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VSCREEN procedure

Performs screening tests for fixed terms in a REML analysis (R.W. Payne).


PRINT = string token Controls printed output (ftests, waldtests); default ftes, wald
EXCLUDEHIGHER = string token Whether to exclude higher-order interactions in the conditional models (yes, no); default no
FORCED = formula Terms that must always be included in the model (no tests on these terms); default *
FSAVE = pointer Saves the F tests
WSAVE = pointer Saves the Wald tests
SAVE = REML save structure Specifies the analysis whose fixed terms are to be tested; by default this will be the most recent REML

No parameters


VSCREEN calculates marginal and conditional tests for fixed terms in a REML analysis. By default, these are from the recent REML analysis. However, you can take an earlier analysis, by using the SAVE option of VSCREEN to specify its save structure (saved using the SAVE parameter of the earlier REML command).

In the marginal test, the term is added to the simplest possible model. For example, the main effect of A would be added to the null model, and the interaction A.B would be added to a model containing only the main effects A and B.

In the conditional test, the term is added to the most complex possible model that contains no terms involving the term to be tested. For example, interaction A.B would be added to the model containing all terms except those involving A.B (such as the interaction A.B.C). By default, the most complex model includes terms with more factors or variates than the term being tested. For example, the interaction C.D.E would be included when testing A.B. You can exclude these higher-order terms by setting option EXCLUDEHIGHER=yes (and VSCREEN will print a message to remind you that this has been done).

You can specify terms that should always be included in the model by using the FORCED option. These terms are fitted first, and are not tested.

The PRINT option controls printed output, with the following settings.

ftests presents F statistics for the terms. If denominator degrees of freedom (ddf) are available from the earlier REML analysis, probabilities are also given. Note, however, that these ddf are correct only for models that correspond to those in the sequential Wald table in the REML analysis. They should be acceptable for the other models, but you should be cautious when probabilities are close to critical values.
waldtests presents Wald statistics for the terms. These suffer from the usual biases of Wald tests in REML analyses, and so should again be used with caution.

You can save the results of the F tests and the Wald tests, in pointers, using the FSAVE and WSAVE options, respectively. The elements of the pointers are labelled by the headers of the columns used in the printed output.

An advantage of using VSCREEN to assess the fixed model, rather than running a succession of REML analyses with different fixed models, is that the fixed terms are assessed against identical estimates of the random variation (as in an analysis of variance). When terms are dropped from (or added to) the fixed model in a REML analysis, the random variation will change. For example, it will increase if a term with a Wald statistics greater than its number of degrees of freedom is dropped. It may therefore be difficult to reach consistent decisions about which fixed terms are genuinely required.

Once you have used VSCREEN to decide which terms to keep in the fixed model, you can use only those terms for prediction, by specifying them in the MODEL option of VPREDICT.

Parameters: none.


VSCREEN defines a weighted regression, with weight matrix given by the inverse of the unit-by-unit variance-covariance matrix (obtained using the UVCOVARIANCE option of VKEEP.) It then calls the RSCREEN procedure to calculate the tests.

Action with RESTRICT

Any restriction applied to vectors used in the REML analysis will apply also to the results from VSCREEN.

See also

Directive: REML.
Commands for: REML analysis of linear mixed models.


CAPTION     'VSCREEN example',\
            'Example 5.3.6 from The Guide to Genstat, Part 2 Statistics';\
FACTOR      [NVALUES=322; LEVELS=27] Dam
&           [NVALUES=322; LEVELS=18] Pup
FACTOR      [NVALUES=322; LEVELS=2; LABELS=!T('M','F')] Sex
FACTOR      [NVALUES=322; LEVELS=3; LABELS=!T('C','Low','High')] Dose
VARIATE     [NVALUES=322] Littersize,Weight
OPEN        '%GENDIR%/Examples/GuidePart2/Rats.dat'; CHANNEL=chan
READ        [CHANNEL=chan] Dose,Sex,Littersize,Dam,Pup,Weight; \
CLOSE       chan
VCOMPONENTS [FIXED=Littersize+Dose*Sex] RANDOM=Dam/Pup
REML        [PRINT=model,components,wald] Weight
Updated on October 28, 2020

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