computePowers | R Documentation |
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.
computePowers(data, k = 5, norm = NULL, age = NULL, t = 3, silent = FALSE)
data |
data.frame with the norm data |
k |
degree |
norm |
the variable containing the norm data in the data.frame; might be T scores, IQ scores, percentiles ... |
age |
Explanatory variable like age or grade, which was as well used for the grouping. Can be either the grouping variable itself or a finer grained variable like the exact age. Other explanatory variables can be used here instead an age variable as well, as long as the variable is at least ordered metric, e. g. language or development levels ... The label 'age' is used, as this is the most common field of application. |
t |
the age power parameter (default NULL). If not set, cNORM automatically uses k. The age power parameter can be used to specify the k to produce rectangular matrices and specify the course of scores per independently from k |
silent |
set to TRUE to suppress messages |
The functions rankBySlidingWindow
, rankByGroup
, bestModel
,
computePowers
and prepareData
are usually not called directly, but accessed
through other functions like cnorm
.
data.frame with the powers and interactions of location and explanatory variable / age
bestModel
Other prepare:
prepareData()
,
rankByGroup()
,
rankBySlidingWindow()
# Dataset with grade levels as grouping
data.elfe <- rankByGroup(elfe)
data.elfe <- computePowers(data.elfe)
# Dataset with continuous age variable and k = 5
data.ppvt <- rankByGroup(ppvt)
data.ppvt <- computePowers(data.ppvt, age = "age", k = 5)
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