predictRaw {cNORM} | R Documentation

## Predict single raw value

### Description

Most elementary function to predict raw score based on Location (L, T score), Age (grouping variable) and the coefficients from a regression model. WARNING! This function, and all functions depending on it, only works with regression functions including L, A and interactions. Manually adding predictors to bestModel via the predictors parameter is currently incompatible. In that case, and if you are primarily interested on fitting a complete data set, rather user the predict function of the stats:lm package on the ideal model solution. You than have to provide a prepared data frame with the according input variables.

### Usage

    predictRaw(
      norm,
      age,
      coefficients,
      minRaw = -Inf,
      maxRaw = Inf,
      covariate = NULL
    )

### Arguments

`norm` | The norm score, e. g. a specific T score or a vector of scores
`age` | The age value or a vector of scores
`coefficients` | The coefficients from the regression model or a cnorm model
`minRaw` | Minimum score for the results; can be used for clipping unrealistic outcomes, usually set to the lower bound of the range of values of the test (default: 0)
`maxRaw` | Maximum score for the results; can be used for clipping unrealistic outcomes usually set to the upper bound of the range of values of the test
`covariate` | In case, a covariate has been used, please specify the degree of the covariate / the specific value here.

### Value

the predicted raw score or a data.frame of scores in case, lists of norm scores or age is used

### See Also

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

### Examples

    # Prediction of single scores
    normData <- prepareData(elfe)
    m <- bestModel(data = normData)
    predictRaw(35, 3.5, m$coefficients)

    # using a cnorm object
    result <- cnorm(raw = elfe$raw, group = elfe$group)
    predictRaw(35, 3.5, result)

    # Fitting complete data sets
    fitted.values <- predict(m)

    # break up contribution of each predictor variable
    fitted.partial <- predict(m, type = "terms")

[Package cNORM version 2.0.3 Index]