plot Bootstrap.Stage.2.NormScore {NormData}

Plot the bootstrap distribution and the percentile bootstrap CI

Description

This function plots the bootstrap distribution and the percentile bootstrap CI for a test score based on a Bootstrap.Stage.2.NormScore object. A non-parametric bootstrap is used to compute a confidence interval (CI) around the estimated percentile rank (for details, see Chapter 8 in Van der Elst, 2023).

Usage

## S3 method for class 'Bootstrap.Stage.2.NormScore'
plot(x, 
cex.axis=1, cex.main=1, cex.lab=1, ...)

Arguments

x

A fitted object of class Bootstrap.Stage.2.NormScore.

cex.axis

The magnification to be used for axis annotation.

cex.main

The magnification to be used for the main label.

cex.lab

The magnification to be used for X and Y labels.

...

Other arguments to be passed to the plot() function.

Value

No return value, called for side effects.

Author(s)

Wim Van der Elst

References

Van der Elst, W. (2024). Regression-based normative data for psychological assessment: A hands-on approach using R. Springer Nature.

See Also

Bootstrap.Stage.2.NormScore

Examples

  # Time-intensive part
# Replicate the bootstrap results that were obtained in 
# Case study 1 of Chapter 8 in Van der Elst (2023)
# -----------------------------------------------------
library(NormData) # load the NormData package
data(GCSE)        # load the GCSE dataset

# Fit the Stage 1 model
Model.1.GCSE <- Stage.1(Dataset=GCSE, 
  Model=Science.Exam~Gender)

# Stage 2: Convert a science exam score = 30 obtained by a 
# female into a percentile rank (point estimate)
Normed_Score <- Stage.2.NormScore(Stage.1.Model=Model.1.GCSE,
  Score=list(Science.Exam=30, Gender="F"), Rounded = FALSE)
summary(Normed_Score)

# Derive the 99pc CI around the point estimate 
# using a bootstrap procedure
Bootstrap_Normed_Score <- Bootstrap.Stage.2.NormScore(
  Stage.2.NormScore=Normed_Score)

summary(Bootstrap_Normed_Score)

plot(Bootstrap_Normed_Score)


# Replicate the bootstrap results that were obtained in 
# Case study 2 of Chapter 8 in Van der Elst (2023)
# ------------------------------------------------
library(NormData)   # load the NormData package
data(Substitution)  # load the Substitution dataset

# Make the new variable Age.C (= Age centered) that is 
# needed to fit the final Stage 1 model, 
# and add it to the Substitution dataset
Substitution$Age.C <- Substitution$Age - 50

# Fit the final Stage 1 model
Substitution.Model.9 <- Stage.1(Dataset=Substitution, 
  Alpha=0.005, Model=LDST~Age.C+LE, Order.Poly.Var=1) 
summary(Substitution.Model.9)

# Convert an LDST score = 40 obtained by a 
# 20-year-old test participant with LE=Low 
# into a percentile rank (point estimate)
Normed_Score <- Stage.2.NormScore(
   Stage.1.Model=Substitution.Model.9, 
   Score=list(LDST=40, Age.C=20-50, LE = "Low"), 
   Rounded = FALSE)

# Derive the 99pc CI around the point estimate 
# using a bootstrap
Bootstrap_Normed_Score <- Bootstrap.Stage.2.NormScore(
   Stage.2.NormScore = Normed_Score)
summary(Bootstrap_Normed_Score)
plot(Bootstrap_Normed_Score)