Optimal Analysis of Test and Rating Scale Data

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Documentation for package ‘TestGardener’ version 3.2.4

Help Pages

Analyze Analyze test or rating scale data defined in 'dataList'.
ArcLength.plot Plot the score index 'theta' as a function of arc length.
dataSimulation Simulation Based Estimates of Root-mean-squared-err of Theta Estimates
density_plot Plot the probability density function for a set of test scores
DHfun Compute the first and second derivatives of the negative log likelihoods
entropies Entropy measures of inter-item dependency
Entropy.plot Plot item entropy curves for selected items or questions.
eval.surp Values of a Functional Data Object Defining Surprisal Curves.
Hcurve Construct grid of 101 values of the fitting function
Hfun Compute the negative log likelihoods associated with a vector of score index values.
Hfuns.plot Plot a selection of fit criterion H functions and their first two derivatives.
ICC Objects required for representing a single test or scale item
ICC.plot Plot probability and surprisal curves for a selection of test or scale items.
make.dataList Make a list object containing information required for analysis of choice data.
mu.plot Plot expected test score as a function of score index
plotCore Plot probability and surprisal curves for a single test or scale item. The core portion within 'plotindex'
plotICC The most essential part for plotting probability/sueprisal curves of a single item.
Power.plot Plot item power curves for selected items or questions.
Quant_13B_problem_dataList List of objects essential for an analysis of the abbreviated SweSAT Quantitative multiple choice test.
Quant_13B_problem_infoList Arclength or information arameter list for 24 items from the quantitative SweSAT subtest.
Quant_13B_problem_key Option information for the short form of the SweSAT Quantitative test.
Quant_13B_problem_parList Parameter list for 24 items from the quantitative SweSAT subtest.
Quant_13B_problem_U Test data for 24 math calculation questions from the SweSAT data.
scoreDensity Compute and plot a score density histogram and and curve.
scorePerformance Calculate mean squared error and bias for a set of score index values from simulated data.
SDS The Symptom Distress Scale data.
SDS_dataList List of objects essential for an analysis of the Symptom Distress Scale.
SDS_infoList Arclength or information parameter list for 13 items in the Symptom Distress Scale.
SDS_key Key for Symptom Distress Scale.
SDS_parList Parameter list for the Symptom Distress Scale.
SDS_U Test data for Symptom Distress Scale.
Sensitivity.plot Plots all the sensitivity curves for selected items or questions.
SimulateData Simulate Choice Data from a Previous Analysis
smooth.ICC Smooth binned probability and surprisal values to make an 'ICC' object.
smooth.surp Fit data with surprisal smoothing.
TestGardener Analyses of Test and Rating Scale using Information Theor7
TestInfo_svd Image of the Test Tnformation Curve in 2 or 3 Dimensions
testscore Compute the expected test score by substituting probability of choices for indicator variable 0-1 values. Binary items assumed coded as two choice items.
TG_density.fd Compute a Probability Density Function
theta.distn Compute score density
theta2arclen Compute results using arc length or information as the abscissa.
thetafun Compute optimal scores
thetasearch Ensure that estimated score index is global
Usimulate Simulate a test or scale data matrix.
usimulate Simulate a test or scale data matrix.
Wbinsmth Estimate the option probability and surprisal curves.
Wbinsmth_nom List vector containing numbers of options and boundaries.
Wpca.plot Plot the test information or scale curve in either two or three dimensions.