Datafile line qualifiers

Purpose

These qualifiers apply to the iteration process.

Common qualifiers

!CONTINUE requests that a job be run using estimates of the variance parameters obtained in a previous run and held in the .rsv file.

!DENSE n resets the number of equations included in the DENSE solved set of equations. By default, fixed factors in the first 800 equations comprise the DENSE set. Increase the number to have more fixed factors included. The Wald F statistics are provided for fixed terms in the DENSE set.

!EMFLAG !PXEM use an EM algorithm to update variance parameters in US (unstructured) variance matrices when the AI algorithn results in a negative definite matrix.

!GDENSE moves a simple large GRM term into the DENSE equations. Since GRM matricies are usually not sparse, they process faster if processed as part of the DENSE equations.

!MAXIT m [!EXTRA n ] limits the number of iteration to m. Provided m is not exceed, ASReml will perform n extra iterations after achieving the default convergence criterion.

!MVINCLUDE [!MVREMOVE ] controls how ASReml treats missing values in design variables. With !MVINCLUDE, missing values are given a value of 0, which is an impliocit extrass class in fixed effect factors. It means missing values are treated as a common class with effect 0.0. It should not be used with covariates unless they have been centered. !MVREMOVE discards recocords which have missing values in design variables.

Controlling AI updates

ASReml stops if the Average Information Matrix is singular unless !AISINGULARITIES is specified

Occasionally there are problems with the AI updates for factor analysis loadings which may be modified using !AILOADINGS.

When trying to estimate an unstructured US variance matrix, you may use !EMFLAG to request EM updates if the AI updates would make the matrix non positive definite. Consider using a different parameterization such as XFA.

In spatial analyses, it is sometimes convenient to estimate an autoregressive correlation parameter when there is little information on its value. You may then use the !ARLIMIT qualifier to limit the size of the parameter.

As an ad hoc procedure, ASReml does not usually take the full AI update for the first few iterations. This is controlled by the !STEPSIZE and !SLOW qualifiers.

!FREEGH i specifies the iteration after which variance parameters br given the H (hold) parameter constraint are released for updating.
ih has a default value of 3. This was added particularly for HGLM models when the first few iterations need to focus on constructing the working variables.

!HOLD list allows the user to temporarily fix the parameters listed. Parameter numbers have been added to the reporting of input values to facilitate use of this and other parameter number dependent qualifiers. The list should be in increasing order using colon to indicate a sequence. For example

 !HOLD 1:20 30:40.

!BLUP

When the user only wants to perform a BLUP analysis and is not interested in estimating variance parameters (assuminng that they are known), the !BLUP qualifier controls how much of an iteration is performed before the BLUP effects are written to file. h2 Modifying model defaults:

!DENSE, modifiers how many (fixed) effects (and consequently model terms) can be included in the DENSE portion of the mixed model equations; the portion used to generate tests of fixed effects.

!EXTRA requests ASReml do more iterations after the usual convergence criterion have been satisfied.

Adjusting sum of squares and/or residual degrees of freedom:

Sometimes, a weighted analysis is performed where the weights are actually just providing a concise method of data entry. Then the mixed model equations actually represent more records than were actually supplied in the data file. Consequently the residual degrees of freedom will be wrong unless adjusted using either

!ADJUST or !DF qualifiers.

When analysing summarised data, you may have an independent estimate of the variance. To include that variation in the analysis, use !YSS 100 !DF 20 say, to add the equivalent of 20 observations with a variance of 5.

Refining GLMM model fitting

!GLMM n sets the number of inner iterations performed when a iteratively weighted least squares analysis is performed. Inner iterations are iterations to estimate the effects in the linear model for the current set of variance parameters. Outer iterations are the AI updates to the variance parameters. The default is to perform 4 inner iterations in the first round and 2 in subsequent rounds of the outer iteration. Set n to 2 or more to increase the number of inner iterations.

Other qualifiers

!AILOADINGS !AIPENALTY moderate the way loadings are updated in Factor Analytic models.

!AISINGULARITY indicates ASReml should keep iterating when the AI matrix is singular (indicating some parameters cannot be updated.

!ARLIMIT !S2LIMIT set bounds on the autoregression correlation parameter and the residual variance parameter.

!EQORDER !LAST affects the way the order of fitting SPARSE equations is determined. !LAST forces certain equations to be fitten 'last' to avoid random effects becomming 'singular'.

!MP p !SP affect how many processor threads are permitted. The maximum is 16 or the (lower) number of processors available. !SP requests ASReml only use 1 processor. Some tests fail to show much difference between different values of p.

!NOCHECK !NOREORDER !NOZEROVC

!SLOW !STEPSIZE reduce the stepsize used in the AI algorithm in the hope thattaking mode smaller updates will lead to better convergence

!SNODE A large part of the recent processing speed increase in ASReml is due to block processiing using Intel MKL libraries. !SNODE (Single Node) avoids using these newer routines.

!TOLERANCE adjusts the criterion of declaring an equation is singular.

!VRB requests ASReml write out the dense block of the C matrix.

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