plotDerivative {cNORM} | R Documentation |
Plots the scores obtained via the first order derivative of the regression model in dependence of the norm score. The results indicate the progression of the norm scores within each age group. The regression based modeling approach relies on the assumption of a linear progression of the norm scores. Negative scores in the first order derivative indicate a violation of this assumption. Scores near zero are typical for bottom and ceiling effects in the raw data. The regression models usually converge within the range of the original values. In case of vertical and horizontal extrapolation, with increasing distance to the original data, the risk of assumption violation increases as well. ATTENTION: plotDerivative is currently still incompatible with reversed raw score scales ('descent' option)
plotDerivative( model, minAge = NULL, maxAge = NULL, minNorm = NULL, maxNorm = NULL, stepAge = 0.2, stepNorm = 1, order = 1 )
model |
The model from the bestModel function or a cnorm object |
minAge |
Age to start with checking |
maxAge |
Upper end of the age check |
minNorm |
Lower end of the norm score range, in case of T scores, 25 might be good |
maxNorm |
Upper end of the norm score range, in case of T scores, 25 might be good |
stepAge |
Stepping parameter for the age check, usually 1 or 0.1; lower values indicate higher precision / closer checks |
stepNorm |
Stepping parameter for norm scores |
order |
Degree of the derivative (default = 1) |
checkConsistency, bestModel, derive
Other plot:
plot.cnorm()
,
plotDensity()
,
plotNormCurves()
,
plotNorm()
,
plotPercentileSeries()
,
plotPercentiles()
,
plotRaw()
,
plotSubset()
# Load example data set, compute model and plot results result <- cnorm(raw = elfe$raw, group = elfe$group) plotDerivative(result, minAge=2, maxAge=5, step=.2, minNorm=25, maxNorm=75, stepNorm=1)