cNORM (version 3.0.3)

predictRaw: 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
)

Value

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

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.

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")