predict.cnormBetaBinomial: Predict Norm Scores from Raw Scores

In cNORM: Continuous Norming

Predict Norm Scores from Raw Scores

Description

This function calculates norm scores based on raw scores, age, and a fitted cnormBetaBinomial model.

Usage

## S3 method for class 'cnormBetaBinomial'
predict(object, ...)

Arguments

object	A fitted model object of class 'cnormBetaBinomial' or 'cnormBetaBinomial2'.
...	Additional arguments passed to the prediction method: age A numeric vector of ages, same length as score. score A numeric vector of raw scores. range The range of the norm scores in standard deviations. Default is 3. Thus, scores in the range of +/- 3 standard deviations are considered.

Details

The function first predicts the alpha and beta parameters of the beta-binomial distribution for each unique age using the provided model. It then calculates the mid-p cumulative probability for each raw score given these parameters and converts these probabilities to the norm scale specified in the model. The distribution is computed only once per unique age value, which considerably speeds up predictions for grouped data.

Value

A numeric vector of norm scores (or percentiles, if no norm scale was specified in the model).

See Also

Other predict: derivationTable(), getNormCurve(), normTable(), predict.cnormBetaBinomial2(), predict.cnormShash(), predictNorm(), predictRaw(), rawTable()

Examples

## Not run: 
# Assuming you have a fitted model named 'bb_model':
model <- cnorm.betabinomial(ppvt$age, ppvt$raw)
raw <- c(100, 121, 97, 180)
ages <- c(7, 8, 9, 10)
norm_scores <- predict(model, ages, raw)

## End(Not run)

cNORM documentation built on July 13, 2026, 5:08 p.m.