Description
asreml.options sets less frequently used asreml() options.
Usage
asreml.options(...)
Arguments
…  Arguments in the form name = value , where name is the name of the optionto set. 
Details
The following settings can be altered:  
ai.loadings=0 
Controls modiﬁcation to AI updates of loadings in extended factoranalytic (fa ) 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 10fold, they are all shrunk. Unless the user speciﬁes constraints, ASReml sets them and rotates the loadings each iteration. When ai.loadings i is speciﬁed (default i = −1 speciﬁes noaction), 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. 
ai.penalty=10 
The algorithm for updating loadings in factor analytic models has been improved. The original update procedure sometimes produced unreasonable updates, or exhibited drift. The present strategy modifys 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 suﬁcient initial retardation. If it appears the updates are unreasonable, the value of p is increased by 10%. The default is p = 10%. After the penalty has reduced to 1%, it is further reduced to 0.2%. ai.penalty can be set to 0 if desired. 
ai.sing=FALSE 
Force continuation if a singularity is detected in the average information matrix. 
aodev=FALSE 
If TRUE , return an analysis of deviance. 
aom=FALSE 
If TRUE , return standardized conditional residuals and standardized conditional BLUPs in the aom component of the asreml object. 
Cfixed=FALSE 
If TRUE , return the computed part of the C−^{1} matrix in component Cfixed ; the default is FALSE . The inverse coefﬁcient matrix is fully formed for terms in the dense set. 
Csparse=~NULL 
If a formula is speciﬁed, return the computed part of C−^{1} for those terms given in the formula. asreml does not compute the whole of C−^{1}, only that which is sufﬁcient to calculate the REML solution. 
debug=FALSE 
Return internal data structures. 
dense=~NULL 
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. 
design=FALSE 
If TRUE , return the design matrix in component design of the asreml object. 
drop.unused.levels=TRUE 

eqorder=3 
Set the algorithm used for ordering the mixedmodel equations prior to solution. eqorder=1 processes the equations in user order; generally this will run much slower, if at all in real time for large analyses. 
extra = n 
Forces another mod(n, maxit) iterations after apparent convergence; the default is n=0 . 
fail="hard" 
If “hard ” (the default) fatal errors will terminate execution, otherwise if “soft ” such conditions will be reported as warnings, allowing simulation 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. 
fixgammas=FALSE 
If TRUE , all variance parameters are constrained to be ﬁxed at their starting values. 
font.scale=1.0 
Scale axis text and labels (relative to the asreml default settings) in the graphs generated by plot.asreml() . 
gammaPar=FALSE 
If TRUE (the default is FALSE ), 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. 
glmminloop=1 
Sets 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 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 to 2 or more to increase the number of inner iterations. 
grid=TRUE 
A logical vector of length 1 or length(design.points) (see predict) controlling the expansion of coordinates for 2 dimensional kriging. For a given term, the coordinatesfor prediction in 2 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 and are taken in parallel. 
keep.order=FALSE 
If TRUE , the order of terms in the fixed formula is retained. Set to TRUE if the special model function and() is present. 
knots=50 
The default number of knot points for spline terms. For a variate x , the number of knot points is min(length(unique(x)), knots) . 
maxit=13 
Maximum number of iterations. 
nsppoints=21 
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 + 1.1i]. 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) . 
oscillate=TRUE 
Test for oscillating loglikelihood (default=TRUE ). 
pxem=1 
(PX)EM update strategy for unstructured (US) variance models when Average Information updates cause them to be nonpositive deﬁnite (see uspd ). Valid values are:
pxem Action ∗ Options 3 and 4 cause all US structures to be updated by (PX)EM if any particular one requires EM updates. 
pworkspace="128mb" 
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 . 
random.order="noeff “ 
Reorder terms in the random and sparse formulae in increasing order of number of effects. This is almost always desirable, especially if the stratum variancedecomposition is required. Other options are “ user ” to retain the order given, or “R ” for the default R rules. 
rotate.fa=FALSE 
If FALSE (the default), asreml() initially constrains the ﬁrst k − 1 loadings for higher order (k > 1) factors in factor analytic models to zero. If constraints are not setfor 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. 
scale=1.0 
Overall scale parameter. 
spline.scale= 1 
When forming a design matrix for a spl() term, a standardised scale is used. Setting spline.scale = 1 forces asreml to use the scale of the variable. The default (1) is recommended in most cases. 
spline.step=list (spl=10000,dev=10000, pol=10000) 
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. 
step.size=0.316 
Update shrinkage factor, reduces the update step sizes of the variance parameters. The step size is incremented each iteration to a maximum of 1.0. 
tol=c(0,0) 
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. 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 xe, 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. 
trace=TRUE 
Report convergence monitoring in the console. 
update.Gcon=TRUE 
Update the constraint status of variance parameters in the G.param list component on termination; this may inﬂuence subsequent updates to the model ﬁt (the default is TRUE ). 
update.Rcon=TRUE 
Update the constraint status of variance parameters in the R.param list component on termination; this may inﬂuence subsequent updates to the model ﬁt (the default is TRUE ). 
update.step.size=0.316 
Update shrinkage factor to use in a call to update() . Ignored if set to the default (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() . 
uspd=TRUE 
If TRUE , (the default) 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 ). 
workspace="128mb" 
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 doubleprecision words (groups of 8 bytes). 