ASReml-S reference manual - VSN International

3.8 Fixed terms 28Summary of reserved names special functionsterm purpose usagelin(f)link(a,b)mbf(obj)pol(x,t)treats the named factor as a variate. The function is defined for f, rf being a simple factor, trait and units. The lin(f) function doesnot center or scale the variable.ensures that the structures for terms a and b are contiguous. rThe function would typically be used in random coefficient regression,where a covariance between intercept and slope mightbe required.Includes obj as a set of covariates to be fitted as a single term rin a similar way to grp. The name obj must also appear as acomponent of the mbf argument to asreml.control() where thedata frame holding the covariates is identified along with a keyfield for merging records with those in data.forms t orthogonal polynomials from the values in x; the mean f, ris excluded if t is negative. For example, pol(time,2) is a factorwith three columns: a constant in the first, centred and scaledlinear covariate in the second and centred and scaled quadraticcovariate in the third. pol() could be interacted with a designfactor to fit random regression models.spl(x, k, points) Random component of a cubic spline for covariate x. spl(x), rdev(x) and possibly lin(x) are used when fitting cubic splines.The cubic spline is composed of a random nonlinear componentimposed on a linear trend. It is fitted by including a special randomfactor, spl(x), and the fixed covariate (x) in the linear model.Knot points are placed at the design points if length(unique(x))< k otherwise there are k equally spaced knot points over therange of x. The default for k is 50. Alternatively, points maycontain a vector of user specified knot points. Both k and pointsmay be omitted and defaults set in asreml.control().3.8.2 Sparse fixed termsThe sparse argument specifies those covariates, factors and interactions for which standarderrors and tests of significance are not required. These effects are estimated usingsparse matrix methods that typically require less memory and less execution time. asremlautomatically includes missing values in the sparse component with a factor named mv.This is a reserved word and should not be used to label variates or factors.3.8.3 CovariatesFor analysis purposes it is recommended that covariates be centred or rescaled to havea variance of 1 to avoid failure to detect singularities. In addition, missing values incovariates are replaced with zeros so it is important in these circumstances to centre thecovariate in question. For example, the command> nin89$linrow