computePowers {cNORM}

Compute powers of the explanatory variable a as well as of the person location l (data preparation)

Description

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.

Usage

computePowers(
  data,
  k = 5,
  norm = NULL,
  age = NULL,
  t = 3,
  covariate = NULL,
  silent = FALSE
)

Arguments

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

covariate

Include a binary covariate into the preparation and subsequently modeling, either by specifying the variable name or including the variable itself. If this has already been done in the ranking, the function uses the according variable. BEWARE! Not all subsequent functions are already prepared for it. It is an experimental feature and may lead to unstable models subsequently.

silent

set to TRUE to suppress messages

Value

data.frame with the powers and interactions of location and explanatory variable / age

See Also

bestModel

Other prepare: prepareData(), rankByGroup(), rankBySlidingWindow()

Examples

# 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)