If TRUE, the date packaged is printed for identification (default = FALSE).

Controls modification to Average Information (AI) updates of loadings in extended factor-analytic (fa(, k)) models. After asreml calculates updates for variance parameters, it checks whether the updates are reasonable and sometimes reduces them over and above any step.size shrinkage. The extra shrinkage has two levels: loadings that change sign are restricted to doubling in magnitude; and if the average change in magnitude of loadings is greater than 10-fold, they are all shrunk. Unless the user specifies constraints, asreml sets them and rotates the loadings each iteration.

When ai.loadings = i is specified (i=-1 is no action), it also prevents AI updates of some loadings during the first i iterations. For f > 1 factors, only the last factor is estimated (conditional on the earlier ones) in the first f - 1 iterations. Then pairs, including the last, are estimated until iteration i (default = 0).

This refers to the algorithm for updating loadings in factor analytic (fa) models. The present strategy modifies the average information matrix by increasing the diagonal elements pertaining to loadings by a percentage, p. The default is to start with p = 10% and reduce it by 1 or 2% each iteration down to 1%. If the starting values are poor, 10% may not be a sufficient initial retardation. If it appears the updates are unreasonable, the value of p is increased by 10%. After the penalty has reduced to 1%, it is further reduced to 0.2%. ai.penalty can be set to 0 if desired (default = 10, corresponding to 10%).

If TRUE, forces continuation if a singularity is detected in the average information matrix. Variance components are reported to help identify singularity but these are often incorrect (default = FALSE).

If TRUE, return an analysis of deviance (default = FALSE).

If TRUE, return standardized conditional residuals and standardized conditional BLUPs in the aom component of the asreml object (default = FALSE).

If TRUE, return the computed part of the C^{-1} matrix in component Cfixed. The inverse coefficient matrix is fully formed for terms in the dense set (default = FALSE).

If TRUE, the header text produced by methods such as wald and predict will be displayed in a different colour if supported by the output terminal device (default = TRUE).

If a formula is specified, return the computed part of C^{-1} for those terms given in the formula. The library asreml does not compute the whole of C^{-1}, only that which is sufficient to calculate the REML solution (default = Csparse = ~NULL).

If TRUE, return the computed part of C^{-1} for all terms in the model.

Return internal data structures for helping debugging (default = FALSE).

Include the equation(s) for the term(s) in the formula in the dense set. This results in faster processing if the term is associated with a known dense inverse relationship matrix (default = dense = ~NULL).

If TRUE, return the design matrix for component design of the asreml object. This option might be used, for example, in generating design matrices that can then be used in post-processing (default = FALSE).

If TRUE, levels of simple factors that do not appear in the data are dropped (default = TRUE).

Set the algorithm used for ordering the mixed-model equations prior to solution. The argument eqorder = -1 processes the equations in user order; generally this will run much slower, if at all, in real time for large analyses (default = 3).

Forces other mod(extra, maxit) extra iterations after apparent convergence (default = 0).

If "hard", fatal errors will terminate execution, otherwise if "soft" such conditions will be reported as warnings, allowing testing runs, for example, to continue. In both cases the converge component of the asreml object will be set to FALSE and the results will be erroneous (default = "hard").

If TRUE, all variance parameters are constrained to be fixed at their starting values (default = FALSE).

Scale axis text and labels (relative to the asreml default settings) in the graphs generated by plot() (default = 1.0).

If TRUE, single-section-models will be fitted using the gamma parameterization irrespective of whether the residual formula specifies a correlation or variance model. The default behaviour for single-section models is to fit on the gamma scale if the residual formula specifies a correlation structure, and on the sigma scale if the residual formula specifies a variance structure (default = FALSE).

Set the number of inner iterations performed in an iteratively weighted least squares analysis. These estimate the effects in the linear model for the current set of variance parameters; outer iterations are the average information 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 to 2 or more to increase the number of inner iterations (default = 1).

A logical vector of length 1 or length(design.points) (see predict) controlling the expansion of coordinates for two-dimensional kriging. For a given term, the coordinates for prediction in two dimensions (x, y) are given as a list of two vectors or a two-column matrix component of design.points. If TRUE, the coordinates are expanded to form an (x, y) grid of all possible combinations, otherwise the columns of the matrix are taken in parallel (default = TRUE).

If TRUE, the order of terms in the fixed formula is retained. Set to TRUE if the special model function and() is present (default = FALSE).

The number of knot points for spline terms to consider. For a variate x, the number of knot points is min(length(unique(x)), knots) (default = 50).

If 0, a new license_id is created for identification (default = 0).

Maximum number of iterations to stop fitting (default = 13).

Value that influences the number of points used when predicting splines and polynomials. The design matrix generated by the pol(x) and spl(x) functions are modified to include extra rows for points used in prediction. The range of x is divided by nsppoints - 1 to give a step size i. For each point p in x, a predict point is inserted at p+i if there is no data value in the interval [p, p+1.1 i].

The argument nsppoints is ignored if the predict.asreml() argument design.points is set (or the design.points component of the predict list argument to asreml() is not empty). This process also affects the number of levels identified by dev(x) (default = 21).

If TRUE, the test for oscillating log-likelihood is implemented (default = TRUE).

Sets the workspace needed by the predict() method; follows the same convention as workspace. Ignored if the predict argument to asreml() is not set. Note that the total workspace used for prediction is: workspace + pworkspace (default = "128mb").

Selection of the (PX)EM update strategy for unstructured (us) variance models when average information updates cause them to be non-positive definite (see uspd) (default = 1). Valid options are:

^*Options 3 and 4 cause all us structures to be updated by (PX)EM if any particular one requires EM updates.

If option "noeff" the terms in the random and sparse formulae are reordered in increasing number of effects. This is almost always desirable, especially if the stratum variance decomposition is required. Other options are "user" to retain the order given in the model specification, or "R" for the default R rules (default = "noeff").

If FALSE, asreml() initially constrains the first k-1 loadings for higher order (k > 1) factors in factor analytic models to zero. If constraints are not set for factor analytic models with more than one factor, asreml() will set them internally and rotate the loadings each iteration (rotate.fa = TRUE). This option also modifies the action of update.Gcon such that rotation, if specified, is applied on an update (default = FALSE).

Overall scale parameter (default = 1.0).

If TRUE, the score vector is returned in a component score of the asreml object (default = FALSE).

When forming a design matrix for a spl() term, a standardized scale is used. Setting spline.scale = 1 forces asreml to use the scale of the variable. The value -1 is recommended in most cases (default = -1).

A list with components named spl, dev and pol specifying the resolution for spline deviations and polynomial functions, respectively. Points closer together than 1/spline.step of the range will be treated as a single point (default spline.step = list(spl = 10000, dev = 10000, pol = 10000)).

Value for updating shrinkage factor. It reduces the update step sizes of the variance parameters. The step size is incremented each iteration to a maximum of 1.0 (default = 0.316).

The maximum number of threads to be used with OMP parallel processing. asreml will use all threads available up to the maximum number specified. The value -1 will use all available threads (default = -1).

A vector of length two that modifies the sensitivity of asreml to detect singularities in the mixed model equations. This is intended for the rare occasions when singularities are detected after the first iteration (default = c(0, 0)).

Normally a singularity is declared if the adjusted sum of squares of a covariable is less than e, or less than the *uncorrected sum of squares* \times e, where e = 10^{-8} in the first iteration and 10^{-10} thereafter. If tol = c(a, b), e is scaled by 10^a for the the first iteration, and 10^b for subsequent iterations. Once a singularity is detected, the corresponding equation is dropped (forced to be zero) in subsequent iterations.

If the problem of later singularities arises because of the low coefficient of variation of a covariable, it may be advisable to centre and rescale the covariable. If the degrees of freedom are correct in the first iteration, the problem lies with the variance parameters and a different variance model (or constraint) is needed.

If TRUE, convergence monitoring of the current fit is reported in the console (default = TRUE).

If TRUE, the constraint status of variance parameters in the G.param list component on termination is updated; this may influence subsequent updates to the model fit (default = TRUE).

If TRUE, the constraint status of variance parameters in the R.param list component on termination is updated; this may influence subsequent updates to the model fit (default = TRUE).

Value for updating shrinkage factor to use in a call to update(). If ignored it is set to 0.316 or if step.size is explicitly specified on the update() call; otherwise the shrinkage factor is set to update.step.size in the call to asreml() constructed by update() (default = 0.316).

If "standard", then asreml will make use of linear algebra routines compiled into the asreml package, otherwise the version of BLAS / LAPACK from R will be used (default = "standard").

If TRUE, set the boundary constraint for each parameter in unstructured variance models to "P". Under these conditions, asreml checks whether the updated matrix is positive definite; if not, the average information update is replaced with an EM update (see pxem) (default = TRUE).

Sets the workspace for the core REML routines in the form of a number optionally followed directly by a valid measurement unit. Valid units are kb, mb or gb; if no units are given then the value is interpreted as double-precision words (groups of 8 bytes) (default = "128mb").

Only relevant if the object passed to the data argument in asreml is of class data.table. If TRUE, a copy of data is created in memory within the function scope; in this case, the original object passed to data is not modified. If FALSE, the original object is modified by reference. Setting this option to FALSE is useful when the original data object is large, leading to less memory usage (default = FALSE).

If TRUE residual working variables will be returned in the asreml.object (if they exist)