normTable {cNORM} | R Documentation

## Create a norm table based on model for specific age

### Description

This function generates a norm table for a specific age based on the regression model by assigning raw scores to norm scores. Please specify the range of norm scores, you want to cover. A T value of 25 corresponds to a percentile of .6. As a consequence, specifying a rang of T = 25 to T = 75 would cover 98.4 the population. Please be careful when extrapolating vertically (at the lower and upper end of the age specific distribution). Depending on the size of your standardization sample, extreme values with T < 20 or T > 80 might lead to inconsistent results. In case a confidence coefficient (CI) and the reliability is specified, confidence intervals are computed as well, including a correction for regression to the mean.

### Usage

    normTable(
      A,
      model,
      minNorm = NULL,
      maxNorm = NULL,
      minRaw = NULL,
      maxRaw = NULL,
      step = NULL,
      covariate = NULL,
      monotonuous = TRUE,
      CI = 0.9,
      reliability = NULL
    )

### Arguments

`A` | the age as single value or a vector of age values
`model` | The regression model or a cnorm object
`minNorm` | The lower bound of the norm score range
`maxNorm` | The upper bound of the norm score range
`minRaw` | clipping parameter for the lower bound of raw scores
`maxRaw` | clipping parameter for the upper bound of raw scores
`step` | Stepping parameter with lower values indicating higher precision
`covariate` | In case, a covariate has been used, please specify the degree of the covariate / the specific value here.
`monotonuous` | corrects for decreasing norm scores in case of model inconsistencies (default)
`CI` | confidence coefficient, ranging from 0 to 1, default .9
`reliability` | coefficient, ranging between 0 to 1

### Value

either data.frame with norm scores, predicted raw scores and percentiles in case of simple A value or a list #' of norm tables if vector of A values was provided

### See Also

rawTable

Other predict: `derivationTable()`, `getNormCurve()`, `predictNorm()`, `predictRaw()`, `rawTable()`

### Examples

    # Generate cnorm object from example data
    cnorm.elfe <- cnorm(raw = elfe$raw, group = elfe$group)

    # create single norm table
    norms <- normTable(3.5, cnorm.elfe, minNorm = 25, maxNorm = 75, step = 0.5)

    # create list of norm tables
    norms <- normTable(c(2.5, 3.5, 4.5), cnorm.elfe,
      minNorm = 25, maxNorm = 75,
      step = 1, minRaw = 0, maxRaw = 26
    )

[Package cNORM version 2.0.3 Index]