plotNormCurves {cNORM} | R Documentation

## Plot norm curves

### Description

The function plots the norm curves based on the regression model. Please check the function for inconsistent curves: The different curves should not intersect. Violations of this assumption are a strong indication for violations of model assumptions in modeling the relationship between raw and norm scores. There are several reasons, why this might occur:

1. Vertical extrapolation: Choosing extreme norm scores, e. g. scores -3 <= x and x >= 3 In order to model these extreme scores, a large sample dataset is necessary.

2. Horizontal extrapolation: Taylor polynomials converge in a certain radius. Using the model scores outside the original dataset may lead to inconsistent results.

3. The data cannot be modeled with Taylor polynomials, or you need another power parameter (k) or R2 for the model.

In general, extrapolation (point 1 and 2) can carefully be done to a certain degree outside the original sample, but it should in general be handled with caution. checkConsistency and derivationPlot can be used to further inspect the model.

### Usage

    plotNormCurves(
      model,
      normList = c(30, 40, 50, 60, 70),
      minAge = NULL,
      maxAge = NULL,
      step = 0.1,
      minRaw = NULL,
      maxRaw = NULL,
      covariate = NULL
    )

### Arguments

`model` | The model from the bestModel function or a cnorm object
`normList` | Vector with norm scores to display
`minAge` | Age to start with checking
`maxAge` | Upper end of the age check
`step` | Stepping parameter for the age check, usually 1 or 0.1; lower scores indicate higher precision / closer checks
`minRaw` | Lower end of the raw score range, used for clipping implausible results (default = 0)
`maxRaw` | Upper end of the raw score range, used for clipping implausible results
`covariate` | In case, a covariate has been used, please specify the degree of the covariate / the specific value here.

### See Also

checkConsistency, derivationPlot, plotPercentiles

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

### Examples

    # Load example data set, compute model and plot results
    normData <- prepareData(elfe)
    m <- bestModel(data = normData)
    plotNormCurves(m, minAge=2, maxAge=5)

[Package cNORM version 2.0.3 Index]