WriteNormTable {NormData}

Write a normative table from R to a .txt/.csv/.xlsx file

Description

The function Stage.2.NormTable() allows for deriving a normative table that shows percentile ranks \hat{\pi}_0 that correspond to a wide range of raw test scores Y_0 (stratified by the relevant independent variables). The raw R output format that is provided by the Stage.2.NormTable() function is not always convenient, especially when a large number of test scores are tabulated and the table is spread out over several lines. The function WriteNormTable() can be used to export the normative table to a .txt, .csv or .xlsx file. Such a file can then be opened in a spreadsheet (such as Google Sheets or LibreOffice), where the normative table can be put in a more user-friendly format.

Usage

WriteNormTable(NormTable, Folder, NameFile="NormTable.xlsx", 
verbose=TRUE)

Arguments

NormTable

An object of class Stage.2.NormTable that contains the normative table that has to be exported.

Folder

The folder where the file with the normative table should be saved.

NameFile

The name of the file to which the normative table should be written. Only the extensions .txt, .csv or .xlsx can be used. If unspecified, the argument NameFile="NormTable.xlsx" is used by default. The .txt and .csv files use a space as the delimiter.

verbose

A logical value indicating whether verbose output should be generated.

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

Stage.2.NormTable

Examples

# Replicate the normative table that was obtained in 
# Case study 1 of Chapter 3 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)

# Make a normative table for raw Science Exam scores = 10, 
# 11, ... 85, stratified by Gender
NormTable.GCSE <- Stage.2.NormTable(Stage.1.Model=Model.1.GCSE,
    Test.Scores=c(10:85), 
    Grid.Norm.Table=data.frame(Gender=c("F", "M")))
summary(NormTable.GCSE)

# Write the normative table to the user's computer
WriteNormTable(NormTable=NormTable.GCSE, 
     NameFile="NormTable.GCSE.xlsx",
     Folder=tempdir()) # Replace tempdir() by the desired folder  


# Replicate the normative table that was obtained in 
# Case study 1 of Chapter 7 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) 

# Make a normative table for LDST scores = 10, 12, ... 56,
# stratified by Age and LE
NormTable.LDST <- Stage.2.NormTable(
  Stage.1.Model=Substitution.Model.9,
  Test.Scores=seq(from=10, to=56, by=2),
  Grid.Norm.Table=expand.grid(Age.C=seq(from=-30, to=30, by=1),
     LE=c("Low", "Average", "High")))

# Write the normative table to the user's computer
WriteNormTable(NormTable=NormTable.LDST, 
  NameFile="NormTable.LDST.xlsx",
  Folder=tempdir()) # Replace tempdir() by the desired folder