cNORM (version 3.3.1)

plotNormCurves: Plot norm curves

Description

This function plots the norm curves based on the regression model. It supports both Taylor polynomial models and beta-binomial models.

Usage

plotNormCurves(
  model,
  normList = NULL,
  minAge = NULL,
  maxAge = NULL,
  step = 0.1,
  minRaw = NULL,
  maxRaw = NULL
)

Value

A ggplot object representing the norm curves.

Arguments

model

The model from the bestModel function, a cnorm object, or a cnormBetaBinomial / cnormBetaBinomial2 object.

normList

Vector with norm scores to display. If NULL, default values are used.

minAge

Age to start with checking. If NULL, it's automatically determined from the model.

maxAge

Upper end of the age check. If NULL, it's automatically determined from the model.

step

Stepping parameter for the age check, usually 1 or 0.1; lower scores indicate higher precision.

minRaw

Lower end of the raw score range, used for clipping implausible results. If NULL, it's automatically determined from the model.

maxRaw

Upper end of the raw score range, used for clipping implausible results. If NULL, it's automatically determined from the model.

Details

Please check the function for inconsistent curves: The different curves should not intersect. Violations of this assumption are a strong indication of violations of model assumptions in modeling the relationship between raw and norm scores.

Common reasons for inconsistencies include: 1. Vertical extrapolation: Choosing extreme norm scores (e.g., scores <= -3 or >= 3). 2. Horizontal extrapolation: Using the model scores outside the original dataset. 3. The data cannot be modeled with the current approach, or you need another power parameter (k) or R2 for the model.

See Also

checkConsistency, plotDerivative, plotPercentiles

Other plot: plot.cnorm(), plot.cnormBetaBinomial(), plot.cnormBetaBinomial2(), plotDensity(), plotDerivative(), plotNorm(), plotPercentileSeries(), plotPercentiles(), plotRaw(), plotSubset()

Examples

if (FALSE) {
# For Taylor continuous norming model
m <- cnorm(raw = ppvt$raw, group = ppvt$group)
plotNormCurves(m, minAge=2, maxAge=5)

# For beta-binomial model
bb_model <- cnorm.betabinomial(age = ppvt$age, score = ppvt$raw, n = 228)
plotNormCurves(bb_model)
}