weights is equal weights.
weights can be expressed like
{3*1 0 2*1/5} to represent
the sequence 0.2 0.2 0.2 0 0.2 0.2. The string inside the curly brace is expanded first and
the expression n*v means n occurrences of v. A
separate !AVERAGE qualifier is required for each
variable requiring explicit weights or to be added to the default averaging set.}
!PARALLEL [ v]
without arguments means all classify variables
are expanded in parallel. Otherwise list the
variables from the classify set whose levels are to be taken in parallel.
!PRESENT v
is used
when averaging is to be based only on cells with data. v is a
list of variables and may include variables in the classify set.
v may not include variables with an explicit !AVERAGE
qualifier. ASReml works out what combinations are present from the
design matrix. A second !PRESENT qualifier is
allowed on a predict statement
(but not with !PRWTS).
This is needed when there are two nested factors such as sites
within regions and genotype within family. The two lists must not overlap.
!PRWTS v
is used
in conjunction with the first !PRESENT factors
to specify the weights that ASReml will use for averaging that
!PRESENT table.
More details.
Controlling inclusion of model terms
!EXCEPT t
causes the
prediction to include all fitted model terms not in t.
!IGNORE t
causes ASReml to set up a prediction model based on the
default rules and then removes the terms in t.
This might be used to omit the spline
Lack of fit term ( !IGNORE fac(x))
from predictions as in
yield ~ mu x variety !r spl(x) fac(x)
predict x !IGNORE fac(x)
which would predict points on the spline curve averaging over
variety.
!ONLYUSE t
causes the
prediction to include only model terms in t. It can be used for example
to form a table of slopes as in
HI ~ mu X variety X.variety
predict variety X 1 !onlyuse X X.variety
!USE t
causes ASReml to
set up a prediction model based on the default rules and then adds
the terms listed in t.
Printing
!DEC [ n]
gives the user control of the number of decimal places
reported in the table of predicted values where n is 0...9.
The default is 4. G15.9 format
is used if n exceeds 9.
!PLOT [ x]
instructs ASReml to attempt a plot of the predicted values.
This qualifier is only applicable in versions of ASReml linked
with the Winteracter Graphics library. If there is no argument,
ASReml produces a figure of the predicted values as best it can.
The user can modify the appearance by typing ESC to expose a menu
or with the
plot arguments.
!PRINTALL
instructs
ASReml to print the predicted value, even if it is not of an estimable
function. By default, ASReml only prints predictions that are of
estimable functions.
!SED
requests all standard errors of difference be printed.
Normally only an average value is printed.
!TDIFF
requests t-statistics be printed for all combinations of predicted values.
!TURNINGPOINTS n
requests ASReml to scan the predicted values from a fitted line
for possible turning points and if found, report them
and save them internally in a vector which can be accessed by subsequent parts
of the same job using !TPn. This was added
facilitate location of putative QTL.
!TWOSTAGEWEIGHTS
is intended for use with variety trials which will
subsequently be combined in a meta analysis.
It forms the variance
matrix for the predictions, inverts it and writes the predicted variety means
with the corresponding diagonal elements of this matrix to the
.pvs file.
These values are used in some variety testing programs in Australia
for a subsequent second stage analysis across many trials.
A data base is used to collect the results from the individual trials
and write out the combined data set. The diagonal elements are used as weights in the combined analysis.
!VPV
requests that the variance matrix of predicted values
be printed to the .pvs file.
Examples
Examples are as follows:
yield ~ mu variety !r repl
predict variety
is used to predict variety means in the NIN field trial
analysis. Random repl is ignored in the prediction.
yield ~ mu x variety !r repl
predict variety
predicts variety means at the average of x
ignoring random repl.
yield ~ mu x variety repl
predict variety x 2
forms the hyper-table based on variety
and repl at the covariate value of 2 and then
averages across repl
to produce variety predictions.
GFW Fdiam ~ Trait Trait.Year !r Trait.Team
predict Trait Team
forms the hyper-table for each trait based on Year and
Team with each linear combination in each
cell of the hyper-table for each trait using Team
and Year effects.
Team predictions are produced by averaging over years.
yield ~ variety !r site.variety
predict variety
will ignore the site.variety term in forming the predictions while
predict variety !AVERAGE site
forms the hyper-table based on site
and variety with each linear combination in each
cell using variety
and site.variety effects and then forms averages across sites
to produce variety predictions.
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