DHfun  R Documentation 
DHfun computes the first and second derivatives of the negative log likelihoods for a set of examinees. Items can be either binary or multioption. The analysis is within the closed interval [0,100].
DHfun(theta, WfdList, Umat)
theta 
Initial values for score indices in [0,n]/[0,100]. Vector of size N. 
WfdList 
A numbered list object produced by a TestGardener analysis of a test. Its length
is equal to the number of items in the test or questions in the scale.
Each member of

Umat 
An 
A named list for results DH
and D2H
:
First derivatives of the negative log likelihood values, vector of size N
Second derivatives of the negative log likelihood values, vector of size N
Juan Li and James Ramsay
Ramsay, J. O., Li J. and Wiberg, M. (2020) Full information optimal scoring. Journal of Educational and Behavioral Statistics, 45, 297315.
Ramsay, J. O., Li J. and Wiberg, M. (2020) Better rating scale scores with informationbased psychometrics. Psych, 2, 347360.
http://testgardener.azurewebsites.net
make.dataList,
Hfun,
Hfuns.plot
# Example 1: # Compute the first and second derivative values of the objective function for # locating each examinee for the 24item short form of the SweSAT quantitative # test on thepercentile score index continuum. WfdList < Quantshort_parList$WfdList theta < Quantshort_parList$theta U < Quantshort_dataList$U DHfunResult < DHfun(theta, WfdList, U) DHval < DHfunResult$DH D2Hval < DHfunResult$D2H print(paste("Mean of objective gradient =",round(mean(DHval),4))) print(paste("Standard deviation of objective gradient =",round(sqrt(var(DHval)),4))) print(paste("Mean of objective Hessian =",round(mean(D2Hval),4))) print(paste("Standard deviation of objective Hessian =",round(sqrt(var(D2Hval)),6))) # Example 2: # Compute the first and second derivative values of the objective function for # locating each examinee for the 13item Symptom Distress scale # on the percentile score index continuum. # Proceed as above changing "Quant" for "SDS".
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