The function computes powers of the norm variable e. g. T scores (location, L),
an explanatory variable, e. g. age or grade of a data frame (age, A) and the
interactions of both (L X A). The k variable indicates the degree up to which
powers and interactions are build. These predictors can be used later on in the
bestModel
function to model the norm sample. Higher values of k
allow for modeling the norm sample closer, but might lead to over-fit. In general
k = 3 or k = 4 (default) is sufficient to model human performance data. For example,
k = 2 results in the variables L1, L2, A1, A2, and their interactions L1A1, L2A1, L1A2
and L2A2 (but k = 2 is usually not sufficient for the modeling). Please note, that
you do not need to use a normal rank transformed scale like T r IQ, but you can
as well use the percentiles for the 'normValue' as well.